# From Data to Insights: A Comprehensive Survey on Advanced Applications in
Thyroid Cancer Research

Xinyu Zhang, Vincent CS Lee, Feng Liu

###### Abstract

Thyroid cancer, the most prevalent endocrine cancer, has gained significant
global attention due to its impact on public health. Extensive research
efforts have been dedicated to leveraging artificial intelligence (AI) methods
for the early detection of this disease, aiming to reduce its morbidity rates.
However, a comprehensive understanding of the structured organization of
research applications in this particular field remains elusive. To address
this knowledge gap, we conducted a systematic review and developed a
comprehensive taxonomy of machine learning-based applications in thyroid
cancer pathogenesis, diagnosis, and prognosis. Our primary objective was to
facilitate the research community’s ability to stay abreast of technological
advancements and potentially lead the emerging trends in this field. This
survey presents a coherent literature review framework for interpreting the
advanced techniques used in thyroid cancer research. A total of 758 related
studies were identified and scrutinized. To the best of our knowledge, this is
the first review that provides an in-depth analysis of the various aspects of
AI applications employed in the context of thyroid cancer. Furthermore, we
highlight key challenges encountered in this domain and propose future
research opportunities for those interested in studying the latest trends or
exploring less-investigated aspects of thyroid cancer research. By presenting
this comprehensive review and taxonomy, we contribute to the existing
knowledge in the field, while providing valuable insights for researchers,
clinicians, and stakeholders in advancing the understanding and management of
this disease.

##  1 Introduction

The thyroid is the largest endocrine gland in an adult, shaped like a
butterfly and locates at the lower neck. It controls the metabolism of cells
by producing hormones and regulating the balance of calcium in the human body.
With an adequately functioned thyroid gland, one can maintain the right amount
of hormones to keep metabolism activities at a favorable rate [1]. However,
this crucial gland is gradually grabbing attention worldwide since it gives
rise to the most common endocrine tumors [2].

Thyroid tumors are prevalent and regarded as the most frequently seen nodular
lesions among adults. Specifically, up to $50\%$ of adults have thyroid
nodules [3]. $5\%$ to $15\%$ of these nodules turned out to be malignant,
forming into four types of thyroid cancer: papillary thyroid carcinoma (PTC),
follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC), and
anaplastic thyroid carcinoma (ATC) [4].

Since the $20th$ century, thyroid cancer has been in a progressive pattern,
rising at the fastest rate among all the malignancies [5]. According to the
latest Cancer Fact & Figures statistics, there will be an estimation of
$43,800$ new cases diagnosed with thyroid cancer in the United States in
$2022$ [6]. Thyroid cancer instances are rapidly increasing, with the highest
rates found in the Federated States of Micronesia, French Polynesia, North
America, and East Asia [7]. In particular, South Korea, Cyprus, and Canada had
the highest incidence-to-mortality rates [7]. In China, thyroid cancer was
even ranked as the $4th$ commonly diagnosed disease for females [8].

With the apparent implications of increased morbidity and mortality rates
brought by thyroid cancer, complications of understanding the cause of the
disease, enhancing the diagnostic performance, and offering patients targeted
treatments, all mitigating recurrence rates; thus, related existing
methodologies should be systematically analyzed. In this scenario, studies
applying statistical and machine learning techniques on investigating the
pathogenesis of thyroid cancer, increasing diagnostic accuracy and efficiency,
and building patient-centric treatment recommendation systems are expected to
be analyzed. Notwithstanding the potential challenges brought by the long-
established machine learning techniques, the advantages of this technology
often take place. Specifically, machine learning-driven techniques adopt
various medical tools by taking into account the roles of physicians and
computers simultaneously. With these techniques, doctors can gain a “second
opinion” relying on decisions made by computers; also, clinicians get to focus
more on patient care, enabling more targeted and customized treatment plans.
Therefore, this survey provides a comprehensive literature analysis regarding
applying machine learning techniques for thyroid cancer pathogenesis,
diagnosis, and prognosis.

Following contributions were proposed in this survey paper:

  * •

We proposed a comprehensive literature review framework in this study. The
proposed framework can be adaptable to multi-disciplines for conducting
literature analysis.

  * •

This study offers a structured organization of existing studies regarding
machine learning application on thyroid cancer, including its pathogenesis,
diagnosis, and prognosis.

  * •

We provide informative concepts about traditional machine learning techniques
and interpret current state-of-the-art deep learning models; this can be
considerably helpful for researchers to understand the theory behind deep
learning approaches, contributing to more advanced computer-aided diagnosis
(CAD) designs for thyroid disease.

  * •

Current challenges faced by the machine learning approaches will be explained
in this study, and corresponding potential research directions will also be
outlined.

The remainder of the survey is structured as follows: Section $2$ covers the
clinical background of thyroid cancer, highlighting the challenges faced by
clinicians. Section $3$ presents the proposed systematic literature review
framework used as the methodology approach. Section $4$ depicts the machine
learning techniques for thyroid cancer pathogenesis, diagnosis, and prognosis.
Section $5$ discusses the challenges faced by existing studies in more detail
and offers potential suggestions. Finally, Section $6$ concludes this survey
paper.

##  2 Background and Motivation

Early detection of thyroid cancer leads to mitigated mortality and morbidity
rates. There exist long-established protocols for the diagnosis and treatment
of thyroid cancer in the clinical setting. Being aware of the pros and cons of
those standardized procedures, applications of machine learning-based
techniques can be initiated to reap the pros’ benefits while overcoming the
cons. We aim to comprehend what, how, and why these machine learning
techniques perform in the clinical field. This section first illustrates the
existing clinical protocols for the pathogenesis, diagnosis, and prognosis of
thyroid cancer, interprets current challenges faced by clinicians, and then
emphasizes the motivations of our study.

###  2.1 Thyroid Cancer Pathogenesis

Although gender is a global consensus factor for thyroid cancer pathogenesis
since females have three more possibilities of being diagnosed with thyroid
cancer than males [9], the risk factors correlated with the disease are still
under-researched, much less is to say the interpretability and reliability of
the controversial factors.

Clinicians usually struggle to determine the cause of thyroid cancer, as the
pathogenesis behind the disease is affected by multiple diversified attributes
(e.g., dietary habits, medical history, gene heredity, and periodical health
conditions). The inherent process is that clinicians always select one factor
and evaluate its association with thyroid cancer development. Following this
intrinsic step, several potential factors and comorbidity were identified
correlated to thyroid cancer, such as vitamin D deficiency [10], radiation
exposure [11], diabetes [12], obesity [13], iodine intake [14, 15], smoking
status [16], and family history [17], to name a few. More controversial
factors can be found in Figure 1. In this regard, the evaluations of either
one of those attributes take long-established retrospective investigations,
let alone the interwoven among them; ignoring the correlations among
diversified risk factors can bring substantial errors in understanding the
cause of thyroid cancer.

Figure 1: Thyroid cancer controversial risk factors

To gather the significant points, confirming the associations between a single
factor and thyroid cancer development at a time is challenging and
inefficient, making thyroid cancer risk factors identification tasks scarcely
achievable. In this case, the associations among multiple factors cannot be
evaluated, amplifying the misinterpretations in understanding thyroid cancer
cause. These shortcomings create limitations for revealing the pathogenesis of
thyroid cancer; therefore, we urgently need the means to incorporate an
automated algorithm to efficiently mine essential knowledge from high-
dimensional medical records to reveal the pathogenesis of thyroid cancer.

###  2.2 Thyroid Cancer Diagnosis

Following the clinical guidance, the thyroid function examination is always in
diagnostic priority, which measures hormones produced by the gland. It
includes measuring thyroid-stimulating hormone (TSH), triiodothyronine (T$3$),
thyroxine (T$4$), free triiodothyronine (FT$3$), and free thyroxine (FT$4$)
[18]. The patient will be suggested an ultrasonography test when identifying
abnormal thyroid function results. Ultrasonography provides an intermediate
risk following the _Thyroid Imaging Reporting and Data System_ (TIRADS) score
based on the features appearing on the ultrasound images. TIRADS measures an
image based on nodule features like irregular margins, hypo-echogenicity,
taller-than-wide shape, and microcalcifications [19]. TIRADS scores rank from
$1$ to $5$ where $1$ was defined as “normal thyroid”, $2$ as “benign
features”, $3$ as “absence of suspicious features”, and $4a$ as “one
suspicious feature”, $4b$ as “two suspicious features”, $4c$ as “three to four
suspicious features”, and $5$ as “five or more suspicious features”,
respectively.

Nevertheless, TIRADS scores cannot provide precise decisions for thyroid
cancer diagnosis. Based on the analysis conducted by [20] in which $951$
patients’ TIRADS scores were compared to the ground-truth histopathological
results. The authors found that thyroid cancer was appearing at a $0.9\%$ with
TIRADS ranking $2$, $2.9$% cancer rate was found in TIRADS $3$, TIRADS $4a$
had a cancer rate of $12.3$%, $34.4$% cancer rate was found in TIRADS $4b$,
$66.6$% in TIRADS $4c$, and $86$% cancer rate was in TIRADS $5$. Driven from
this finding, solely relying on ultrasound images to make a diagnosis through
TIRADS scores is considered insufficient. In this scenario, patients having
TIRADS scored from $2$ to $5$ will need to perform further fine-needle
aspirations cytology (FNAC) to determine whether the nodule has cancerous
cells [21].

FNAC is regarded as the golden standard in assessing malignancy of thyroid
nodules [22]. It gets a biopsy from nodule cells using needles following the
ultrasonography guidance, then has a biopsy assessed by pathologists to make
diagnostic decisions regarding nodule malignancy risk stratification. Figure 2
demonstrates the FNAC apparatus and Figure 3 displays the implementation
procedures. However, FNAC examinations are easily affected by the
professionalism and expertise of pathologists, resulting in $30$% of FNAC
results being non-diagnostic or indeterminate [23]. Under this circumstance,
patients with indeterminate FNAC results usually need to undergo another FNAC
or even an excisional biopsy for further diagnostic decision-making. Sometimes
patients might undergo unnecessary surgeries, then determining the nodule
shows no evidence of malignancy.

Figure 2: FNA Apparatus (samples from [4]) Figure 3: FNA Procedures (samples
from [24])

These shortcomings create limitations for thyroid cancer diagnosis in the
clinical setting. Therefore, clinicians seek to improve diagnostic accuracy
and efficiency by integrating human knowledge with computer-driven techniques.
With the emergence of machine learning, particularly the deep learning
concept, the diagnostic procedures form into advanced ways of making
decisions, naming “computer-aided diagnosis (CAD)” techniques.

###  2.3 Thyroid Cancer Prognosis

Thyroid cancer has a rigorous treatment protocol. Specifically, patients
diagnosed with thyroid cancer, regardless of the subtype, require thyroid
surgery (i.e., partial thyroidectomy, total thyroidectomy, or total
thyroidectomy with lymph node dissection). Further radiation therapy is
expected to mitigate recurrence risks by performing Iodine-$131$ treatment.
Patients with thyroidectomy are expected to take levothyroxine one hour before
breakfast for thyroid hormones replacements [25]. Levothyroxine is a
manufactured form of the thyroid hormone thyroxine, and the common thyroxine
medications are Eutroxsig, Oroxine, and Aspen Pharma [25].

Noteworthy, over-treatments are substantially occurred with thyroid disease
[26]. The post-therapy hypothyroid disease frequently occurs after surgery, or
Iodine-$131$ treatments [27]. Radio-iodine therapy would somehow bring adverse
effects for patients [28]. [29] also confirmed that Iodine-$131$ treatment
would cause salivary gland dysfunction if the dosage of iodine were not
carefully determined. Thus, over-treatment must be avoided by offering
patients customized treatment plans based on their age, weight, and cancer
stage, to name a few. However, there is a scarcity of studies that propose
customized treatment recommendation systems for patients with thyroid cancer.

Besides providing personalized treatment plans, predicting the recurrence and
survival rates can also potentially enhance the prognosis for thyroid cancer
patients. An estimation of $2,230$ people will die from thyroid cancer in
$2022$ [6] with a mortality rate of $5\%$. Among the four types of thyroid
cancer, PTC is the most commonly diagnosed kind with the highest survival
rate. Specifically, the five-year survival rate of PTC is more than $98\%$
[5]; patients with PTC are usually expected to have a normal lifespan after
surgery and radiotherapy treatments. On the contrary, rarer kinds like
anaplastic or medullary have much lower survival rates, which is less than
$10\%$ [30].

With respect to the recurrence of thyroid cancer, [31] indicated that $20\%$
of patients would experience the recurrence, in which they might need re-
operations or repetitive radiotherapy treatments. Unlike the pathogenesis of
thyroid cancer, the risk factors correlated to thyroid cancer recurrences are
not too controversial, including gender, elder age, primary disease extent,
metastases to other organs, tumor size, extra-thyroidal invasion, location of
nodules, and cervical lymph nodules are the leading factors for thyroid cancer
recurrence [32, 33, 34, 35, 36, 37]. [38] proposed that PTC patients who had a
total thyroidectomy and neck dissections tend to have a $3.8\%$ recurrence
rate. In addition, different surgical treatments also differentiate recurrence
rates of thyroid cancer. For instance, based on the comparative study by [39],
the recurrence rate for patients with thyroidectomy and neck dissection is
$2.02\%$, whereas the recurrence rate for patients with thyroidectomy only is
$3.92\%$. It is evident that thyroidectomy and iodine treatment decreases the
recurrence rate of thyroid cancer [31, 34, 40].

Collectively, thyroid cancer prognosis is closely related to the well-
established treatment protocols. Existing studies harnessed the power of data
mining to extract valuable knowledge from thyroid cancer patients in helping
provide personalized treatment plans and make predictions in recurrence to
achieve a patient-oriented treatment recommendation system so that patients
and experts can access self-driven decision-making to optimize the prognosis
of thyroid cancer.

##  3 Literature Review Methodology

In order to conduct a comprehensive systematic literature analysis, this paper
proposes a detailed step-by-step literature review framework presented in
Figure 4.

Figure 4: Systematic literature review framework

The overall literature review framework contains three stages: research
definition, research methodology, and research analysis. “Research definition”
includes identifying the research field, defining research objectives, and
outlining the research scope. “Research methodology” stage oughts to identify
all the related literature studies based on the pre-defined searching
protocols. “Research analysis” stage then analyses the identified studies,
demonstrating the critical findings and explaining the existing literature
gaps while interpreting future research opportunities. In this section, the
three stages will be explained in detail.

###  3.1 Research Definition

The first stage of the literature review framework is the research definition.
The research focus has been identified in this phase; meanwhile, the research
motivations and objectives will need to be highlighted, and the research scope
will be defined.

