# AI-Enabled Lung Cancer Prognosis ††thanks:  This is the author’s version of
a book chapter entitled: "Cancer Research: An Interdisciplinary Approach",
Springer.
M. Darvish and L. Ren are with the University of Maryland School of Medicine,
Baltimore, MD 21201.
R. Trask, P. Tallon, M. Khansari, and B. Yousefi are with the University of
Maryland, College Park, MD 20742.
M. Hershman is with the University of Pennsylvania and Boise Radiology Group,
Philadelphia, PA 19104.
**Corresponding author:
B. Yousefi, BSE-4117, 9636 Gudelsky Drive, Rockville, MD 20850, Tel:
240-665-6529, Email<EMAIL_ADDRESS>

Mahtab Darvish, Ryan Trask, Patrick Tallon, Mélina Khansari,
Lei Ren, Michelle Hershman, and Bardia Yousefi

###### Abstract

Lung cancer is the primary cause of cancer-related mortality, claiming
approximately 1.79 million lives globally in 2020, with an estimated 2.21
million new cases diagnosed within the same period. Among these, Non-Small
Cell Lung Cancer (NSCLC) is the predominant subtype, characterized by a
notably bleak prognosis and low overall survival rate of approximately 25$\%$
over five years across all disease stages. However, survival outcomes vary
considerably based on the stage at diagnosis and the therapeutic interventions
administered. Recent advancements in artificial intelligence (AI) have
revolutionized the landscape of lung cancer prognosis. AI-driven
methodologies, including machine learning and deep learning algorithms, have
shown promise in enhancing survival prediction accuracy by efficiently
analyzing complex multi-omics data and integrating diverse clinical variables.
By leveraging AI techniques, clinicians can harness comprehensive prognostic
insights to tailor personalized treatment strategies, ultimately improving
patient outcomes in NSCLC. Overviewing AI-driven data processing can
significantly help bolster the understanding and provide better directions for
using such systems.

_K_ eywords Lung cancer prognosis $\cdot$ Artificial intelligence in
healthcare $\cdot$ High dimensional data analysis $\cdot$ Deep learning for
cancer prognosis $\cdot$ Radiomics $\cdot$ Multiomics.

##  1 Introduction

NSCLC (non-small cell lung cancer) is the most common type of lung cancer,
accounting for 84$\%$ of lung cancer diagnoses [1], and number of cases are
trended to increase globally [2]. Radiomics extracted from various imaging
modalities, omics, and pathological data have been found to possess
significant predictive power for identifying survival responses in lung cancer
patients when analyzed using various machine-learning models. CT (computed
tomography) and Positron emission tomography (PET) imaging are the most common
imaging modalities used for noninvasive treatment planning. Radiomics
extracted from these imaging systems significantly aid AI (artificial
intelligence) methods to excel in the computer-aided prognosis of cancer and
help treatment planning.

Lung cancer manifests mesoscopic-scale phenotypic traits often imperceptible
to the human eye. However, these traits can be non-invasively captured through
radiomic features in medical imaging, forming a high-dimensional (HD) data
space conducive to machine learning. Leveraging AI, radiomic features enable
effective patient risk stratification, prediction of histological and
molecular outcomes, and assessment of clinical measures, thus advancing
precision medicine in lung cancer care. In contrast to tissue sampling-centric
methods, radiomics-based approaches offer advantages in terms of non-
invasiveness, reproducibility, cost-effectiveness, and reduced vulnerability
to intra-tumoral heterogeneity. Despite the grand ability of such data to
encode NSCLC’s characteristics, the large number of covariates can hinder
modeling due to overfitting affecting the generalizability of models [3]. To
design such models, there should be great investigation toward all the
components involved in data collection, such as biological factors, imaging,
pathology, and the AI methodology to tackle HD data processing (Figure 1).
This chapter overviews the integration of AI deciphering HD data and their
effects on the prognosis of NSCLC and the AI methodologies used to tackle such
a problem.