In this research, we focus on understanding machine learning-based
applications for thyroid cancer, not only for the diagnosis but also to
embrace the studies conducted around thyroid cancer risk factors, treatments,
and prognosis. Existing literature review studies simply investigate machine
learning approaches for thyroid disease detection, ignoring the advanced
algorithms results in the obsolete knowledge discovery, much less is to
summarise the findings from the pathogenesis and prognosis facets. While the
early detection of thyroid disease can certainly reduce morbidity and
mortality rates, the pathogenesis and prognosis are as necessary as the
diagnosis to achieve a well-established prognosis. Hence, we ought to include
studies from all three perspectives so that the researchers can gain in-depth
knowledge to develop more sophisticated models.

We seek to identify papers that incorporate machine learning algorithms to
help reveal thyroid cancer pathogenesis, achieve advanced CAD design to
release patients from painful clinical diagnostic procedures, and design a
patient-centric treatment recommendation system to achieve a promising
prognosis. Therefore, we aim to analyse literature that aims to comprehend,
diagnose, and treat thyroid cancer through data mining, machine learning, or
deep learning techniques.

Lastly, the scope of this survey paper is to include as much related
literature as possible for analysis. Machine learning techniques have been
applied quite often in the medical field, yet the application to thyroid
cancer is relatively limited. In order to have a comprehensive analysis, we
intend to include as many high-level ranking literature studies as possible.
Moreover, the specific literature identification process will be explained
next.

###  3.2 Research Methodology

After defining the research scope, a set of literature identification
processes was conducted. We have included a list of high-level ranking
conference and journal databases during the literature searching phase, such
as Elsevier, Springer, IEEE Xplore, and ACM Digital Library, to name a few. A
set of criteria were used as our search protocols, and Table 1 lists some
keywords applied.

Table 1: Literature identification searching phase Research Focus | Searching Keyword Phases
---|---
Thyroid cancer pathogenesis | Risk factors of thyroid cancer
Thyroid cancer pathogenesis with machine learning
Thyroid cancer pathogenesis with data mining
Thyroid cancer risk factors with association rule mining
Thyroid cancer diagnosis | Machine learning with thyroid cancer
Deep learning with thyroid cancer
CAD for thyroid cancer detection
Thyroid cancer prognosis | Thyroid cancer treatment recommendation system
Machine learning with thyroid cancer treatment
Prediction of thyroid cancer survival or recurrence

Since deep learning techniques are relatively advanced, we want to explore the
developments of these approaches over time; thus, during the searching phase,
no timeline restriction was followed. In this regard, the literature studies
were selected based on the flowchart (Figure 5) when applying the above-
identified keywords in the academic database.

Figure 5: Flowchart of the searching strategy and literature selection

Precisely, we have carefully read through the abstract of each paper.
Optionally we would read through the methodology section if the abstract were
not explicit enough regarding whether machine learning techniques were used.
Then, the papers that fit into our pre-defined scope will be stored in the
literature repository for further analysis, otherwise removed. Lastly, all the
identified papers were categorized into three groups: pathogenesis, diagnosis,
and prognosis. As a result, $70$ papers are categorized into the pathogenesis
group, $213$ papers are categorized into the diagnosis group, $24$ papers are
about the prognosis of thyroid cancer. Details of the paper distribution can
be viewed in Figure 6.

Figure 6: Number of literature distribution in the last $10$ years

Figure 6 exhibits that the number of papers on thyroid disease diagnosis is
way much higher than the pathogenesis and prognosis-related studies.
Furthermore, most related studies were published in $2019$, while the least
number of papers was published in $2014$. Since thyroid disease is rapidly
rising, more and more research studies have been conducted in the last $10$
years; thus, a gradual increase can be found in the figure.

###  3.3 Research Analysis

The research analysis is the last stage of the systematic literature review
approach. A total number of $136$ papers were utilised for the analysis phase
in this survey paper. Each of the three categories was analysed based on the
performance, feasibility, and generalizability of the proposed machine
learning approaches. To better interpret our findings, the analysis section
was categorised into three main perspectives aligning with our research scope.

For thyroid cancer pathogenesis-related studies, we have divided the
literature into qualitative and quantitative groups. Qualitative studies
investigated risk factors correlated with thyroid cancer, and quantitative
studies further confirmed some risk factors through data mining techniques.
For thyroid cancer diagnosis-related studies, machine learning and deep
learning applications were discussed independently. As for thyroid cancer
prognosis-related studies, the design of treatment recommendation systems and
the prediction of survival, death, and recurrence rates were explained. At the
end of each section, existing literature gaps and currently facing challenges
will be outlined. Collectively, a summary of the literature analysis will be
presented, existing challenges inferred from literature analysis will be
outlined, and corresponding future research directions will be provided for
researchers.

##  4 Machine Learning Applications in Thyroid Cancer

With the emergence of machine learning techniques in $1960s$ [41], their
applications have been implemented to enable modelling and analyzing large
scales of datasets. Various methods have been deployed in the medical domain
to address clinical dilemmas. A summary of major machine learning techniques
around thyroid cancer applications is provided in this section.

###  4.1 Applications in Thyroid Cancer Pathogenesis

Thyroid cancer pathogenesis has always been on hit in the clinical and
academic domains. Qualitative and quantitative analysis was usually conducted
for revealing such a mystery. Well established factors like radiation exposure
in childhood [42] and gene heredity [43] are in consensus; whereas factors
like diabetes [44], obesity [45], vitamin D deficiency [46], hormonal factors
[47], are still under debate.

With the identified studies, qualitative literature was based on survey
analysis, whereas quantitative studies were majorly built based on statistical
learning, including case-control, retrospective, or prospective cohort
studies. The machine learning application in the clinical domain is
considerably limited with respect to thyroid cancer pathogenesis
identification, leading to insufficient work produced. In this regard, this
section interprets the commonly utilised machine learning algorithms.

Among the identified machine learning-related studies, data mining,
specifically, association rule mining (ARM) techniques have been applied
relatively often. ARM is dedicated to revealing hidden patterns among complex,
high-dimension, and large volumes of interwoven attributes [48]; its
applications in the clinical domain demonstrate superior performance in
comorbidity detection [49, 50], disease symptom analysis [51], and behavioural
analysis for driving certain disease [52].

ARM techniques generate association rules denoted as $X\Rightarrow Y$, in
which $X$ is the antecedent item and $Y$ is the consequence, meaning when $X$
exists in an observed instance, then item $Y$ should co-exist. The high-
quality rules are assessed through the support and the confidence metrics. The
support value can be seen as the frequency of the itemset that appeared in the
database, calculated through Eq. 1, where $N$ is the total number of instances
in the database. The confidence is a conditional probability that determines
the occurrence of the itemset $X$ and $Y$ given the probability of antecedent
$X$ itself, calculated through Eq. 2. Besides the support and confidence
metrics, strongly-related ARM rules sometimes are also selected based on a
lift measurement. The lift value suggests the ratio of the probability of
having $X$ and $Y$ simultaneously compared to the probability of having $X$
and $Y$ independently, which is calculated through Eq. 3. Specifically, when
the lift value is high, it is more likely that the co-existence of $X$ and $Y$
is not just a random occurrence [53].

$Support=\frac{freq(X\Rightarrow Y)}{N}$ (1)

$Confidence=\frac{freq(X\Rightarrow Y)}{freq(X)}$ (2)

${}Lift=\frac{Support(X\Rightarrow Y)}{P(X)\times P(Y)}$ (3)

With those fundamental metrics, extensive studies mined valuable information
from heterogeneous database for knowledge discovery. Although clinical
research applying ARM is rich, the adoption in identifying thyroid disease
pathogenesis is considerably limited, with only two pieces of literature were
found amid. [54] once deployed the Apriori and the Predictive Apriori
algorithms to find risk factors correlated with thyroid disease, and further
input the strongly related factors into a back-propagation neural network for
binary classification between healthy and sick groups. A similar study was
conducted by [55], in which the Apriori algorithm was incorporated with the
bagging algorithm for thyroid disease detection.

####  4.1.1 Apriori

The Apriori algorithm was proposed by [56] in $1994$, aimed to discover
underlying association rules, also known as frequent itemsets, given a large
transaction database. The algorithm was built as tabular-based and required
the generation of candidate itemsets through iteration. In the first
iteration, the occurrence of each candidate item $k$ would be counted and
recorded, whereas the rest of the items below the minimum threshold would be
pruned. In this regard, the frequency of the item is known as the support
value; thus, the minimum threshold would be the minimum support value defined
prior to the implementation of the algorithm. In the second iteration, an
increment $(k+1)$ itemsets would again be assessed with the threshold. The
algorithm scans through the entire database in each iteration until it
finishes counting the occurrence of all possible combinations for the
candidate itemsets. To this extent, the Apriori algorithm was considered as
the most classic ARM technique as it is easy to be implemented and understood.

Nevertheless, the major shortage of the Apriori algorithm is with its
efficiency. The algorithm requires the generation of candidate itemsets and
scanning through the entire database in each iteration, this limits the
running time of the algorithm. In particular, medical datasets are vast in
volume and complex in dimension, making the workload of the Apriori algorithm
even more cumbersome. Although considered as accurate in determining frequent
itemsets, the Apriori algorithm is not computationally efficient enough when
the dataset gets more extensive or complicated.

####  4.1.2 Predictive Apriori

The generated association rules can be classified into common rules (i.e.,
rules with high support and confidence), reference rules (i.e., rules with low
support and confidence), and exception rules (i.e., rules with low support but
high confidence) [57]. Common rules are the frequent itemsets which we aim to
identify through ARM techniques. Reference rules are usually discarded as they
merely produce valuable knowledge. Exception rules, on the other hand, should
not be overlooked since they are beneficial and seen as actionability for
policy establishment, system development, decision-making, and risk factors
prevention. The Apriori algorithm requires the selection of support and
confidence thresholds, this process is risky as exception rules can easily be
pruned out when we set the support threshold too high. The Predictive Apriori
algorithm was introduced by [58], made up to the limitation and dedicated to
mitigating the risks of trimming valuable exception rules.

Compare to the Apriori algorithm, the Predictive Apriori algorithm does not
require configuring thresholds for support and confidence metrics. Instead,
the algorithm determines an expected number of rules that want to be obtained
based on the “predictive accuracy”. In such a process, the algorithm can prune
redundant association rules efficiently. However, the pre-defining of the
predictive accuracy and the limitation of efficiency are still presented for
the Predictive Algorithm, as it also requires scanning through the entire
database continuously.

With the ARM techniques, the most strongly-related factor with thyroid disease
is elder age. The age group ranging from $60$ to $80$ is very likely to be
diagnosed with thyroid diseases, such as hypothyroidism or hyperthyroidism.
The age factor has always been discussed in the field. [59] once deployed
qualitative analysis for thyroid cancer risk factors investigation, and they
found that middle-aged people, specifically those aged from $50$ to $54$ are
at high risk of being diagnosed with thyroid cancer compared to other age
groups. Contradictory studies are often presented. A case-control study was
conducted by [60], and their results demonstrated that younger age is
statistically significantly related to the risks of establishing thyroid
cancer. The retrospective study implemented by [61] also indicated that
thyroid cancer is likely to be established in people age younger than $55$.

Other qualitative studies found risk factors correlated with the increasing
rate of thyroid cancer, including the history of thyroid disease [62, 42],
obesity [63, 64, 65], iodine deficiency [66], dietary habits [67], family
history [42], diabetes [44, 46], hormonal factors [68], and stress [69], to
name a few. Through the evaluation of quantitative methodologies, such as
logistic regression and cox regression, confirmed factors include the history
of thyroid disease (i.e., Hashimoto’s thyroiditis, thyroid nodules) [70] and
family history [71]. However, the remaining factors are still controversial
and cannot be ascertained based on existing studies, and these factors are
obesity, iodine deficiency, dietary habits, diabetes, and hormonal factors.
[72] once established a retrospective study with $467$ patients examined
through case-control technique, and they found tobacco use, alcohol
consumption, and obesity had no association with the increasing risk of
thyroid cancer; this finding aligns with [45]. Additionally, iodine intake is
always a debating factor, yet whether its excessive or deficient consumption
will lead to thyroid cancer is still unknown [73]. Similarly, food consumption
in daily dietary [67], diabetes [74], and hormonal factors [75] in relation to
thyroid cancer risk tend to be weak and inconsistent.

####  4.1.3 Thyroid Cancer Pathogenesis Literature Summary

Table 2: Thyroid Cancer Risk Factors (Last $10$ Years) Reference |  Methodology |  Findings
---|---|---
[76] |  Multi-variable analysis |  Presence of Hashimoto’s thyroiditis, Autoimmunity
[70] |  Logistic regression (LR) |  Smoking, History of thyroid nodules, History of non-thyroid cancer, Diabetes, Radiotherapy of head or neck
[67] |  Survey |  Consumption of meat, Regular use of multivitamins, Dietary nitrate and nitrite
[44] |  Survey |  Diabetes
[68] |  Survey |  Thyroid hormones, Insulin resistance, Adipokines, Inflammation, Sexual hormones
[77] |  Case-control study |  Nuclear fallout before $15$, History of medical radiation, Obesity, Tallness, Artificial menopause, Family history, Iodine deficiency, Spring drinking water
[66] |  Survey |  Iodine deficiency
[71] |  LR |  Marital status, Family history, Dietary iodine, Oxidative stress, Fast and fried food
[78] |  Survey |  Older age at menopause and parity
[79] |  Case-control study |  High body surface area, Great height, Excess weight, High body of fat percentage
[80] |  Survey |  Late age at menopause
[81] |  Cohort study |  Hysterectomy
[63] |  Survey |  Obesity, Overweight, Radiation exposure
[82] |  Survey |  Radiation, Smoking, Alcohol, Nutrition elements
[83] |  LR |  Obesity, Family history, Use of levothyroxine, TSH
[84] |  Retrospective study |  Younger age, Smaller nodule size, Hypo-echoic nodule, Calcification
[42] |  Survey |  Radiation exposure during childhood, Family history, Presence of Hashimoto’s thyroiditis
[43] |  Survey |  Genetic factors
[85] |  Case-control study |  Hormonal factors
[86] |  Survey |  Hashimoto’s thyroiditis, Elevated TSH
[87] |  Follow-up study |  Metabolic syndrome, Obesity
[59] |  Survey |  Geographical factors, Age, Higher BMI
[46] |  Survey |  Iodine deficiency, Diabetes, Pollutants, Radiation
[47] |  Mendelian randomization |  Telomere length, Waist-to-hip ratio, Diastolic blood pressure, Central obesity
[69] |  Survey |  Stress
[88, 89] |  Retrospective Study, Case-control study |  Body Mass Index (BMI), Obesity
[90, 91, 62] |  Survey, Cohort study |  History of thyroid disease
[92, 54, 55] |  ARM, Follow-up study |  Age
[93, 94, 95, 96, 97] |  Survey, LR |  Radiation exposure
[98, 13, 99, 64, 100, 65] |  Survey, LR, Cohort study |  Overweight, Obesity

Prior prospective studies determined that environmental or medical radiation
exposure during childhood, genetic factors, history of thyroid disease, and
family history are of high risks in relation to the development of thyroid
cancer; other factors beyond them need further interpretations and
observations (details see Table 2).

With the continuous progress of machine learning techniques, the utilisation
of ARM algorithms demonstrates increased efficiency in revealing the hidden
patterns for thyroid cancer pathogenesis. Nevertheless, the most significant
limitation of classic ARM algorithms is that they can only handle categorical
attributes. In this regard, the presence of a particular symptom or
comorbidity can be assessed, whereas further numerical attributes cannot be
taken into consideration. For instance, factors like blood pressure, BMI, or
weight has to be discretised and assigned to a definite genre for evaluations.
Additionally, with the increased complexity of electronic health records
(EHR), medical data is with high complexity and dimensionality; thus, the
traditional ARM techniques cannot deal with EHR records efficiently and
comprehensively. Another limitation of traditional ARM techniques is the
minimum threshold selection for support and confidence, which is still
challenging and risky. These shortcomings create limitations for the ARM
applications in the clinical domain. Ignoring those shortcomings will bring
substantial errors or perennial pauses in machine learning applications in the
clinical domain. Accordingly, more advanced ARM techniques are in demand for
revealing intricate disease epidemiology.

In this respect, the Frequent Pattern-Growth Tree (FP-Tree) algorithm
introduced by [101] harnessed the power of tree structures and enhanced
efficiency for mining association rules compared to Apriori. [53] proposed
fuzzy ARM to incorporate numerical and categorical attributes for findings
association rules. This concept has also been deployed already in the clinical
for heart disease diagnosis [102]. [103] introduced weighted ARM by generating
weighted support values for selecting rules, which has also been used for
comorbidity detection [104]. A prior study conducted by [105] proposed a
profile-based ARM technique which generates profiles for the individual
patient based on their medical conditions and aims to assess their risks of
developing heart disease through the incorporation of fuzzy ARM. These studies
are in the practice of developing precise association rules. Nevertheless,
studies incorporating such ARM techniques are still absent for thyroid cancer
pathogenesis identification. Bridging such a gap will potentially be a
breakthrough in the clinical domain. Moreover, developing a robust, adaptive,
and efficient ARM technique which considers the comprehensive attribute
formats is also in great demand.

###  4.2 Applications in Thyroid Cancer Diagnosis

CAD applications usually consist of four parts: pre-processing, segmentation,
feature extraction, and classification [106]. Medical images acquired from
cross-institutions are not standardised due to the differences in devices,
policies, or expertise. Thus, pre-processing of images is required to remove
noises, enhance image quality (i.e., contrast, colours, and sharpness), or
even augment dataset to make them consistent and adequate for further analysis
[107]. Afterwards, a segmentation step, which extracts the object for
detection from the background, is usually required to determine the region of
interest (ROI) [108]. Feature extraction primarily selects essential features
from the ROI based on domain expert knowledge, and this allows to form into a
feature set ready to be fed into the classifier for decision-making [106].
Classification is always the ultimate goal which decides the class of the
object (e.g., benign or malignant, stage of a particular disease) based on the
input feature sets [106]. Compared to manual diagnosis, CAD makes diagnosis
more accurate and efficient. CAD mitigates human false-positive rates and
achieves diagnosis through computational power, allowing clinicians to focus
more on patient care. Deriving from machine learning techniques allows CAD
applications to become relatively engaging in thyroid disease diagnosis in the
era of artificial intelligence.

####  4.2.1 Pre-processing

The essence of machine learning techniques is to train computers to learn from
data through statistical analysis; deriving from this, the quality of input
data is highly influential to machine learning-based CAD systems. With the
objective to enhance the CAD performance, the input data quality is the
primary to be ensured. Nevertheless, CAD applications generally require cross-
institutional evaluations, and the data quality can be affected by varied
institutional guidelines or policies. Under this scenario, standardizing the
input data is demanding to enhance its quality. Besides, data quality can also
be promoted based on volume, consistency, and timeliness. Accordingly, this
section presents some data pre-processing techniques for CAD design in the
thyroid cancer detection task.

Machine learning techniques make predictions or decisions based on the learnt
experience. To this end, the experience denotes the input data; thus, the more
volume of the input data is, the more accurate the predictions will be. In
this regard, many data augmentation techniques were proposed in CAD design.
For instance, [107] once deployed ultrasound images for detecting thyroid
cancer. The originally acquired dataset consists of $1791$ images, then the
authors applied image augmentation techniques, including adjusting image
contrasting and horizontal flipping. Afterwards, the authors augmented the
image set to $7360$ ultrasound images for training and testing. Additionally,
some researchers choose to mirror the images for augmentation purposes. For
example, [109] adopted an open-access ultrasound image set for thyroid nodules
recognition, and they have eventually augmented to $4000$ images, whereas the
original dataset only contains $451$ images. Other studies choose to crop
images into patches to increase the size of the input data [110], rotate
images [111], or even add or adjust the Gaussian noises for image augmentation
[112]. With varied extend of augmentation techniques, the CAD performance can
be increased, becoming an indispensable pre-processing step.

In the design of CAD applications for thyroid cancer, the use of medical
images is more prosperous than clinical features. However, the acquired
medical images are different in quality based on cross-institutional
guidelines, devices, or even the clinician’s expertise. In this regard,
standardizing the medical images is necessary for the pre-processing step. The
common tasks are removing image annotations [113] and speckle noises [114].
For instance, [115] once deployed the technique proposed by [116] to remove
artefacts in thyroid ultrasound images. Precisely, the authors extract the
non-zero region from the input image, plot a histogram containing the
artefacts, identify the histogram peaks as the intensity level of the
artefacts, and subtract the artefact pixels with the intensity levels to
restore the image without any annotations [115]. Besides, some researchers
adopted the adaptive median filtering (AMF) technique for annotations,
markers, and noises removal [117]. The median filtering algorithm detects the
impulse noise by comparing each pixel to its neighbours; when identifying the
impulse noise pixel, its value will be replaced by the median value of all the
neighbours [118]. The difference between the median filtering algorithm and
the AMF algorithm is that the filter size of the latter one can be changed
based on the characteristics of the input image [113]. As the most frequently
adopted technique, AMF is very efficient in restoring image quality, and its
implementation flowchart is demonstrated in Figure 7. Apart from annotation
and noise removal, images with different scales should also be resized to
reach consistency, and examples can be found in [114, 119].

Figure 7: Adaptive median filtering flowchart for noise removal

Compared to the standardized augmentation techniques, the annotations and
markers removal techniques are crucially monotonous. Medical images,
especially ultrasound images, are frequently deployed for thyroid cancer
detection through CAD systems. However, ultrasound images are operator-
dependent and highly sensitive, resulting in unavoidable speckle noises. Among
all the identified studies, scanty studies proposed noise removal techniques
apart from the AMF algorithm; thus, the pre-processing noise removal step is
considerably limited.

####  4.2.2 Segmentation

With the objective to detect thyroid cancer through CAD applications,
segmentation of medical images is crucial in accurate automatic nodule
assessment [120]. Image segmentation task allows extracting ROI from the
background, mitigating computational resources to diagnose the overall image,
and enhancing diagnostic performance by omitting insignificant features.

Medical image segmentation subsumes varied exceptional techniques tailored for
semantic entities extraction in the computer vision domain [121]. For
instance, [122] once evaluated three segmentation algorithms, including active
contours without edges (ACWE), graph cut, and pixel-based classifier using
thyroid ultrasound images. Specifically, the ACWE technique initialises an ROI
from the input image manually, in which the circled region will be denoted as
$1$, and the rest of the ROI will be denoted as $0$ as background. Then, the
ROI will be computed by using the Euclidean distance. The image force will be
calculated through Eq. 4, in which $I$ represents the original image, $\mu$
denotes the average value inside/outside the contour. Afterwards, the centre
of mass will be calculated for the segmented thyroid nodule; this helps find
the potential centre of mass of the nodule in the following image.

$F_{image}=\int_{inside}(I-\mu_{in})^{2}+\int_{outside}(I-\mu_{out})^{2}$ (4)

The second segmentation method [122] utilised is named “graph cut”, which was
inspired by the “GrabCut” technique proposed by [123]. Like the ACWE
algorithm, the graph cut technique initially requires the manual marking of
ROI. Then, the segmented ROI and the background will form into the Gaussian
Mixture Models (GMMs) through clustering; this allows assigning the Gaussian
components to the corresponding foreground and background pixels. Lastly, the
graph cut will find the new foreground and background pixels.

The third method is the pixel-based classifier that users will need to click
inside and outside of the ROI, and the features from both regions will be
passed into the decision tree for training. In this case, the extracted
features are the coefficient of variation and the mean of the two
neighbourhoods. Thus, the decision tree can automatically learn the foreground
and background from those features. However, this technique requires rigour
selection of ROI; otherwise, the segmentation of thyroid nodules can result in
erroneous.

Apart from the aforementioned techniques, [122] also deployed the most classic
convolutional neural network (CNN) architecture (i.e., U-Net) for thyroid
nodule segmentation. With the emergence of deep learning, artificial neural
networks (ANN), convolutional neural networks (CNN), and recurrent neural
networks (RNN) have become backbones for image analysis, pattern recognition,
and computer vision tasks. ANN and CNN are usually applied for classification
tasks, whereas RNN is tailored for natural language processing for time-series
predictions. The concept of CNN can be traced back to the $1980s$ [124], as it
emerged from the study of the brain’s visual cortex and has been widely used
for image classification and pattern recognition since then. CNN contains
three components, including the convolutional operation, pooling operation,
and fully connected operations.

_Convolutional Operation_ In a feed-forward CNN architecture, convolutional
layers are used as feature extraction operations [125]. The convolutional
layer breaks the input image into different local regions such as edges and
textures. The first convolutional layer focuses on extracting low-level
features and then assembling those features into a high-level abstract feature
map accordingly. Noteworthy, inputs were convolved with weights to form a
feature map, and all neurons in the same feature map have constraint equal
weights [125]. Accordingly, the feature map was calculated in Eq. 5, where
$Y_{k}$ is the feature map for the $k_{t}h$ convolution, $f(.)$ is the
activation operation, $w_{k}$ is the weight for neurons in the $k_{t}h$ step,
and $X$ is the input image. Conventionally, the sigmoid function was used as
the activation operation, yet today, the ReLU activation is more common for
producing non-linearity [126]. The ReLU activation formula is presented in Eq.
6 [127].

$Y_{K}=f(w_{k}\times X)$ (5)

$ReLU(x)=\left\\{\begin{array}[]{ll}x,&\mbox{if $x\geq 0$}\\\
0,&\mbox{otherwise}\end{array}\right.$ (6)

The convolutional operation has three hyper-parameters: filter, padding, and
stride. Kernel size, also known as filter size, determining the dimensions of
the sliding window over the input image, has been proven to be influential to
the performance of CNN. The typical kernel sizes for CNN are $3\times 3$,
$5\times 5$, and $7\times 7$, sometimes the $1\times 1$ kernel size will be
used to maintain the feature map size while adjusting the channel numbers.
Padding is for conserving input images at the borders of activation maps, and
usually, zero-padding is adopted for saving computational resources. The
stride is the steps taken by the sliding window in which the kernel size
decided to shift over at a time. Figure 8 presents a convolutional operation
where the input image is with width and height in RGB color as $224\times
224\times 3$, when traveling through the kernel size of $3\times 3$, the
receptive fields are the reflections in the input image with the same size of
filters, then the output feature map size was calculated using Eq. 7.
Specifically, $n^{h}$, $n^{w}$, and $n^{c}$ are the input image size, $k$ is
the kernel size, and $s$ is the stride steps.

$f=(\frac{\mathrm{n^{h}}-k}{s}+1)\times(\frac{\mathrm{n^{w}}-k}{s}+1)\times
n^{c}$ (7)

Figure 8: Convolutional operation

_Pooling Operation_ Pooling layers are essentially used to reduce the size of
feature maps generated by convolutional operations, thus reducing the
computational load. In addition, pooling layers only have kernel size and
stride step, and no padding parameters are for pooling layers. Pooling neurons
also have no weights; it aggregates the input using average and max operations
functions. Figure 9 presents the max-pooling operation.

Figure 9: Max-pooling operation

_Fully Connected Layer_ A fully connected layer converts the output from
convolutional and pooling operations into a vector representing the
probability that a particular feature belongs to a class. Moreover, the
softmax function is usually used in a fully connected layer to make
classifications or predictions for the input image, determining the class of
the object.

In general, the first CNN architecture was Le-Net introduced in $1998$ [128],
since then, AlexNet [129] and VGG [130] were developed for classification
purposes. These CNN models are in standardized architectures (Figure 10) that
stacks several convolution operations, following max-pooling or average-
pooling layers, and ended with fully connected layers.

Figure 10: Standardised CNN structure

With the increasing power of computational resources, more advanced CNN
architectures were designed for image classification or segmentation tasks,
such as ResNet [131] and Inception [132]. Among them, U-Net is a well-known
segmentation CNN model, which consists of down-sampling, up-sampling, and skip
connection modules, aiming for biomedical image segmentation tasks [133]
(architecture can be found in Figure 11. The down-sampling layers of U-Net
utilise convolutional operations to extract features from the input image. The
up-sampling layers restore the extracted features using the down-sampled
latent information. The skip connections feed the down-sampling feature maps
to the corresponding up-sampling feature maps; in the meantime, crop the image
from down-sampling to up-sampling to ensure the size is consistent. Besides
[122], other researchers also deployed the U-Net architecture for thyroid
nodule segmentation [134, 120, 135, 136].

Figure 11: U-Net architecture [133]

Although the U-Net architecture is the most classic segmentation baseline,
many researchers proposed varied models built upon it. For instance, [137]
modified the model to embed residual and attention blocks to the original
U-Net and called ReAgU-Net. The model presented an increased dice similarity
coefficient (DSC) score of $0.869$ compared to the U-Net of $0.820$ on thyroid
nodule segmentation. The DSC measure is a commonly used evaluation metric for
segmentation tasks, and it is calculated through Eq. 8, in which TP stands for
true positive, FP stands for false positive, and FN stands for false negative.