##  2 HD gene expression for prognosis of NSCLC

While traditional staging systems provide valuable clinical information, they
often fall short of capturing the unique molecular portrait of each tumor.
Harnessing the HD gene expression has emerged as a crucial avenue for
unraveling prognostic insights. Exploring the profound implications of genetic
information in shaping the prognosis of NSCLC patients through the patterns
encoded in the genetic data, researchers strive to unveil novel markers and
comprehensive signatures that hold the promise of refining prognostic
predictions and advancing personalized therapeutic strategies for such
formidable diseases. Enter gene expression profiling (GEP), a ground-breaking
technology empowering personalized care by decoding the whispers within cancer
cells themselves [4].

Traditional NSCLC staging, relying on tumor size, lymph node involvement, and
metastasis, paints a limited picture. It neglects the intricate molecular
choreography within tumor cells that dictates individual patient trajectories
[5]. GEP steps in, analyzing the activity of thousands of genes within a tumor
sample. By illuminating distinct patterns associated with aggressiveness,
response to therapy, and ultimately, survival, it offers a deeper
understanding of individual tumor behavior [6].

Landmark studies have demonstrated the superiority of GEP signatures compared
to traditional staging. In a groundbreaking publication, Beer et al.
identified a 50-gene signature that independently predicted survival in early-
stage NSCLC patients, even after accounting for the stage. This work showcased
the ability of GEP to uncover hidden prognostic information missed by
conventional means, offering hope for more personalized predictions [7].
Similarly, Kratz et al. demonstrated that the incorporation of the molecular
prognostic classifier, which integrates the prognostic expression levels of 11
cancer-related target genes, significantly improves risk stratification and
survival predictions of NSCLC compared to the conventional staging systems.
This study emphasizes the importance of incorporating molecular descriptors of
tumor biology into staging systems to achieve more accurate risk
stratification and prognosis [8] Välk et al. conducted a screening of 81 NSCLC
samples utilizing whole-genome gene expression microarrays to identify
differentially expressed genes and novel NSCLC biomarkers. Their analysis
revealed upregulated expression of novel genes in NSCLC, including SPAG5,
POLH, KIF23, and RAD54L, associated with mitotic spindle formation, DNA
repair, chromosome segregation, and dsDNA break repair, respectively.

Figure 1: The AI-based NSCLC therapy pipeline shows the major HD data
integration involving imaging biomarkers, omics, and clinical information.
Imaging biomarkers can be integrated with other data, omics, and clinicals, to
improve precision medicine.

In addition, they identified several downregulated genes in NSCLC, such as
SGCG, NLRC4, MMRN1, and SFTPD, involved in extracellular matrix formation,
apoptosis, blood vessel leakage, and inflammation, respectively. A significant
correlation was observed between RNA degradation and survival in
adenocarcinoma cases. The study demonstrated improved prediction values in a
group selection based on molecular profiles compared to histology [9]. In
another study, Bhattacharjee et al. developed a molecular classification for
lung carcinoma and analyzed mRNA expression in 186 lung tumor samples,
including 139 adenocarcinomas. The analysis revealed distinct subclasses of
lung adenocarcinoma, including those with high expression of neuroendocrine
genes and type II pneumocyte genes. Adenocarcinomas with neuroendocrine gene
expression had poorer outcomes. This classification method could differentiate
primary lung adenocarcinomas from metastases of extra-pulmonary origin,
suggesting its potential for aiding in lung cancer diagnosis [10].
Additionally, Guo and colleagues devised a computational model to predict
individual patient outcomes based on gene expression profiles in lung
adenocarcinoma. They identified a 37-gene signature through advanced
bioinformatics, evaluating its prognostic power using hierarchical clustering
and Kaplan-Meier analysis. Applying the signature to 84 patients yielded a
96$\%$ predictive accuracy, categorizing patients into groups with varying
prognoses. Notably, patients with stage I disease predominantly fell into the
group with a good prognosis. These findings suggest the potential for
personalized therapy guided by prediction models based on a small set of
marker genes [11] These examples highlight the potential of GEP to move beyond
traditional limitations and guide better clinical decision-making.

The power of GEP extends beyond mere survival prediction. Identifying specific
gene expression patterns associated with aggressive behavior or response to
different treatment options paves the way for personalized prognostication and
treatment decisions. For instance, Kikuchi and colleagues conducted innovative
studies aimed at correlating a patient’s gene expression profile with the most
effective chemotherapy regimen. They analyzed 37 NSCLCs using cDNA microarray
analysis and compared gene expression data with NSCLC sensitivity to six anti-
cancer drugs. Their findings revealed significant associations between gene
expression levels and chemosensitivity. This suggests the potential for
identifying predictive markers for chemotherapy agents [12] Additionally,
Petty and colleagues examined the genetic profile of NSCLC patients to predict
their response to platinum-based chemotherapy. They identified 17 genes
associated with treatment responsiveness, particularly highlighting lysosomal
protease inhibitors like serpinB3 and cystatin C. Applying their findings to
predict the response of additional patients, they achieved 72The field of GEP-
based prognosis is constantly evolving. Liquid biopsies, analyzing tumor DNA
shed into blood, offer a minimally invasive approach to capturing gene
expression information, overcoming the limitations of tissue biopsies [14].
Imamura et al. (2020) evaluate the efficacy of monitoring circulating tumor
DNA (ctDNA) in EGFR-mutant non-small-cell lung cancer patients treated with
epidermal growth factor receptor tyrosine kinase inhibitors. Researchers
analyzed serial plasma samples from 52 patients, observing dynamic changes in
plasma mutation scores during treatment. The study highlights the usefulness
of ctDNA monitoring in assessing treatment responses and driver oncogene
status, revealing clonal heterogeneity and the genetic evolution of cancer at
an individual level [15]. In a separate study, Pender et al. illustrated the
application of droplet digital PCR (ddPCR) in identifying low-frequency
mutations in lung cancer, with a specific focus on KRAS mutations commonly
found in NSCLC. Their research involved testing on KRAS mutant cell lines as
well as DNA obtained from tumor tissue of lung cancer patients, revealing a
notable level of sensitivity [16]. These studies show promising results using
liquid biopsies to monitor disease progression and response to therapy in
NSCLC patients, potentially paving the way for more personalized treatment
monitoring and adjustments.

Furthermore, exploring the interplay between GEP and other factors like the
immune system or the tumor microenvironment could unlock new avenues for
prognostication and therapeutic targeting. Studies like the one conducted by
Ye et al. suggest that understanding the interaction between GEP and the
immune system could aid in predicting response to newer immunotherapy
treatments, offering a glimpse into the future of personalized cancer care
[17].

Integrating GEP with other prognostic tools like imaging techniques could
further refine risk stratification and treatment decision-making. For example,
Aerts et al showed that radiomics-based phenotyping could improve the
stratification and assessment of response to tyrosine kinase inhibitors (TKIs)
in NSCLC patients [18]. In a more recent study by Chen et al., researchers
investigated the correlation between radiological imaging, gene expression
patterns, and patient outcomes in NSCLC. They conducted an extensive analysis
of 116 NSCLC cases, utilizing CT images, gene expression data, and clinical
factors. Through meticulous analysis, radiomic and genomic features were
extracted and scrutinized, resulting in the creation of risk scores for each
patient pertaining to overall survival (OS). Notably, a fusion survival model
was developed, integrating CT images, gene expression data, and clinical
factors. This model effectively stratified patients into low- and high-risk
groups with notable accuracy, surpassing traditional unimodal data-based
approaches. The success of this fusion model underscores the potential of
integrating diverse datasets for more precise risk assessment and enhanced
patient care strategies in managing NSCLC [19].

##  3 Pathological Imaging

Pathological data serves as a critical cornerstone in understanding the
intricacies of NSCLC prognosis. By scrutinizing histopathological features and
molecular signatures, researchers aim to crack invaluable information into
disease progression, treatment response, and patient outcomes. This section
delves into the crucial role of pathological data in shaping prognostic models
for NSCLC, highlighting its potential to refine risk stratification and guide
tailored therapeutic interventions in clinical practice. Employing machine
learning on the multiple tissue microarray data for distinguishing short- and
long- term survival indicate the potential of automated image features in
prognostic predictions for lung cancer [20], which also suggests that
quantitative features derived from histopathology images can enhance survival
predictions and improving current practices. Such approaches hold promise for
precision oncology, offering insights applicable to organ pathologies.