$DSC=\frac{2\times TP}{2\times TP+FP+FN}$ (8)

Similarly, [138] proposed attention-based U-Net. [139] deployed Res-U-Net
proposed by [140] to segment thyroid nodules on ultrasound images. [141] built
a dual-route mirroring U-Net called “DMU-Net”. [142] proposed a cascade U-Net
for thyroid nodule segmentation and classification. [143] introduced residual
dilated U-Net (resDUnet) for thyroid nodule segmentation, where [144] also
deployed dilation in the same task. The dilated convolution is referred to
“convolution with a dilated filter” [145]. In particular, the dilation in
convolutional operation supports the exponential expansion of the receptive
field without loss of resolution. Figure 12 demonstrates the dilated
convolution operation. The first subset is when the input image produces a
standard $3\times 3$ convolution with dilation equal to $1$. The second image
is produced using $3\times 3$ kernel and $2\times 2$ dilation rate, generating
the receptive field size of 7×7. The third image is produced when utilising a
$4\times 4$ dilated rate, generating a receptive filed size of $15\times 15$.
Through the use of dilated convolutions, the number of parameters is
identical, whereas the receptive field scale grows exponentially, allowing
more features to be captured.

Figure 12: Dilated convolution [145]

Owning to performance improvements in deep learning techniques, there has been
a concurrent rise in researchers shifting their focus from simply CAD
application to designing more exquisite and sophisticated models or modules
for thyroid nodule segmentation. [146] applied a fully convolutional network
(FCN) for this task, in which the model only performs convolution, pooling,
and up-sampling. Likewise, [147] proposed an encoder and decoder-based FCN
model for thyroid nodule segmentation and achieved $81.26\%$ DSC outperforming
U-Net. [148] also deployed encoder-decoder structure on thyroid ultrasound
images. Those segmentation-related studies harnessed the power of deep
learning algorithms dedicated to detecting thyroid ROI more efficiently and
precisely, leading to enhanced classification accuracy.

####  4.2.3 Feature Extraction

Thyroid cancer diagnosis follows a standard clinical procedure, including
medical imaging for detecting abnormality inside thyroid glands, FNAC, and
excisional biopsy if necessary for pathological examinations. For medical
imaging analysis, various types of medical image modalities can be deployed,
such as ultrasound images, CT scans, magnetic resonance imaging (MRI), radio-
iodine scintigraphy, and positron emission tomography (PET) scans. Among those
medical image modalities, ultrasonography is the most common medical image
modality for thyroid cancer detection. Accordingly, the features manually
extracted from ultrasound images are called “hand-crafted features”. Most
existing studies consider geometric, morphological, and texture hand-crafted
features from ultrasound images in a CAD setting. Geometric features are the
information used to evaluate an object with geometric elements to describe the
shape of irregularity [149]. Morphological features are information about
lesions’ morphological characteristics [150]. Additionally, texture features
are represented by an image’s contrast [150]. [149] extracted geometric and
morphological features from the open-access Digital Database of Thyroid
Ultrasound Images (DDTI). By augmenting the image set, they have extracted
$19$ geometric (e.g., solidity, rectangularity, Orientation, roundness,
centroid, etc.) and $8$ morphological features (e.g., area, perimeter, area
perimeter ratio, etc.) from $3,188$ ultrasound images. With the extracted
features, the classifier reached an accuracy of $99.33\%$ for detecting
malignant thyroid nodules. Similarly, [118] once deployed $9$ geometric
features (e.g., circularity, compactness, convexity, solidity, etc.) from
$165$ ultrasound images. Through a standardised pre-processing, segmentation
of ROI, and ultrasound feature extraction, the neural network reached an
accuracy of $0.9479$ classification performance.

Although geometric features are ordinary and sufficient for thyroid nodule
detection, integrating various feature sets will undoubtedly enhance the
cancer diagnostic rate. Therefore, besides deploying a solitary type of
feature set from ultrasound images, [150] combined textual and corresponding
morphological features (e.g., solidity, centroid, bounding box, etc.) for
thyroid cancer detection, and their work obtained a diagnostic accuracy of
$89.13\%$. With all the feature extraction-related studies, fair to moderate
agreements were reported that geometric and morphological features are
beneficial for establishing thyroid cancer detection CAD systems. Table 3
summarises the frequently utilised ultrasound features for thyroid cancer
detection.

Table 3: Commonly Extracted Ultrasound Features Feature |  Description
---|---
Area |  Area occupied by the object
Solidity |  Extent of a shape’s convexity
Centroid |  Center of the object
Perimeter |  Number of pixels of the object border
Convexity |  Ratio of the convex hull to the contour of the object
Tortuosity |  Comparison between an object’s major axis length and perimeter
Elongation |  Ratio between length and width of an object’s bounding box
Roundness |  Likelihood of an object being round
Filled Area |  Total number of pixels inside the bounding box
Orientation |  Direct or a position of an object
Eccentricity |  Ratio between major and minor axis length
Convex Hull |  A convex structure within a detected object
Convex Area |  Area surrounded by the convex hull
Aspect Ratio |  Ratio between object depth and width
Compactness |  Irregularity of an object
Bounding Box |  Bounding shape of an object
Rectangularity |  Likelihood of an object being rectangular
Elliptic Variance |  Comparison of an object shape to an ellipse
Circular Variance |  Comparison of an object shape to a circle
Convex Perimeter |  Perimeter of the convex hull given an object
Major/Minor Axis Length |  Lines lengths through the center of an object

Besides the geometric and morphological features, radiomics analysis is also
used commonly as a thyroid ultrasound feature extraction technique, and
examples can be found in [151, 152]. Radiomics analysis was introduced by
[153]. It sought to extract critical features from biomedical images for
detecting a particular disease by converting medical images into quantitative
numbers [154]. Specifically, the work conducted by [151] has extracted $1,079$
radiomics features from ultrasound images to detect lymph metastasis for
thyroid cancer patients. Similarly, [152] extracted radiomics features from
$302$ thyroid nodules.

Apart from using ultrasound images, a few studies proposed other medical image
modalities for feature extraction in thyroid cancer detection. For instance,
[154, 155] both applied radiomics analysis on magnetic reasoning images (MRI).
[156, 157] adopted computed tomography (CT) scans for radiomics analysis.
Additionally, [158] adopted morphological features from CT scans, and their
work reached a classification accuracy of $77.7\%$ for detecting PTC.

Ultrasound images have high dimensionality, and extracted features are usually
robust yet complicated. In this regard, it is recommended to reduce the
redundant and irrelevant features for enhanced classification performance.
Therefore, feature selection algorithms are generally inducted for recruiting
vital features to achieve accurate disease detection. [159] once compared five
feature selection algorithms with ultrasound image features, including three
filter-based and two embedded-based techniques. Feature selection algorithms
can be fallen into three categories, including filter-based, wrapper-based,
and embedded-based [159]. Filter-based feature selection algorithms consist of
two stages, including ranking the features and making classifications with the
highest-ranked features, and classic filter-based feature selection techniques
are Chi-square, Pearson’s coefficient, and Mutual information [160].
Nevertheless, the most significant limitation of filter-based algorithms is
that they can only select features independently, therefore, overlooking the
relevance or associations among varied features. On the contrary, wrapper-
based methods select features iteratively by searching through a subset of
feature combinations for classification evaluation, and traditional wrapper-
based techniques are recursive feature elimination and genetic algorithms
[160]. Embedded-based techniques usually select features directly from the
modelling process, typical example is decision tree [159]. Based on their
work, the utilised filter-based algorithms were Maximum Class Separation
Distance, Fisher Scores, and Relief.

Maximum Class Separation Distance is a feature selection method sorting the
differences between benign and malignant ultrasound image features, and they
selected features with the largest differences [159]. The differences can be
calculated through Eq. 9 where $N_{B}$ is the number of benign ultrasound
images, $N_{M}$ is the number of malignant ultrasound images, and $f$ is the
feature vector for the $i$th image.

$Diff=|\frac{\sum_{i=1}^{N_{B}}f_{i}}{N_{B}}-\frac{\sum_{i=1}^{N_{M}}f_{i}}{N_{M}}|$
(9)

Fisher score was introduced by [161] to derive a kernel function based on
generated probability model so that it can select features with higher
discriminate information [159], and it is calculated through Eq. 10.
Specifically, $C$ is the number of classes, $N_{k}$ is the number of $k$th
class, $\mu_{jk}$ is the mean for $j$th feature in $k$th class, and
$\sigma_{jk}$ is the standard deviation of the $j$th feature in $k$th class.

$Fisher=\sum\nolimits_{k=1}^{C}N_{k}(\mu_{jk}-\mu_{j})^{2}/\sum\nolimits_{k=1}^{C}N_{k}\sigma_{j}^{2}$
(10)

Relief is the method proposed by [162], the Relief method has a high tolerance
for noises, and it can select features even though they are interactive. The
Relief algorithm randomly selects a sample and its nearest neighbour in the
same class and updates their weights constantly. In work [159], they have
selected features from $200$ samples with $5$ nearest neighbours based on the
highest feature weights.

With the two wrapper-based feature selection algorithms, [159] also evaluated
their performance. Penalized Least-Squares Mutual Information aims to find a
sparse weight through maximizing the squared-loss variance of mutual
information[159]. The Hilbert-Schmidt Independence Criterion LASSO (HSIC
LASSO) is proposed by [163] to capture non-linear feature dependency. Based on
the analysis conduced by [159], when $90\%$ of classification accuracy
obtained, the minimum number of feature sets were used through the HSIC LASSO
technique with only $59$ features involved. The Max Distance however acquired
$1400$ feature sets for the classification task.

Figure 13: PCA and LDA comparison

Apart from those, [117] once adopted the principal component analysis (PCA)
technique for selecting features on thyroid ultrasound images. PCA is
introduced by [164] to reduce feature dimensionality, ensure explicit
interpretability, and minimize information loss. PCA analyzes observations
that have dependent and inter-correlated variables by extracting information
from data and forming a new set of orthogonal variables components. It also
identifies the patterns in observations and highlights their similarities by
calculating the mean centring for each dimension of observation, then
following the eigenvalue decomposition of the data matrix [165], and PCA is
frequently adopted in thyroid disease detection [166, 167]. [166] also adopted
PCA as feature extraction to distinguish instances among “normal”,
“hypothyroidism”, and “hyperthyroidism”. [167] applied PCA with an ensemble
method for thyroid disease detection.

Besides PCA, linear discriminant analysis (LDA) is also another feature
selection algorithm used quite commonly. LDA reduces feature dimensions yet
focus more on separating the known classes to the maximum extend. PCA aims to
maximize the variance of the input features, while LDA focuses on maximizing
the distance between different categories. Figure 13 presents their
difference.

Table 4: Ultrasound Features Selected for Thyroid Cancer Detection Reference |  Features Used
---|---
[168] |  Intercept, size, shape, margin, echogenicity, cystic change, micro-calcification, halo
[169] |  Micro-calcification, shape, margin, capsular invasion, architecture, echogenicity, ring down artifact, vascularity
[170] |  Gray level co-occurrence matrix, statistical feature matrix, gray level run-length matrix, law’s texture energy measure, neighboring gray level dependence matrix, wavelet features, Fourier features
[171] |  Shape, margin, echogenicity, internal composition, presence of calcification, peripheral halo, vascularity
[3] |  Shape, echogenicity, calcification, echo texture, margin, capsule invasion, halo
[172] |  Size, area, shape, color, texture of regions, histogram of oriented gradients, co-occurrence gray level matrices, chromatin density
[173] |  Histogram, intensity differences, elliptical fit, gray-level co-occurrence matrix, gray-level run-length matrix
[174] |  Location, position, shape, margin, internal contents, echogenicity, calcification, echo-genic foci in solid portion, halo, infiltration and extracapsular invasion, increased intra-nodular vascularity, abnormal lymphadenopathy, multifocal
[175] |  Gray level co-occurrence matrix, Local binary patterns, Histogram of oriented gradient, Scale-invariant feature transform, vector of locally aggregated descriptors
[176] |  Margin, internal content, anteroposterior dimension-to-transverse dimension ratio, microcalcifications
[177] |  Size, margin, shape, aspect ratio, composition, calcification
[178] |  Size, morphology, location, echo, margin, boundary, surrounding tissue, posterior echo, calcification
[179] |  Aggressive histology subtype, vascular tumor capsular invasion, extra-thyroidal extension, regional metastases, and distant metastases
[180] |  Composition, echogenicity, orientation, margin, shape, spongiform, calcification, elasticity, vascularity
[181, 182] |  Composition, shape, margin, orientation, echogenicity, spongiform status
[183, 184, 185] |  Composition, echogenicity, calcification, margin, shape
[186, 23, 187] |  Size, margins, shape, aspect ratio, capsule, hypo-echoic halo, internal composition, echogenicity, calcification pattern, vascularity, and cervical lymph node status

Based on the identified studies, researchers tend to apply various feature
selection algorithms to establish thyroid cancer diagnostic systems, and the
commonly selected features from ultrasound images are summarized in Table 4.
The most commonly used features are nodular size, shape, margin, composition,
and calcification presence. Those knowledge extracted from medical images will
then be fed into machine learning or deep learning classifiers for evaluate
the thyroid status for disease detection.

####  4.2.4 Classification

With the development of electronic computers, algorithms were developed
rapidly in the $1950$s. Three basic machine learning algorithms emerged then,
including symbolic learning, statistical learning, and neural networks. Those
three branches grew more advanced and become the well-known classifiers today
“decision trees, K-nearest neighbours (KNN), and multi-layer feed-forward
neural networks” [188]. In this regard, various CAD systems built upon those
classifiers, such as support vector machines (SVM) to diagnose breast cancer
[189], Naive Bayes (NB) to detect brain tumour [190], multi-layer perceptron
to diagnose liver cancer [191], to name a few. Generally, the essence of
machine learning is to train computers to learn from data through statistical
analysis. Deep learning is emerged as a subset of machine learning and has
become an intense tool for computer vision tasks. Recently, deep neural
networks have been used frequently in CAD design for helping to make
diagnostic decisions and have shown satisfying performance, such as detecting
diabetic retinopathy [192], Covid-$19$ detection [193, 194], and malaria
diagnosis [195]. Studies using machine learning-based CAD application to
detect thyroid cancer is also abundant.

[196] once implemented the Naïve Bayes (NB) classifier on $41$ thyroid nodules
($21$ benign and $20$ malignant) from $37$ patients and compared with two
experienced radiologists. The results obtained by the NB algorithm
outperformed the radiologists and reached an area under the curve (AUC) of
$0.851$. Similarly, [197, 186, 23] acquired self-obtained ultrasound features
for making classifications and obtained comparable results to experienced
radiologists.