The development and validation of a pathology image-based predictive model for
the prognosis of lung adenocarcinoma (ADC) patients across multiple
independent cohorts through the extraction of morphological features from
formalin-fixed paraffin-embedded tumor tissues indicated some potential for
widespread applicability in ADC prognosis prediction [22]. Imaging features
were also used for decoding pathological imaging features and their
interactions with the prediction response of NSCLC. For example, three
radiomic features related to primary tumor sphericity and lymph node
homogeneity were significantly predictive of pathological complete response
(pCR), while two features quantifying lymph node homogeneity were predictive
of gross residual disease (GRD). These features are used for assessing
pathological response after neoadjuvant chemoradiation in locally advanced
NSCLC [21]. The combination of radiomic and clinical data outperformed other
feature sets, suggesting the importance of lymph node phenotypic information
in predicting pathological response.

A CAD-based HD data processing for assessing PD-L1 expression, crucial for
anti-PD-1/PD-L1 treatment in NSCLC, showed a strong correlation with manual
pathologist scores, demonstrating high concordance across varying PD-L1
cutoffs and provided efficient and comparable PD-L1 assessment, supporting
treatment decisions and assay evaluations. [23]. A combination of data from
gene expressions and histopathological imaging features can provide insight
into NSCLC prognosis, which suggests that gene expressions exhibit slightly
better prognostic performance, and there’s a weak correlation between most
gene expressions and imaging features [24]. The study also emphasized the
importance of understanding the roles of molecular and imaging data in cancer
prognosis modeling, highlighting the need for further methodological
development and investigation into the interconnections between these data
types.

Models trained on H&E-stained Whole Slide Images (WSIs) of transbronchial lung
biopsy (TBLB) specimens may lead to high accuracy in classifying ADC, squamous
cell carcinoma (SCC), small-cell lung cancer (SCLC), and non-neoplastic cases
[25]. Studies of clinically relevant molecular phenotypes from whole-slide
histopathology images using human-interpretable image features (HIFs)
suggested better addressing the interpretability challenge in pathology
workflow [26]. These HIFs, correlating with known tumor microenvironment
markers, exhibit predictive capability for diverse molecular signatures,
demonstrating a comprehensive and interpretable insight into the spatial
architecture of the tumor microenvironment. Aiming to identify crucial
features for classifying subtypes, radiomics may lead to optimized classifier
and achieving reliable precision, which demonstrates the impressive potential
of radiomics in accurately classifying pathologically confirmed NSCLC
subtypes, with implications for treatment planning and precision medicine
[27].

##  4 HD Imaging Biomarkers

Radiomics involves translating digital medical images into quantitative data,
aiming to generate imaging biomarkers for clinical decision support in cancer
care. By utilizing imaging data from routine clinical work-ups, radiomics
enhances the understanding of tumor biology and supports precision medicine
(Figure 2). The noninvasive nature of radiomics enables comprehensive
assessments of tumors and their microenvironments, including temporal and
spatial heterogeneity. While there’s a surge in computational medical imaging
publications emphasizing the utility of imaging biomarkers in oncology, this
highlights the need for improvement and standardization to enable routine
clinical adoption of radiomics as a clinical biomarker [28]. Quantitative
features, categorized into intensity, structure, texture/gradient, and
wavelet, reveal subvisual attributes correlated with disease pathogenesis.
Numerous studies have explored the correlation between these features and the
malignant potential of nodules on chest CTs. In cancer patients, these
nodules’ features also correlate with prognosis and mutation status. However,
radiomics faces challenges such as non-standardized acquisition parameters,
inconsistent methods, and limited reproducibility. Ongoing research aims to
address these limitations, enhancing the acceptance of radiomics within the
medical community [29]. Dealing with high dimensional radiomics is also
considered to be a challenge in AI in NSCLC prognosis using imaging data
similar to genomic applications. While a combination of both, Radiogenomics,
is commonly used to ensure an optimized strategy to discover prognostic
imaging biomarkers. By correlating image features with coexpressed gene
clusters, predictive models were built, achieving significant correlations and
accurate predictions [81]. Notably, tumor size, edge shape, and sharpness
emerged as top prognostic indicators, suggesting the potential of this
radiogenomics approach to expedite the assessment of new imaging modalities in
personalized medicine for NSCLC.

Relationship between CT-based radiomic features and epidermal growth factor
receptor (EGFR) mutation status was another innovative approach that has been
used in many studies such as; exploring 11 radiomics from different categories
associated with EGFR mutation in 298 patients with surgically resected
peripheral lung adenocarcinomas [35]; 485 radiomics from pre-therapy CT scans
associated with EGFR mutation suggesting the potential of radiomic features in
guiding treatment decisions, particularly for EGFR tyrosine kinase inhibitor
(TKI) therapy [46]; EGFR and KRAS mutations status connected to radiomics also
revealed a potential correlation between EGFR mutation status and CT scan
imaging phenotypes [50]; radiomics associated with EGFR-targeted therapy in
advanced NSCLC patients with EGFR mutations, circulating-tumor DNA (ctDNA),
and clinical factors which led to predicting progression-free survival through
dealing with HD data [51, 52]. These methods are involved with HD data
processing and finding the imaging phenotypes and then their association with
genomics and clinical factors. This signifies the importance of dealing with
HD imaging biomarkers and their value within the application of AI for NSCLC
prognosis. Besides radiogenomics, radiopathomic data also has significant
popularity, where radiomic and pathomic features are fused to provide
performance status [30].

The correlation of clinicopathological (CP) and imaging data with 1-, 3-, and
5-year overall survival (OS) of surgically treated patients, both alone and
combined, revealed that combining CP and imaging parameters yielded the best
predictive values. Normalization influenced the performance of certain imaging
features in predicting OS, indicating the potential of density correction for
optimizing predictive models [49, 27]. Normalization, or known as
harmonization, of radiomics showed its strength in helping to predict
patients’ OS. The Nested ComBat [54] and OPNested ComBat [55] methods allow
for harmonization by multiple imaging parameters, while the GMM ComBat method
uses a Gaussian Mixture Model for grouping scans based on distribution shape
before harmonization. The findings suggest promise for improved
standardization and generalizability in datasets with multiple or unknown
imaging parameters.

Figure 2: Radiomics involves the extraction and analysis of quantitative
features from medical images, enabling the characterization of tumor phenotype
and behavior. These features are categorized into four main groups: first-
order statistics, which describe voxel intensity distributions and capture
spatial relationships between voxel intensities; shape features, which decode
the information related to the shape of tumor; and gray level and
spatiotemporal features, which encounter texture, pixel intensity, and
different features such as Laplacian of Gaussian (LoG), and Wavelet
descriptors, respectively, providing insights into tumor heterogeneity and
complexity.

Radiomics-based features showing potential for a computational model aiding
histopathological diagnosis and metastatic prediction in lung cancer. Such
studies offer a "virtual biopsy" without requiring whole-body imaging
scanning, providing valuable support for therapy decision-making through
machine learning to predict lung cancer histopathology and metastases [47].
Notably, quantitative CT analysis from surgically resected lung adenocarcinoma
(ADC), integrating various features, proved effective in noninvasively
identifying the micropapillary component. The findings emphasize the
diagnostic value of combining radiomic features with clinical parameters for
improved treatment planning in lung ADC [38]. Computational methods
integrating computerized subtyping and prognosis for NSCLC suggest that
quantitative analysis can effectively represent tissue characteristics,
offering a viable alternative to invasive pathological methods [41]. The
computer-aided detection (CAD) system for an integrated framework involving
cell detection, segmentation, classification, image marker discovery, and
survival analysis is also an interesting process that utilizes a robust cell
segmentation algorithm and various classification techniques, such as random
forest and AdaBoost [33]. Such models acquiring correlations between
discovered image markers and NSCLC subtypes, prognostic image markers related
to staining characteristics, and nuclear inhomogeneity significantly affect
the prediction of NSCLC patients’ survival.