With varied image characteristics (i.e., image quality, image size, image
scale), researchers usually spend a significant amount of time evaluating
heterogeneous feature extraction algorithms for classification purposes. To
mitigate the manual process during feature selection, deep learning techniques
emerged to help make the diagnosis much more efficiently by extracting and
selecting features automatically from input images and making classification
decisions correspondingly. To this extent, researchers can shift their focus
to designing a more accurate and adapted classifier for disease detection.

As introduced earlier, deep learning is a subset of machine learning, and
included but not limit to ANN, CNN, and RNN models. In some earliest works,
ANN has been adopted relatively often using ultrasound images to make a
thyroid cancer diagnosis. According to [171], $689$ thyroid nodules were
examined using ANN and reached an accuracy of $83.1\%$. Similarly, [185]
compared SVM classifier with ANN through $348$ thyroid nodules, and the
diagnostic accuracy rates are $69\%$ and $74\%$ respectively. More recently,
CNN has been adopted more frequently regarding detecting thyroid cancer
through ultrasound images.

CNN models play vital roles in the computer vision domain; not only they can
be used for segmentation tasks, but also for localisation and classification
tasks. Thyroid cancer diagnosis through CNN is natural and intrinsic. For
instance, [198] acquired $131,731$ ultrasound images for cross-institutional
analysis through the CNN model. Their work demonstrated an accuracy of
$0.889$, $0.856$, and $0.915$ for the Tianjin, Jilin, and Weihai cohorts.
[199] acquired ultrasound images from Germany and applied CNN for thyroid
cancer detection, and their work obtained an accuracy of $0.78$, which was
lower than experienced radiologists. [200] proposed a generic eight-layer CNN
model for classifying thyroid and breast lesions, and with thyroid cancer
detection, the accuracy rate was $86.5\%$ on $719$ ultrasound images. [201]
used the Inception model to detect thyroid cancer and reached an accuracy of
$87.2\%$ with the DDTI ultrasound database. [107] once compared VGG$19$,
ResNet$101$, and InceptionV$3$ models on $812$ ultrasound images, and the
best-performing model was ResNet with $77.6\%$ accuracy rate reached.

VGG models are generic and adaptive as the convolution operations and the
fully connected layers can be adjusted for a specific task. The commonly used
architectures for VGG are VGG-F, VGG$11$, VGG$16$, and VGG$19$. Theoretically,
the more convolutional operations we stack to the CNN, the better the
classification results will be; nevertheless, this is not the real-world case.
The reality is that when more convolutions are stacked, the model will likely
get a gradient explosion. In this regard, the ResNet model was proposed to
address the issue as it can generate very deep CNNs, avoiding aggregating
parameters that take exponentially increased computational resources [131].
More importantly, ResNet produces better classification results than
conventional CNN stacking architectures as it can learn residuals through
layers. The Inception model was proposed by [132], allowing for stacking
multiple Inception modules to generate more accurate results. Each Inception
module concatenates feature maps generated through different kernel sizes with
different information learned through the varied size of receptive fields.
Figure 14 demonstrates a ResNet block and an Inception module.

Figure 14: ResNet and Inception Blocks

The use of CNN architecture demonstrates varied performance following the
different image analysis steps with heterogeneous image quality. Under most
situations, the proposed models are limited in generalising to different
datasets, and there is a possible over-fitting concern for most previous
works. In this regard, transfer learning has been applied quite often to
mitigate the over-fitting phenomenon. Transfer learning uses parameters learnt
from pre-trained neural networks and applies those “gained knowledge” to new
tasks by freezing the previous layers and making changes to the last few
layers. For example, [202] proposed the integration of two pre-trained CNNs
through transfer learning, where the shallower network was used for learning
low-level features, and the other deeper network was used to learn high-level
abstract features. Then the two learned feature maps from two CNNs were fused
as an input into a softmax layer to diagnose malignant thyroid nodules,
resulting in diagnostic accuracy of $83.02\%$. Furthermore, [115] fine-tuned
an Inception model and tested it on two ultrasound databases. For the DDTI
set, the accuracy was $98.29\%$, and for the private data set, the accuracy
was $96.34\%$. A moderate consensus was made that CNN applications on
ultrasound images for detecting thyroid cancer are efficient and accurate
[176, 184, 178, 111].

There has been a concurrent rise in applying other image modalities for
thyroid cancer detection rather than ultrasound. For example, [203] adopted
VGG-$19$ and InceptionResNetV$2$ to multi-classify thyroid nodules into seven
classes through histopathology images, including normal tissues, adenoma,
goiter, papillary cancerous nodule, follicular cancerous nodule, medullary
cancerous nodule, and anaplastic cancerous nodule. The results suggest that
VGG-$19$ yields better average accuracy than InceptionResNetV$2$ that is
$97.34\%$ and $94.42\%$ respectively. Similarly, [204] adopted the Multiple-
instance Learning (MIL) approach on segmented whole-slide images to predict
malignancy of thyroid tissues. [205] trained ResNet$50$ and DenseNet$121$
models through transfer learning with histopathology images and reached an
accuracy of $100\%$. [206] acquired FNAC cytological images to detect
medullary thyroid cancer, and the CNN model reached an approximated
classification accuracy of $99.00\%$. The DenseNet was proposed by [207] where
it contains three dense blocks. Each layer from a dense module connects to all
its preceding layers as the input (Figure 15).

Figure 15: Dense Blocks

Apart from those, [208] adopted eight CNNs on CT scans to differentiate
thyroid cancerous metastasis, including DenseNet$121$, DenseNet$169$,
InceptionResNetV$2$, InceptionV$3$, ResNet, VGG$16$, VGG$19$, Xception. Based
on their comparison, the best AUC was obtained by InceptionV$3$ and ResNet,
around $0.95$. [209, 119] compared ultrasound and CT modalities on CNN
performance and found that ultrasound has a slightly better result than CT.
[210] utilised MRI on multi-modality CNN for classification and reached a
diagnostic accuracy of $0.82$ on $45$ images. [211] constructed a multi-input
CNN for thyroid cancer diagnosis from $49$ patients who underwent MRI tests,
and their model reached an accuracy of $0.88$. We found that ultrasound seems
to lose its dominance in thyroid cancer detection; more medical image
modalities were analysed through the use of CNN, including but not limited to
CT scans [212], hyperspectral imaging [213], and SPECT images [214].

####  4.2.5 Thyroid Cancer CAD Literature Summary

Machine learning-based CAD decision support systems for thyroid cancer appear
to present a varied performance from $70\%$ to $90\%$; the main underlying
reason might be aroused from acquired data sets qualities. Medical data sets
are challenging to be acquired as they are correlated with ethical and privacy
concerns. With machine learning, it is of primary demand to acquire extensive
high-quality data for training unbiased models. In the real-world setting,
high-quality medical data sets are scarce, much less is to say the expensive
labour sources of data labelling and annotations process, which aggravates the
challenges of data acquisition. Under this scenario, the open-access datasets
are valuable for researchers to evaluate their CAD systems and provide
baseline comparisons. Table 5 demonstrates the open-access datasets for
thyroid disease detection tasks frequently used in existing studies.

Table 5: Open-access datasets for thyroid disease detection Data Source | Data type | Sample Size
---|---|---
Gene Expression Omnibus repository | Numerical |  $25$ samples
UCI Machine Learning Repository | Numerical, categorical |  $7,200$ instances
Knowledge extraction based on evolutionary learning | Numerical, categorical |  $7,200$ instances
Kaggle database | Numerical, categorical |  $3,772$ instances
Digital database of thyroid ultrasound images (DDTI) | Ultrasound images |  $400$ instances
Wilmington endocrinology database | Ultrasound images |  $59$ images
Papanicolaou Society of Cytopathology | FNAC images |  $110$ images
National Cancer Institute TCGA | Whole-slide image |  $507$ instances

Since machine learning approaches expect scholars to compare their proposed
works with existing benchmarks, open-access databases are vital. With the
limited number of sample instances in self-acquired datasets, not only the use
of open-access datasets is essential, but also data augmentation is critical.
Data augmentation methods for medical images are in the cookie-cutter
stereotype, such as flipping or rotating. However, annotations on each medical
image suggest the nodule location. Changing the image’s direction will
correspondingly affect the nodule position; this, in turn, may influence the
model’s performance. Additionally, data augmentation methods were usually
applied to medical images, whereas studies utilising clinical features (e.g.,
morphological or texture information) scarcely deployed such augmentation
steps. Therefore, standardised data augmentation techniques may work well on
natural images, whereas they are not appropriate for medical images; future
research needs to investigate more solid and suitable augmentation techniques
for medical images and clinical features.

The CAD process is tedious and requires sufficient training, evaluation, and
selection of the most appropriate solution. In the clinical domain, efficiency
is just as imperative as accuracy; thus, removing unnecessary steps is always
encouraged. In the segmentation tasks, removing annotations and segmenting ROI
can be conducted simultaneously using De-noise auto-encoders. They are
efficient in noise removal and have already been applied in the clinical
domain for breast lesion detection [215] and brain lesion detection [216].
However, the use of this architecture in thyroid cancer detection is still
absent. Future research studies should focus on shortening the CAD diagnostic
efficiency without harming its accuracy and propose more effective, accurate,
and adaptive models.

Moreover, diagnosis is correlated with precision treatments to achieve a
promising prognosis. Thyroid cancer has several kinds of subtypes, all
resulting in different treatment protocols. Therefore, for the sake of
customised treatments and a well-established prognosis, the diagnosis should
be made more targeted. However, existing studies mainly focus on the binary
classification to detect benign and malignant thyroid nodules. Among all the
identified thyroid cancer CAD-related literature studies, only a few have
applied multi-class classification tasks to determine thyroid cancer subtypes
[203, 119]. Hence, studies around the multi-class classification of thyroid
cancer are significantly limited. More studies should be proposed from the
perspective of multi-classifying thyroid cancer subtypes to achieve
comparative diagnoses made by experienced clinicians.

Furthermore, most existing studies are dedicated to applying ultrasound images
for making diagnoses through machine learning techniques. Thyroid disease can
be diagnosed through different medical images; relying on a single type of
medical image may resist the diagnostic accuracy. Therefore, more approaches
should be designed using different medical data sets rather than depending on
ultrasound images for evaluation. Incorporating text-based reports, clinician
notes, or even varied image modalities is recommended to drive a more
comprehensive and accurate diagnostic decision.

Those aforementioned shortages create limitations for applying CAD systems in
the clinical field. There is still a long way for clinics to trust and get
comfortable with the CAD models, as they need evidence to show their
practicability. Bridging the gaps, researchers should establish favourable
benchmarks for evaluating their machine learning approaches, and CAD systems
should also target dealing with limited-quality medical data sets. Besides,
evidence to show the feasibility and interpretability of deep learning models
is demanding so clinicians can evaluate the techniques and comprehend the
decisions the systems make.

###  4.3 Applications in Thyroid Cancer Prognosis

With the progress of CAD developments, a manifesto for promoting an accurate
diagnosis has been achieved, whereas precision medicine is a prospective
phenomenon to be launched. Individual patients have varied health conditions
or disease stages, and treatment generalisation is not appropriate in this
regard. Precision medicine, on the other hand, is tailored for individual
health care on the basis of the target’s genes, lifestyle and environment
[217], maximising the patient’s health status after targeted treatments. In
the clinical domain, customised treatment plans contrive to betterment the
prognosis for individual patients. Establishing sagacious decision support
systems on treatment protocols enlarges the applications of precision
medicine. More importantly, predicting thyroid cancer patients’ death,
survival, and recurrent rates will potentially guide them to achieve an
optimal prognosis.

####  4.3.1 Treatment Decision Support System

Thyroid cancer risk stratification depends on its subtypes, with papillary
kind being the least severe kind and anaplastic being the most severe kind
based on their death rate [30], and different subtypes undergo varied
treatment protocols. Treatment protocols rely on individual patients’ health
conditions, such as age, weight, body mass index, pregnancy status, medical
history, and medication doses for other diseases. Sometimes, external factors
like seasonal temperature, financial status, and the patient’s wills also play
significant roles in developing treatment plans [218]. To this extent,
precision medicine and treatment have to be achieved for the target patient.
With the increasing instances of thyroid cancer, CAD applications have been
generated more often, whereas precision medicine development has been somehow
neglected in the past few years. Therefore, customized treatment protocols
should be offered to individual patients based on their expectations to design
a thyroid cancer-targeted treatment decision support system.

Among the identified studies, customised treatment decision support systems-
based works are limited. [219] once adopted the density-peaked clustering
analysis technique for disease symptoms clustering. Meanwhile, they adopted
the Apriori algorithm for establishing treatment rules, called the Disease
Diagnosis and Treatment Recommendation System. In [219]’s work, they have also
applied the Apriori algorithm to detect the association relationships between
the symptom clusters and the treatment schemes and have yielded satisfying
performance; meanwhile, the system’s interface was also designed and tested.

[220] once applied the DeepSurv model to develop personalized treatment
protocols for patients with a particular disease. The DeepSurv is a feed-
forward “Cox proportional hazards deep neural network” used to model the
interactions between a patient’s covariates and treatment effectiveness [220].
The typical structure of the DeepSurv model is presented in Figure 16. The
input dataset will travel through several stacked, fully connected, and
dropout layers and end with a linear function. Lastly, the model produces an
output of the predicted log-risk function to determine the survival time of a
set of patients with personalized treatment protocols established.

Figure 16: DeepSurv Model [220]

Besides the Apriori-based and DeepSurv models, the fuzzy cognitive map
approach has been implemented a few times to design a patient-centric
treatment decision support system. A fuzzy cognitive map (FCM) incorporates
ANN and fuzzy logic and shares similar logic with human reasoning and human
decision-making [221]. Specifically, FCM integrates qualitative and
quantitative data, and it looks like a cognitive map consisting of concepts
and relationships. FCM can model complex systems and is tailored for
developing decision-making systems, particularly disease treatment decisions.
FCM consists of concepts and weights. The concepts are the representative
variables for making treatment decisions, for example, patient age, nodule
size, nodule location, metastasis extent, to name a few. The directed edges
with arrows present the degree of the relationship between interdependent
concepts, and they are known as weights. The fuzzy rules infer with the
weights. Figure 17 demonstrates the basic FCM architecture. The mathematical
representations of a FCM can be achieved through Eq. 11. Specifically, The
$A_{i}^{(k+1)}$ is the value for a concept at simulation step $k+1$. $w_{ji}$
is the weight for the interconnection between two concepts. $N$ is the total
number of instances. $f$ is the sigmoid threshold function. And the FCM
approach has been applied several times in designing personalised disease
treatment recommendation systems [222, 221, 223].