Through integrating immunohistochemical staining, GLASS-AI, a machine
learning-based histological image analysis tool, identified dysregulation of
Mapk/Erk signaling in high-grade lung adenocarcinomas and locally advanced
tumor regions [56]. This underscores the utility of GLASS-AI in preclinical
models and the synergy of machine learning with molecular biology techniques
for unraveling the molecular pathways in cancer progression. Such studies are
used for HD radiomics-driven addressing interobserver variability in manual
segmentation of NSCLC [53]. Interaction of radiomics and demographics is also
commonly used due to the importance of these attributes [31], [32], [43, 44,
45]. Some findings suggest that baseline radiomics of lung cancer screening CT
scans can effectively assess the risk of cancer development [34], radiomics
outperforms other approaches, such as the Lung Imaging Reporting and Data
System, volume-only methods, and the McWilliams risk assessment model. In
addition, the effectiveness of CT radiomic features, in conjunction with
clinical data, for diagnosing distant metastasis in lung cancer was shown by
analyzing the predictive potential of CT radiomic features for distant
metastasis in lung cancer [42]. Some results showed that both 2D and 3D CT
radiomics features demonstrated prognostic ability through comparison of the
prognostic performance of 2D and 3D radiomics features in CT images of NSCLC,
but 2D features exhibited better performance in the study, suggesting their
more favorable use considering the cost of feature calculation [36]. However,
combining optimized radiomics signatures from 2D and/or 3D CT images with
clinical predictors showed superior prognostic performance [48].

The radiomic features’ dynamics in NSCLC during therapy and their impact on
prognostic models were found to significantly change during radiation therapy
[37]. This demonstrates that pretreatment compactness enhances overall
survival and distant metastase prediction. Notably, texture strength at the
end of treatment effectively stratifies patients for local recurrence risk.
The findings indicate that radiomics features, altered by radiation therapy,
can serve as indicators of tumor response, and contribute to prognostic
models. Moreover, texture features, specifically the standard deviation and
mean value of positive pixels, were found to be associated with tumor hypoxia
(measured by Glut1/pimonidazole) and angiogenesis (evaluated using CD34) [39].

Multimodal radiomics, extracting diverse features from medical images through
bioinformatic methods, holds promise in predicting tumor biology and behavior.
Particularly in 18F-fluorodeoxyglucose positron emission tomography (PET) plus
CT radiomics, early data suggest improved tumor volume definition and
prediction of radiation toxicity and treatment response in radiation therapy
[57]. The impact of respiratory motion on image features investigation
involving conventional and respiratory-gated PET/CT images of lung cancer
patients suggested that certain features, such as sphericity and entropy for
PET, and minimum intensity and RMS for CT, show minimal variability [58]. The
association of multimodal imaging features with gene expression pathways, such
as hypoxia and the KRAS pathway, was shown through exploring PET/CT imaging
for NSCLC prognosis [59]. AI methods to process imaging biomarkers are often
involved with machine learning techniques. Different strategies to handle HD
data can provide different outcomes which entails projecting HD imaging
biomarkers manifold to low-dimensional (LD) space to preserve characteristics
of datasets. To tackle challenges like feature redundancy and unbalanced data,
the study employed techniques such as Principal Component Analysis [40, 51,
52, 53, 54, 55], SMOTE [40], Clustering [51, 52], and LASSO [48, 59].

##  5 Deep Learning Integrated Prognosis

Deep neural networks revolutionized conventional machine learning and its
effect led to improvements in NSCLC prognosis (see Figure 3 and Table 1). A
challenge of lung cancer OS analysis using CT images was addressed through
utilizing unsupervised deep learning, residual convolutional autoencoder, to
leverage unlabeled data for survival analysis, demonstrating superior
performance compared to handcrafted features [60]. LungNet, a shallow
convolutional neural network (CNN), served as a non-invasive predictor for
NSCLC prognosis (and diagnosis), showcasing the potential of CNNs in
interpreting CT images for lung cancer stratification and prognostication
[61]. Lightweight models, i.e. 1D CNN model [62], offer efficient NSCLC
detection, consuming fewer resources, and time than traditional models,
potentially serving as a decision support system for oncologists and
radiologists. Deep neural network models were also developed for accurate OS
prediction in NSCLC patients, combining gene expression and clinical data,
suggesting its potential as a valuable tool for developing personalized
therapies and advancing precision medicine in NSCLC [64].