Figure 17: Fuzzy Cognitive Map Architecture

$A_{i}^{(k+1)}=f(A_{i}^{k}+\sum_{j=1,j\neq i}^{N}A_{j}^{k}w_{ji})$ (11)

####  4.3.2 Death, Survival, and Recurrence Prediction

Several judicious studies proposed utilizing machine learning approaches to
evaluate the prognosis of thyroid cancer, including the survival rate, death
rate, and recurrence rate of patients diagnosed with thyroid cancer. Among the
identified papers, ANN has been adopted most often [224]. For instance, [225]
has applied SEER data on MLP and logistic regression to predict the survival
rate for $7,706$ patients with thyroid cancer, resulting in the better
performance obtained by the MLP approach. Similarly, [226] once adopted
decision trees, regression trees, and the Cerebellar Model Articulation
Controller neural network to identify leading factors that might influence the
survival of patients with anaplastic thyroid carcinoma. Their results showed
that age, the growth rate of tumours, metastasis distance, the patient’s
health condition, performance, and concomitant diseases should all be
considered when predicting the individual’s survival. Similarly, [37] has
applied three ANNs, specifically Multi-layer Perceptron (MLP), to the U.S.
SEER-$18$ database, which consists of $25,063$ patients with thyroid cancer,
to determine the probability of death rate caused by thyroid cancer. Their
first MLP has selected input features, including patient gender, age, race,
tumour size, primary disease extent, nodular location, and er of positive
lymph nodes. With a $19$-layer MLP, the ANN would output the patient’s status,
which is still alive or dead. The second MLP only inputs age, primary disease
extends, and nodule locations selected by the filter-based feature selection
algorithms with $18$ hidden neurons and the same output. The third MLP is
designed with three input features: tumour size, number of positive lymph
nodes, and metastases. Moreover, the architecture has $4$ hidden neurons, and
the outputs are maintained from the second MLP architecture. The best
performing model was the first MLP that reached an accuracy of around
$94.49\%$.

Researchers also pay close attention to the recurrence rate of thyroid cancer.
For instance, [227] compared five machine learning models to evaluate the
recurrence rate of patients with papillary thyroid carcinoma. The input
features they selected were age, gender, tumour size, tumour multiplicity,
lymph nodes metastasis, lymph nodes ratio, extranodal spread, and
extrathyroidal extension. The selected models were decision tree, random
forest, XGBoost, LightGBM, and Stacking. The decision tree outperforms the
other four and has reached a prediction rate of $95\%$. [228] has applied the
Ensemble Algorithm for Clustering Cancer Data (EACCD) algorithm to design a
prognosis system for thyroid cancer patients to minimize the probabilities of
recurrence. Through a set of input features: tumour size, lymph nodes,
metastasis, and age, the EACCD algorithm was applied. It consists of three
steps, including defining the initial dissimilarities between classes,
applying ensemble learning to obtain the learned dissimilarities, and
clustering the combinations of the learned dissimilarities. [229] applied the
multivariate logistic regression on $217$ patients’ lymph ratios to determine
the recurrence of papillary thyroid cancer. The results show that the lymph
node ratio is a significant factor that correlates with PTC recurrence.

####  4.3.3 Thyroid Cancer Prognosis Literature Summary

Patients diagnosed with thyroid cancer concerns the most about the treatments,
recurrence, survival, and death rates. Based on our analysis, studies on
machine learning-based treatment decision support systems are far less than
those on CAD systems. Among all the identified works, none of them was
designed and targeted specifically for thyroid cancer. Unlike the CAD systems,
the treatment decision support systems are much more interpretable and
acceptable by clinicians since factors affecting the treatment decision-making
are easily qualified and quantified by clinicians. Moreover, clinicians are
crucially involved in the system’s development phase. However, the works
around this area have been considerably ignored. With the customised treatment
decision support system design, individual patients can obtain optimal
prognostications by receiving the most fitted treatment protocols so that the
recurrence or even death rates can be dramatically mitigated.

Based on our analysis, most of the established work was proposed in $2005$.
Nevertheless, today is the artificial intelligence era, and many more advanced
machine learning techniques have emerged yet have not been applied in the
clinical field for thyroid cancer treatment. Hence, we expect a thyroid
cancer-specific treatment decision support system to be effectively generated
and proposed by adopting current state-of-the-art algorithms by taking into
consideration all related factors, such as patient information (i.e., age,
gender, BMI, height, weight), medical history, dietary, financial conditions,
disease subtypes, illness degree, medication intake, to name a few. Through
comprehensive factor analysis, an optimal treatment plan can be provided for
patients to achieve customized treatments to enhance clinical trust leading to
potential adoption.

Notably, the most challenging issue the researchers face today is the limited
data set, where retrospective medical data sets are vital. Retrospective data
contains patient information from the diagnosis to the prognosis, usually
suggested being archived for five to ten years. In order to have a valid and
feasible algorithm to detect the survival rates (i.e., $5$-year, $10$-year,
$20$-year), death rates, and recurrence rates for patients with thyroid
disease, it requires a large amount of high-quality retrospective clinical
data. Therefore, retrospective medical data sets are precious, making the
predictions on the prognosis of thyroid disease extremely tricky; that is why
this research field expects a data set that is authentic and complete.

Additionally, machine learning approaches usually take patient age, tumour
size, and lymph metastasis factors for making predictions, yet ignore another
vital factor, “time”. To develop algorithms that can predict the prognostic
status, the “time” factor is particularly imperative since clinicians would
potentially prepare for the upcoming health changes appearing in the patient.
Hence, we expect more algorithms like RNN to get involved, which takes time
into consideration to make predictions so that patients can better understand
their health status to guide them into an optimal health record.

In summary, more advanced machine learning algorithms should be involved in
the development of thyroid cancer-specific treatment recommendation systems.
As for the prognostic predictions of thyroid disease, retrospective medical
data is challenging to acquire, leading to limited studies in this field.
Hence, an open-access database will be significantly helpful for promoting the
developments in the domain. Another worth mentioning point is that time is
crucial for survival, death, and recurrent rates prediction, while it has been
neglected in previous studies. We urgently need the means to see more machine
learning approaches that incorporate the time factor into forecasts to help
patients prepare so that they can gain a promising prognosis.

##  5 Challenges and Recommendations

Although machine learning and deep learning techniques brought unprecedented
advantages to the clinical field, there still exist many challenges and
opportunities for researchers to excavate. This section highlights the open
issues and corresponding future research directions.

Thyroid cancer pathogenesis is always of immediate attention to be discussed
and researched in the past decades. Researchers dedicated to put their effort
into identifying risk factors correlated with thyroid cancer through both
qualitative and quantitative techniques. However, many of the factors are
still controversial and need further evaluations. Among the identified
studies, the use of data mining techniques to reveal thyroid cancer
pathogenesis is still under-researched; future work should investigate more
data mining algorithms to address the issue.

With the development of machine learning-based CAD approaches, the diagnosis
of thyroid cancer was more accurate with less human involvement. Those CAD
systems require extensive high-quality data to train a sophisticated neural
network. However, high-quality medical datasets are rare. Currently, most of
the well-performing neural networks were pre-trained through transfer
learning; this potentially causes an over-fitting issue that might be biased
when evaluating the model relying on a single data source. Moreover, medical
images have different characteristics from natural images on ImageNet [230],
as medical images are less heterogeneous in structure. Therefore, we recommend
training a CAD model with a specific thyroid medical image set and evaluating
the system using multiple data sources to ensure the fairness and
generalisation of the model; this potentially enhances the CAD adoption in the
clinical field.

As mentioned earlier, high-quality medical data is rare because standardising
the dataset in the pre-processing stage is complicated and time-consuming, and
collecting them requires ethical agreements. Among those studies that have not
utilised open-access data sets, the scale of the acquired private data set is
relatively small. To address the solutions of the small data set issue, data
augmentation is an inevitable procedure. Typical data augmentation approaches
are synthetic augmentation (to increase data size by generating new data),
oversampling, and bootstrapping. With medical images, data augmentation can be
achieved through cropping, flipping, rotating, and mirroring. Therefore, when
the input data set is imbalanced or small-scale, data augmentation approaches
should be adopted for enhancements. Furthermore, we do expect to see more data
be donated as open-access for research purposes, and this will undoubtedly
offer more opportunities to develop and evaluate more robust systems. This can
be achieved by offering a dataset where individual researchers can contribute
little by little and eventually form into a medical data set containing a
tremendous amount of high-quality records and is available to everyone.

Moreover, existing studies focus their attention on classifying hypothyroid
and hyperthyroid, or benign and malignant nodules, rather than detecting
thyroid disease subtypes. Hence, we expect to see more machine learning
approaches proposed for multi-classifying thyroid disease subtypes so that
patient-centric treatments can be ensured to reduce morbidity rates.
Additionally, text-based reports and varied medical image modalities
complement one another, and they should be incorporated for diagnosis rather
than relying on a single type of medical data set that lacks authenticity and
limits the accuracy of the proposed work.

The issue around the machine learning-based treatments for thyroid disease is
that most studies applied machine learning approaches to design personalised
treatment recommendation systems rather than explicitly built for thyroid
disease or based on thyroid-related data set. Additionally, the evaluations of
those treatment recommendation systems were significantly ignored by
researchers. We believe there is a significant demand for treatment
recommendation systems specifically built for thyroid disease in future works,
and more comprehensive factors should be taken into consideration during the
developing stage. Moreover, the evaluations of those systems must be arisen to
enhance the potential adoption in the clinical; this can be achieved by
clinicians’ involvement to accelerate the design and adoption of such systems.

Furthermore, with the studies on the prognosis of thyroid cancer, the input
features for the prognostic, predictive models did not consider “time” as an
essential attribute, which can immensely concern the performance of any
survival, death, or recurrence prediction models. The time factor is expected
to be incorporated in future works to help build an optimal model so that
clinicians and patients can be well prepared for the health condition changes
appearing in the subsequent period intervals to achieve an ideal prognosis.

Last but not least, for the diagnostic and prognostic systems with thyroid
disease, supervised models were used mainly, while treatment recommendation
systems usually establish unsupervised models. In reality, unlabeled medical
datasets are usually excluded from research, and this leads to huge data
waste. Combining supervised and unsupervised (i.e., semi-supervised) machine
learning models might accelerate the utilization of unlabelled data. We expect
to see more studies to deal with data waste issues in the field by designing
potent semi-supervised decision support systems.