Figure 3: Deep learning has demonstrated notable applications in NSCLC
prognosis, including the integration of genomic, multiomics, pathological, and
imaging data for survival prediction. Future research in this domain aims to
refine deep learning models, enhance interpretability, and incorporate real-
time clinical data to further improve prognostic accuracy and guide
personalized treatment strategies for NSCLC patients. The future direction of
deep neural networks entails the development of more sophisticated models and
the integration of attention mechanisms and transformers for improved models.
Table 1: Summary of Deep learning and AI-based models for NSCLC prognosis

A stacking ensemble deep learning model (1D-CNN) incorporating the LASSO
technique for feature selection, demonstrated high performance metrics in RNA-
seq analysis which led to the classification of five distinct cancer types
[72]. Deep learning-based multi-model ensemble, employing a two-stage model
with the DESeq technique for S-fold cross-validation, demonstrated remarkable
accuracy which prevents overfitting and enhances predictive accuracy in RNA-
seq analyses [71]. Multimodal integration strategies utilizing microRNA, mRNA,
DNA methylation, long non-coding RNA (lncRNA), and clinical data showed
enhanced survival prediction over single-modality models while employing
denoise autoencoders for data compression and integration [80]. Utilization of
k-fold cross-validation in deep neural network models employing Focal Loss may
affect the model’s reliability, while KL divergence served as the model
features and efficacy of their approach in RNA-seq analysis [73]. Integrating
clinical information and multi-omics data (gene expression, miRNA, copy number
variations, and DNA methylation) for the deep neural network may result in the
transformation of the given features which leads to improved prognostic
prediction for NSCLC [68].

Insights into the intricate landscape of lung cancer subtypes, based on 450 K
DNA methylation data, also suggested employing an unsupervised deep learning
approach utilizing Variational Autoencoders (VAEs) to achieve high precision
in classification [75]. Adjusting prediction accuracy using the CNN framework
operating on protein-protein interaction (PPIs) networks and gene expression
profiles suggested marginal improvements compared to conventional algorithms
emphasizing the delicate balance between model complexity and performance
gains [74]. Utilizing multi-omics analysis resulted in surviving groups
(subtypes), independent of histopathological classification, and on reverse
phase protein array (RPPA) data, which led to improved prognosis in lung
cancer patients [65].

DeePaN, deep patient graph convolutional network, revealed distinct subgroups
with significantly different median survival post-IO therapy, outperforming
non-graph-based methods and offering insights into potential immune-
oncological (IO) biomarkers, such as tumor mutation burden (TMB), and
therapeutic considerations like KRAS mutations and blood monocyte count [66].
Shallow artificial neural networks-based metabolomic models also may improve
the prediction of staging and chemotherapy response [67].

Integrating clinicopathological, inflammatory, and radiomics features through
multidimensional deep neural network models, i.e. using DeepSurv and NMTLR
algorithms [77] or [25, 26], demonstrated enhanced predictive accuracy, aiding
in personalized prognosis and guiding optimal treatment strategies, especially
for patients identified in the high-risk group with significantly low PFS and
OS. An extension of deep-learning analysis using Hematoxylin and Eosin-stained
histopathological images resulted in identifying 50 histomorphological
phenotype clusters (HPCs) as pathomic features to improve prognostic precision
and therapeutic assessment in SCLC patients [79].

The imaging-based applications of deep neural networks showed significant
improvements in all aspects of lung cancer prognosis. These methods used
Multi-scale Deep learning-based Radiomics 1model, MDR-SURV [63], iterative
sure independence screening (ISIS) scheme [69], 3D ResNet on CT imaging and
measurement of programmed death-ligand 1 (PD-L1) expression signature (ES)
[70] or 3d ResNet on F-18 fluorodeoxyglucose PET (FDG PET) images with
clinical data [76], and an end-to-end deep learning model, Mobilenet v2,
leveraging PET/CT images.

##  6 Conclusions

The synergistic integration of AI methodologies, spanning different aspects of
prognostic analysis such as genomic, multi-omics, pathological, and imaging
biomarkers, alongside sophisticated AI models, i.e., deep learning approaches,
represents a notable advancement in the field of NSCLC prognosis. Through the
intricate analysis and synthesis of multifaceted data, AI-driven models have
revolutionized survival prediction, offering profound insights into the
disease’s complexity. This transformative paradigm not only enhances
prognostic accuracy but also facilitates the tailoring of personalized
treatment regimens, heralding a new era of precision medicine in NSCLC.

## ACKNOWLEDGMENTS

This work was supported, in part, by the University System of Maryland’s
William E. Kirwan Center for Academic Innovation grant 2022-2023 and TLTC T-02
(2024) funding initiative for incorporating artificial intelligence and
machine learning.

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