## References

  * [1] F. M. Salman, Abu-Naser, and S. S., “Thyroid knowledge based system,” International Journal of Academic Engineering Research, vol. 3, no. 5, pp. 11–20, 2019.
  * [2] Z. W. Baloch, S. L. Asa, J. A. Barletta, R. A. Ghossein, C. C. Juhlin, C. K. Jung, V. A. LiVolsi, M. G. Papotti, M. Sobrinho-Simões, G. Tallini, and O. Mete, “Overview of the $2022$ who classification of thyroid neoplasms,” Endocrine Pathology, vol. 33, p. 27–63, 2022.
  * [3] U. R. Acharya, G. Swapna, S. V. Sree, F. Molinari, S. Gupta, R. H. Bardales, A. Witkowska, and J. S. Suri, “A review on ultrasound-based thyroid cancer tissue characterization and automated classification,” Technology in Cancer Research & Treatment, vol. 13, no. 4, pp. 289–301, 2014.  PMID: 24206204.
  * [4] P. Nix, A. Nicolaides, and A. P. Coatesworth, “Thyroid cancer review 1: presentation and investigation of thyroid cancer,” International journal of clinical practice, vol. 59, no. 11, pp. 1340–1344, 2005.
  * [5] C. L. Vecchia, M. Malvezzi, C. Bosetti, W. Garavello, P. Bertuccio, F. Levi, and E. Negri, “Thyroid cancer mortality and incidence: a global overview,” International journal of cancer, vol. 136, no. 9, pp. 2187–2195, 2015.
  * [6] A. C. Society, “Cancer facts & figures 2022,” 2022.
  * [7] M. Pizzato, M. Li, J. Vignat, M. Laversanne, D. Singh, C. La Vecchia, and S. Vaccarella, “The epidemiological landscape of thyroid cancer worldwide: Globocan estimates for incidence and mortality rates in $2020$,” The Lancet Diabetes & Endocrinology, vol. 10, no. 4, pp. 264–272, 2022.
  * [8] R.-M. Feng, Y.-N. Zong, S.-M. Cao, and R.-H. Xu, “Current cancer situation in china: good or bad news from the 2018 global cancer statistics?,” Cancer communications, vol. 39, no. 1, pp. 1–12, 2019.
  * [9] M. Massimino, D. B. Evans, M. Podda, C. Spinelli, P. Collini, N. Pizzi, and A. Bleyer, “Thyroid cancer in adolescents and young adults,” Pediatric Blood & Cancer, vol. 65, no. 8, 2018.
  * [10] M. J. Kim, D. Kim, J. S. Koo, J. H. Lee, and K.-H. Nam, “Vitamin d receptor expression and its clinical significance in papillary thyroid cancer,” Technology in Cancer Research & Treatment, vol. 21, p. 15330338221089933, 2022\.
  * [11] K. Suzuki, V. Saenko, S. Yamashita, and N. Mitsutake, “Radiation-induced thyroid cancers: Overview of molecular signatures,” Cancers, vol. 11, no. 9, 2019.
  * [12] S. Buscemi, F. M. Massenti, S. Vasto, F. Galvano, C. Buscemi, D. Corleo, A. M. Barile, G. Rosafio, N. Rini, and C. Giordano, “Association of obesity and diabetes with thyroid nodules,” Endocrine, vol. 60, no. 2, pp. 339–347, 2018.
  * [13] J. Ma, M. Huang, L. Wang, W. Ye, Y. Tong, and H. Wang, “Obesity and risk of thyroid cancer: evidence from a meta-analysis of 21 observational studies,” Medical science monitor: international medical journal of experimental and clinical research, vol. 21, p. 283, 2015.
  * [14] H. Zhao, H. Li, and T. Huang, “High iodine intake and central lymph node metastasis risk of papillary thyroid cancer,” Journal of Trace Elements in Medicine and Biology, vol. 53, pp. 16–21, 2019.
  * [15] K. Kim, S. W. Cho, Y. J. Park, K. E. Lee, D.-W. Lee, and S. K. Park, “Association between iodine intake, thyroid function, and papillary thyroid cancer: A case-control study,” Endocrinology and metabolism, vol. 36, no. 4, 2021.
  * [16] A. Cho, Y. Chang, J. Ahn, H. Shin, and S. Ryu, “Cigarette smoking and thyroid cancer risk: a cohort study,” British journal of cancer, vol. 119, no. 5, 2018.
  * [17] J. S. D. Kust and N. Mateša, “Bethesda thyroid categories and family history of thyroid disease,” Clinical endocrinology, vol. 88, no. 3, pp. 468–472, 2018.
  * [18] G. Kline and H. Sadrzadeh, “Chapter $2$ \- thyroid disorders,” in Endocrine Biomarkers (H. Sadrzadeh and G. Kline, eds.), pp. 41–93, Elsevier, 2017.
  * [19] R. G, B. C, R. B, R. A, and M. Bienvenu-Perrard, “The thyroid imaging reporting and data system (tirads) for ultrasound of the thyroid,” Journal de radiologie, vol. 92, pp. 701–713, 2011.
  * [20] A. T. Association, “Thyroid imaging reporting and data systems (tirads) accurately determine the risk of cancer in small thyroid nodules,” Clinical Thyroidology for the Public, vol. 11, pp. 7–8, 2018.
  * [21] R. Stewart, Y. J. Leang, C. R. Bhatt, S. Grodski, J. Serpell, and J. C.Lee, “Quantifying the differences in surgical management of patients with definitive and indeterminate thyroid nodule cytology,” European Journal of Surgical Oncology, vol. 46, pp. 252–257, 2020.
  * [22] Z. W. Baloch, S. Fleisher, V. A. LiVolsi, and P. K. Gupta, “Diagnosis of “follicular neoplasm”: a gray zone in thyroid fine-needle aspiration cytology,” Diagnostic cytopathology, vol. 26, no. 1, pp. 41–44, 2002.
  * [23] F. sheng Ouyang, B. liang Guo, L. zhu Ouyang, Z. wei Liu, S. jia Lin, W. Meng, X. yi Huang, H. xiong Chen, Q. gen Hu, and S. ming Yang, “Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules,” European Journal of Radiology, vol. 113, pp. 251–257, 2019.
  * [24] D. S. Dean and H. Gharib, “Fine-needle aspiration biopsy of the thyroid gland,” Endotext [Internet], 2015.
  * [25] J. P. Walsh, “Managing thyroid disease in general practice,” Medical Journal of Australia, vol. 205, no. 4, pp. 179–184, 2016.
  * [26] S. Jegerlehner, J.-L. Bulliard, D. Aujesky, N. Rodondi, S. Germann, I. Konzelmann, A. Chiolero, and N. W. Group, “Overdiagnosis and overtreatment of thyroid cancer: A population-based temporal trend study,” PloS one, vol. 12, no. 6, 2017.
  * [27] E. K. Alexander and P. R. Larsen, “High dose 131i therapy for the treatment of hyperthyroidism caused by graves’ disease,” The Journal of Clinical Endocrinology & Metabolism, vol. 87, pp. 1073–1077, 03 2002.
  * [28] B. Schmidbauer, K. Menhart, D. Hellwig, and J. Grosse, “Differentiated thyroid cancer—treatment: State of the art,” International Journal of Molecular Sciences, vol. 18, no. 6, 2017.
  * [29] H. N. Lee, J. Y. An, K. M. Lee, E. J. Kim, W. S. Choi, and D. Y. Kim, “Salivary gland dysfunction after radioactive iodine (i-131) therapy in patients following total thyroidectomy: emphasis on radioactive iodine therapy dose,” Clinical Imaging, vol. 39, no. 3, pp. 396–400, 2015.
  * [30] M. Melissa, C.S., “Thyroid cancer symptoms, signs, treatment, types & survival rate,” 2020.
  * [31] E. L. Mazzaferri and S. M. Jhiang, “Long-term impact of initial surgical and medical therapy on papillary and follicular thyroid cancer,” The American Journal of Medicine, vol. 97, no. 5, pp. 418–428, 1994.
  * [32] W. Simpson, S. McKinney, J. Carruthers, M. Gospodarowicz, S. Sutcliffe, and T. Panzarella, “Papillary and follicular thyroid cancer: Prognostic factors in 1,578 patients,” The American Journal of Medicine, vol. 83, no. 3, pp. 479–488, 1987.
  * [33] F. Borson-Chazot, S. Causeret, J.-C. Lifante, M. Augros, N. Berger, and J.-L. Peix, “Predictive factors for recurrence from a series of 74 children and adolescents with differentiated thyroid cancer,” World journal of surgery, vol. 28, no. 11, pp. 1088–1092, 2004.
  * [34] M. R. Pelizzo, I. M. Boschin, A. Toniato, A. Piotto, C. Pagetta, M. D. Gross, A. F. Al-Nahhas, and D. Rubello, “Papillary thyroid carcinoma: 35-year outcome and prognostic factors in 1858 patients,” Clinical nuclear medicine, vol. 32, no. 6, pp. 440–444, 2007.
  * [35] R. H. Grogan, S. P. Kaplan, H. Cao, R. E. Weiss, L. J. DeGroot, C. A. Simon, O. M. Embia, P. Angelos, E. L. Kaplan, and R. B. Schechter, “A study of recurrence and death from papillary thyroid cancer with 27 years of median follow-up,” Surgery, vol. 154, no. 6, pp. 1436–1447, 2013.
  * [36] S. jin Kim, S. Y. Park, Y. J. Lee, E. K. Lee, S. ki Kim, T. H. Kim, Y.-S. Jung, J. Ryu, J. P. Myong, and K.-W. Chung, “Risk factors for recurrence after therapeutic lateral neck dissection for primary papillary thyroid cancer,” Annals of surgical oncology, vol. 21, no. 6, pp. 1884–1890, 2014.
  * [37] M. Mourad, S. Moubayed, A. Dezube, Y. Mourad, K. Park, A. Torreblanca-Zanca, J. S. Torrecilla, J. C. Cancilla, and J. Wang, “Machine learning and feature selection applied to seer data to reliably assess thyroid cancer prognosis,” Scientific Reports, vol. 10, no. 1, pp. 1–11, 2020.
  * [38] T. S. Wang, K. Cheung, F. Farrokhyar, S. A. Roman, and J. A. Sosa, “A meta-analysis of the effect of prophylactic central compartment neck dissection on locoregional recurrence rates in patients with papillary thyroid cancer,” Annals of surgical oncology, vol. 20, no. 11, pp. 3477–3483, 2013.
  * [39] T. Zetoune, X. Keutgen, D. Buitrago, H. Aldailami, H. Shao, M. Mazumdar, T. J. F. III, and R. Zarnegar, “Prophylactic central neck dissection and local recurrence in papillary thyroid cancer: a meta-analysis,” Annals of surgical oncology, vol. 17, no. 12, pp. 3287–3293, 2010.
  * [40] D. Van Nostrand, “The benefits and risks of i-131 therapy in patients with well-differentiated thyroid cancer,” Thyroid, vol. 19, no. 12, pp. 1381–1391, 2009.
  * [41] I. Kononenko, “Machine learning for medical diagnosis: history, state of the art and perspective,” Artificial Intelligence in Medicine, vol. 23, no. 1, pp. 89–109, 2001.
  * [42] A. Bikas and K. D. Burman, “Epidemiology of thyroid cancer,” The thyroid and its diseases, pp. 541–547, 2019.
  * [43] K. Hińcza, A. Kowalik, and A. Kowalska, “Current knowledge of germline genetic risk factors for the development of non-medullary thyroid cancer,” Genes, vol. 10, no. 7, 2019.
  * [44] Y. Yeo, S.-H. Ma, Y. Hwang, P. L. Horn-Ross, A. Hsing, K.-E. Lee, Y. J. Park, D.-J. Park, K.-Y. Yoo, and S. K. Park, “Diabetes mellitus and risk of thyroid cancer: A meta-analysis,” PLOS ONE, vol. 9, pp. 1–11, 06 2014\.
  * [45] D. Gąsior-Perczak, I. Pałyga, M. Szymonek, A. Kowalik, A. Walczyk, J. Kopczyński, K. Lizis-Kolus, T. Trybek, E. Mikina, D. Szyska-Skrobot, K. Gadawska-Juszczyk, S. Hurej, A. Szczodry, A. Słuszniak, J. Słuszniak, R. Mężyk, S. Góźdź, and A. Kowalska, “The impact of bmi on clinical progress, response to treatment, and disease course in patients with differentiated thyroid cancer,” PLOS ONE, vol. 13, pp. 1–18, 10 2018.
  * [46] L. Barrea, M. Gallo, R. M. Ruggeri, P. D. Giacinto, F. Sesti, N. Prinzi, V. Adinolfi, V. Barucca, V. Renzelli, G. Muscogiuri, A. Colao, R. Baldelli, and on behalf of E.O.L.O. Group, “Nutritional status and follicular-derived thyroid cancer: An update,” Critical Reviews in Food Science and Nutrition, vol. 61, no. 1, pp. 25–59, 2021.
  * [47] L. Huang, X. Feng, W. Yang, X. Li, K. Zhang, S. Feng, F. Wang, and X. Yang, “Appraising the effect of potential risk factors on thyroid cancer: A mendelian randomization study,” The Journal of Clinical Endocrinology & Metabolism, 04 2022.
  * [48] M. Kaushik, R. Sharma, S. A. Peious, M. Shahin, S. B. Yahia, and D. Draheim, “A systematic assessment of numerical association rule mining methods,” SN Computer Science, vol. 2, no. 5, pp. 1–13, 2021.
  * [49] C.-H. Wang, T.-Y. Lee, K.-C. Hui, and M.-H. Chung, “Mental disorders and medical comorbidities: Association rule mining approach,” Perspectives in Psychiatric Care, vol. 55, no. 3, pp. 517–526, 2019.
  * [50] S. Cha and S.-S. Kim, “Comorbidity patterns of mood disorders in adult inpatients: Applying association rule mining,” Healthcare, vol. 9, no. 9, 2021.
  * [51] M. Tandan, Y. Acharya, S. Pokharel, and M. Timilsina, “Discovering symptom patterns of covid-19 patients using association rule mining,” Computers in Biology and Medicine, vol. 131, p. 104249, 2021.
  * [52] S. J. Lee and K. B. Cartmell, “An association rule mining analysis of lifestyle behavioral risk factors in cancer survivors with high cardiovascular disease risk,” Journal of Personalized Medicine, vol. 11, no. 5, 2021.
  * [53] A. L. Buczak and C. M. Gifford, “Fuzzy association rule mining for community crime pattern discovery,” in ACM SIGKDD Workshop on Intelligence and Security Informatics, ISI-KDD ’10, Association for Computing Machinery, 2010\.
  * [54] D. Li, D. Yang, and J. Zhang, “Arb: Knowledge discovery and disease diagnosis on thyroid disease diagnosis integrating association rule with bagging algorithm,” Engineering Letters, vol. 28, no. 2, 2020.
  * [55] D. Li, D. Yang, J. Zhang, and X. Zhang, “Ar-ann: Incorporating association rule mining in artificial neural network for thyroid disease knowledge discovery and diagnosis,” IAENG International Journal of Computer Science, vol. 47, no. 1, pp. 25–36, 2020.
  * [56] R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proc. 20th int. conf. very large data bases (A. K. Laha, ed.), vol. 1215, pp. 487–499, VLDB, 1994.
  * [57] D. Taniar, W. Rahayu, V. Lee, and O. Daly, “Exception rules in association rule mining,” Applied Mathematics and Computation, vol. 205, no. 2, pp. 735–750, 2008.  Special Issue on Advanced Intelligent Computing Theory and Methodology in Applied Mathematics and Computation.
  * [58] T. Scheffer, “Finding association rules that trade support optimally against confidence,” in 5th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 424–435, Springer, 2001.
  * [59] M. Zhai, D. Zhang, J. Long, Y. Gong, F. Ye, S. Liu, and Y. Li, “The global burden of thyroid cancer and its attributable risk factor in 195 countries and territories: A systematic analysis for the global burden of disease study,” Cancer Medicine, vol. 10, no. 13, pp. 4542–4554, 2021.
  * [60] M. Mileva1, B. Stoilovska, A. Jovanovska, A. Ugrinska, G. Petrushevska, S. Kostadinova-Kunovska, D. Miladinova, and V. Majstorov, “Thyroid cancer detection rate and associated risk factors in patients with thyroid nodules classified as bethesda category iii,” Radiology and oncology, vol. 52, no. 4, p. 370, 2018.
  * [61] G. Azizi and C. D. Malchoff, “Autoimmune thyroid disease: A risk factor for thyroid cancer,” Endocrine Practice, vol. 17, no. 2, pp. 201–209, 2011\.
  * [62] A. Papanastasiou, K. Sapalidis, D. G. Goulis, N. Michalopoulos, E. Mareti, S. Mantalovas, and I. Kesisoglou, “Thyroid nodules as a risk factor for thyroid cancer in patients with graves’ disease: A systematic review and meta-analysis of observational studies in surgically treated patients,” Clinical Endocrinology, vol. 91, no. 4, pp. 571–577, 2019.
  * [63] H. Sadeghi, M. Rafei, M. Bahrami, A. Haghdoost, and Y. Shabani, “Attributable risk fraction of four lifestyle risk factors of thyroid cancer: a meta-analysis,” Journal of Public Health, vol. 40, pp. e91–e98, 08 2017\.
  * [64] A. Matrone, F. Ferrari, F. Santini, and R. Elisei, “Obesity as a risk factor for thyroid cancer,” Current Opinion in Endocrinology, Diabetes and Obesity, vol. 27, no. 5, pp. 358–363, 2020.
  * [65] C. M. Kitahara and J. A. Sosa, “Understanding the ever-changing incidence of thyroid cancer,” Nature Reviews Endocrinology, vol. 16, no. 11, pp. 617–618, 2020.
  * [66] M. B. Zimmermann and V. Galetti, “Iodine intake as a risk factor for thyroid cancer: a comprehensive review of animal and human studies,” Thyroid research, vol. 8, no. 1, pp. 1–21, 2015.
  * [67] C. W. Jin and K. Jeongseon, “Dietary factors and the risk of thyroid cancer: A review,” Clinical Nutrition Research, vol. 3, no. 2, pp. 75–88, 2014.
  * [68] M. A. Marcello, L. L. Cunha, F. A. Batista, and L. S. Ward, “Obesity and thyroid cancer,” Endocrine-related cancer, vol. 21, no. 5, pp. T255–T271, 2014.
  * [69] A. Kyriacou, V. Tziaferi, and M. Toumba, “Stress, thyroid dysregulation and thyroid cancer in children and adolescents: Proposed impending mechanisms,” Hormone Research in Paediatrics, 2022.
  * [70] V. Zivaljevic, N. Slijepcevic, S. Sipetic, I. Paunovic, A. Diklic, G. Zoric, and N. Kalezic, “Risk factors for well-differentiated thyroid cancer in men,” Tumori Journal, vol. 99, no. 4, pp. 458–462, 2013.
  * [71] F. Asif, M. R. Ahmad, and A. Majid, “Risk factors for thyroid cancer in females using a logit model in lahore, pakistan,” Asian Pacific Journal of Cancer Prevention, vol. 16, no. 15, pp. 6243–6247, 2015.
  * [72] K. J. Stansifer, J. F. Guynan, B. M. Wachal, and R. B. Smith, “Modifiable risk factors and thyroid cancer,” Otolaryngology–Head and Neck Surgery, vol. 152, no. 3, pp. 432–437, 2015.
  * [73] L.-Z. Cao, X.-D. Peng, J.-P. Xie, F.-H. Yang, H.-L. Wen, and S. Li, “The relationship between iodine intake and the risk of thyroid cancer: a meta-analysis,” Medicine, vol. 96, no. 20, 2017.
  * [74] C.-H. Tseng, “Thyroid cancer risk is not increased in diabetic patients,” PLOS ONE, vol. 7, no. 12, 2012.
  * [75] C. L. Meinhold, E. Ron, S. J. Schonfeld, B. H. Alexander, D. M. Freedman, M. S. Linet, and A. Berrington de González, “Nonradiation risk factors for thyroid cancer in the us radiologic technologists study,” American Journal of Epidemiology, vol. 171, pp. 242–252, 11 2009.
  * [76] Y. Lun, X. Wu, Q. Xia, Y. Han, X. Zhang, Z. Liu, F. Wang, Z. Duan, S. Xin, and J. Zhang, “Hashimoto’s thyroiditis as a risk factor of papillary thyroid cancer may improve cancer prognosis,” Otolaryngology–Head and Neck Surgery, vol. 148, no. 3, pp. 396–402, 2013.
  * [77] C. Xhaard, Y. Ren, E. Clero, S. Maillard, P. Brindel, F. Rachedi, F. Bost-Bezeaud, F. Doyon, C. Rubino, and F. de Vathaire, “Differentiated thyroid carcinoma risk factors in french polynesia,” Asian Pacific journal of cancer prevention, vol. 15, no. 6, pp. 2675–2680, 2014.
  * [78] Y. Cao, Z. Wang, J. Gu, F. Hu, Y. Qi, Q. Yin, Q. Sun, G. Li, and B. Quan, “Reproductive factors but not hormonal factors associated with thyroid cancer risk: a systematic review and meta-analysis,” BioMed Research International, vol. 2015, 2015.
  * [79] C. Xhaard, F. de Vathaire, E. Cléro, S. Maillard, Y. Ren, F. Borson-Chazot, G. Sassolas, C. Schvartz, M. Colonna, B. Lacour, A. Danzon, M. Velten, E. Marrer, L. Bailly, E. M. Barjoan, M. Schlumberger, J. Orgiazzi, E. Adjadj, and C. Rubino, “Anthropometric risk factors for differentiated thyroid cancer in young men and women from eastern france: A case-control study,” American Journal of Epidemiology, vol. 182, pp. 202–214, 06 2015.
  * [80] P. Wang, L. Lv, F. Qi, and F. Qiu, “Increased risk of papillary thyroid cancer related to hormonal factors in women,” Tumor Biology, vol. 36, no. 7, pp. 5127–5132, 2015.
  * [81] J. Luo, M. Hendryx, J. E. Manson, X. Liang, and K. L. Margolis, “Hysterectomy, oophorectomy, and risk of thyroid cancer,” The Journal of Clinical Endocrinology & Metabolism, vol. 101, pp. 3812–3819, 07 2016.
  * [82] F. Khodamoradi, M. Ghoncheh, A. Mehri, S. Hassanipour, and H. Salehiniya, “Incidence, mortality, and risk factors of thyroid cancer in the world: a review,” World Cancer Research Journal, vol. 5, no. 2, 2018.
  * [83] K. Y., M. Gh., S. P., and G. Sh., “Assessment of thyroid cancer risk factors in kurdistan province,” Scientific Journal of Kurdistan University of Medical Sciences, vol. 23, no. 3, pp. 10–18, 2018.
  * [84] M. Mileva, B. Stoilovska, A. Jovanovska, A. Ugrinska, G. Petrushevska, S. Kostadinova-Kunovska, D. Miladinova, and V. Majstorov, “Thyroid cancer detection rate and associated risk factors in patients with thyroid nodules classified as bethesda category iii,” Radiology and oncology, vol. 52, no. 4, p. 370, 2018.
  * [85] A. S. Mannathazhathu, P. S. George, S. Sudhakaran, D. Vasudevan, J. K. KM, C. Booth, and A. Mathew, “Reproductive factors and thyroid cancer risk: Meta-analysis,” Head & neck, vol. 41, no. 12, pp. 4199–4208, 2019.
  * [86] Y. Nagayama, “Thyroid autoimmunity and thyroid cancer–the pathogenic connection: a 2018 update,” Hormone and Metabolic Research, vol. 50, no. 12, pp. 922–931, 2018.
  * [87] J.-H. Park, M. Choi, J.-H. Kim, J. Kim, K. Han, B. Kim, D.-H. Kim, and Y.-G. Park, “Metabolic syndrome and the risk of thyroid cancer: A nationwide population-based cohort study,” Thyroid, vol. 30, no. 10, pp. 1496–1504, 2020.
  * [88] J. M. Han, T. Y. Kim, M. J. Jeon, J. H. Yim, W. G. Kim, D. E. Song, S. J. Hong, S. J. Bae, H.-K. Kim, M.-H. Shin, Y. K. Shong, and W. B. Kim, “Obesity is a risk factor for thyroid cancer in a large, ultrasonographically screened population,” Eur J Endocrinol, vol. 168, no. 6, pp. 879–886, 2013.
  * [89] S. Zhao, X. Jia, X. Fan, L. Zhao, P. Pang, Y. Wang, Y. Luo, F. Wang, G. Yang, X. Wang, W. Gu, L. Zang, Y. Pei, J. Du, J. Ba, J. Dou, Y. Mu, and Z. Lyu, “Association of obesity with the clinicopathological features of thyroid cancer in a large, operative population: a retrospective case-control study,” Medicine, vol. 98, no. 50, p. e18213, 2019.
  * [90] S. I. Noureldine and R. P. Tufano, “Association of hashimoto’s thyroiditis and thyroid cancer,” Current Opinion in Oncology, vol. 27, no. 1, pp. 21–25, 2015.
  * [91] C. M. Kitahara, D. Kormendiné Farkas, J. O. L. Jørgensen, D. Cronin-Fenton, and H. T. Sørensen, “Benign thyroid diseases and risk of thyroid cancer: A nationwide cohort study,” The Journal of Clinical Endocrinology & Metabolism, vol. 103, pp. 2216–2224, 03 2018.
  * [92] X. Shen, G. Zhu, R. Liu, D. Viola, R. Elisei, E. Puxeddu, L. Fugazzola, C. Colombo, B. Jarzab, A. Czarniecka, A. K. Lam, C. Mian, F. Vianello, L. Yip, G. Riesco-Eizaguirre, P. Santisteban, C. J. O’Neill, M. S. Sywak, R. Clifton-Bligh, B. Bendlova, V. Sýkorová, and M. Xingcorresponding, “Patient age–associated mortality risk is differentiated by braf v600e status in papillary thyroid cancer,” Journal of Clinical Oncology, vol. 36, no. 5, p. 438, 2018.
  * [93] G. Pellegriti, F. Frasca, C. Regalbuto, S. Squatrito, and R. Vigneri, “Worldwide increasing incidence of thyroid cancer: update on epidemiology and risk factors,” Journal of cancer epidemiology, 2013.
  * [94] B. Aschebrook-Kilfoy, M. H. Ward, C. T. D. Valle, and M. C. Friesen, “Occupation and thyroid cancer,” Occupational and environmental medicine, vol. 71, no. 5, 2014.
  * [95] Y. Liu, L. Su, and H. Xiao, “Review of factors related to the thyroid cancer epidemic,” International journal of endocrinology, vol. 2017, 2017.
  * [96] A. A. Efanov, A. V. Brenner, T. I. Bogdanova, L. M. Kelly, P. Liu, M. P. Little, A. I. Wald, M. Hatch, L. Y. Zurnadzy, M. N. Nikiforova, V. Drozdovitch, R. Leeman-Neill, K. Mabuchi, M. D. Tronko, S. J. Chanock, and Y. E. Nikiforov, “Investigation of the relationship between radiation dose and gene mutations and fusions in post-chernobyl thyroid cancer,” JNCI: Journal of the National Cancer Institute, vol. 110, pp. 371–378, 11 2017.
  * [97] M. A. Han and J. H. Kim, “Diagnostic x-ray exposure and thyroid cancer risk: Systematic review and meta-analysis,” Thyroid, vol. 28, no. 2, pp. 220–228, 2018.
  * [98] T. Pappa and M. Alevizaki, “Obesity and thyroid cancer: A clinical update,” Thyroid, vol. 24, no. 2, pp. 190–199, 2014.
  * [99] H. Kwon, Y. Chang, A. Cho, J. Ahn, S. E. Park, C.-Y. Park, W.-Y. Lee, K.-W. Oh, S.-W. Park, H. Shin, S. Ryu, and E.-J. Rhee, “Metabolic obesity phenotypes and thyroid cancer risk: A cohort study,” Thyroid, vol. 29, no. 3, pp. 349–358, 2019.
  * [100] S.-Y. An, S. Y. Kim, D. J. Oh, C. Min, S. Sim, and H. G. Choi, “Obesity is positively related and tobacco smoking and alcohol consumption are negatively related to an increased risk of thyroid cancer,” Scientific reports, vol. 10, no. 1, pp. 1–9, 2020.
  * [101] J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” SIGMOD Rec., vol. 29, p. 1–12, may 2000.
  * [102] P. Umasankar and V. Thiagarasu, “Decision support system for heart disease diagnosis using interval vague set and fuzzy association rule mining,” in 2018 4th International Conference on Devices, Circuits and Systems (ICDCS), pp. 223–227, 2018.
  * [103] F. Tao, F. Murtagh, and M. Farid, “Weighted association rule mining using weighted support and significance framework,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (New York, NY, USA), p. 661–666, Association for Computing Machinery, 2003.
  * [104] K. S. Lakshmi and G. Vadivu, “A novel approach for disease comorbidity prediction using weighted association rule mining,” Journal of Ambient Intelligence and Humanized Computing, vol. 9, pp. 1–8, 2019.
  * [105] A. Yavari, A. Rajabzadeh, and F. Abdali-Mohammadi, “Profile-based assessment of diseases affective factors using fuzzy association rule mining approach: A case study in heart diseases,” Journal of Biomedical Informatics, vol. 116, p. 103695, 2021.
  * [106] V. Anand and D. Koundal, “Computer-assisted diagnosis of thyroid cancer using medical images: A survey,” in Proceedings of ICRIC 2019, pp. 543–559, Springer, 2020.
  * [107] W.-K. Chan, J.-H. Sun, M.-J. Liou, Y.-R. Li, W.-Y. Chou, F.-H. Liu, S.-T. Chen, and S.-J. Peng, “Using deep convolutional neural networks for enhanced ultrasonographic image diagnosis of differentiated thyroid cancer,” Biomedicines, vol. 9, no. 12, 2021.
  * [108] J. Ma, F. Wu, T. Jiang, Q. Zhao, and D. Kong, “Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks,” International journal of computer assisted radiology and surgery, vol. 12, no. 11, pp. 1895–1910, 2017.
  * [109] B. Chouiha and A. Amamra, “Thyroid nodules recognition in ultrasound images based on imagenet top-performing deep convolutional neural networks,” in International Conference on Computing Systems and Applications, pp. 313–322, Springer, 2020.
  * [110] Z. Zhao, C. Yang, Q. Wang, H. Zhang, L. Shi, and Z. Zhang, “A deep learning-based method for detecting and classifying the ultrasound images of suspicious thyroid nodules,” Medical Physics, vol. 48, no. 12, pp. 7959–7970, 2021.
  * [111] O. A. Adebisi, J. A. Ojo, and T. O. Bello, “Computer aided diagnosis system for classification of abnormalities in thyroid nodules ultrasound images using deep learning,” Journal of Computer Engineering, vol. 22, pp. 60–66, 2020.
  * [112] X. Shen, X. Ouyang, T. Liu, and D. Shen, “Cascaded networks for thyroid nodule diagnosis from ultrasound images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 145–154, Springer, 2020.
  * [113] H. A. Nugroho, Zulfanahri, E. L. Frannita, I. Ardiyanto, and L. Choridah, “Computer aided diagnosis for thyroid cancer system based on internal and external characteristics,” Journal of King Saud University - Computer and Information Sciences, vol. 33, no. 3, pp. 329–339, 2021.
  * [114] V. V. Vadhiraj, A. Simpkin, J. O’Connell, N. Singh Ospina, S. Maraka, and D. T. O’Keeffe, “Ultrasound image classification of thyroid nodules using machine learning techniques,” Medicina, vol. 57, no. 6, 2021.
  * [115] J. Chi, E. Walia, P. Babyn, J. Wang, G. Groot, and M. Eramian, “Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network,” Journal of digital imaging, vol. 30, no. 4, pp. 477–486, 2017\.
  * [116] N. S. Narayan, P. Marziliano, and C. G. Hobbs, “Automatic removal of manually induced artefacts in ultrasound images of thyroid gland,” in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3399–3402, IEEE, 2013.
  * [117] H. Mugasa, S. Dua, J. E. Koh, Y. Hagiwara, O. S. Lih, C. Madla, P. Kongmebhol, K. H. Ng, and U. R. Acharya, “An adaptive feature extraction model for classification of thyroid lesions in ultrasound images,” Pattern Recognition Letters, vol. 131, pp. 463–473, 2020.
  * [118] H. A. Nugroho, E. L. Frannita, and A. H. T. Hutami, “Thyroid nodules categorization based on margin features using deep learning,” in 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 499–504, 2020.
  * [119] X. Zhang, V. C. Lee, J. Rong, J. C. Lee, and F. Liu, “Deep convolutional neural networks in thyroid disease detection: A multi-classification comparison by ultrasonography and computed tomography,” Computer Methods and Programs in Biomedicine, vol. 220, p. 106823, 2022.