# Evaluating Large Language Models on a Highly-specialized Topic, Radiation
Oncology Physics

Jason Holmes  Department of Radiation Oncology, Mayo Clinic  Zhengliang Liu
School of Computing, The University of Georgia  Lian Zhang  Department of
Radiation Oncology, Mayo Clinic  Yuzhen Ding  Department of Radiation
Oncology, Mayo Clinic  Terence T. Sio  Department of Radiation Oncology, Mayo
Clinic  Lisa A. McGee  Department of Radiation Oncology, Mayo Clinic  Jonathan
B. Ashman  Department of Radiation Oncology, Mayo Clinic  Xiang Li  Department
of Radiology, Massachusetts General Hospital  Tianming Liu  School of
Computing, The University of Georgia  Jiajian Shen *co-corresponding author:
<EMAIL_ADDRESS>Department of Radiation Oncology, Mayo Clinic  Wei Liu
*co-corresponding author<EMAIL_ADDRESS>Department of Radiation Oncology,
Mayo Clinic

###### Abstract

We present the first study to investigate Large Language Models (LLMs) in
answering radiation oncology physics questions. Because popular exams like AP
Physics, LSAT, and GRE have large test-taker populations and ample test
preparation resources in circulation, they may not allow for accurately
assessing the true potential of LLMs. This paper proposes evaluating LLMs on a
highly-specialized topic, radiation oncology physics, which may be more
pertinent to scientific and medical communities in addition to being a
valuable benchmark of LLMs. We developed an exam consisting of 100 radiation
oncology physics questions based on our expertise at Mayo Clinic. Four LLMs,
ChatGPT (GPT-3.5), ChatGPT (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated
against medical physicists and non-experts. ChatGPT (GPT-4) outperformed all
other LLMs as well as medical physicists, on average. The performance of
ChatGPT (GPT-4) was further improved when prompted to explain first, then
answer. ChatGPT (GPT-3.5 and GPT-4) showed a high level of consistency in its
answer choices across a number of trials, whether correct or incorrect, a
characteristic that was not observed in the human test groups. In evaluating
ChatGPT’s (GPT-4) deductive reasoning ability using a novel approach
(substituting the correct answer with "None of the above choices is the
correct answer."), ChatGPT (GPT-4) demonstrated surprising accuracy,
suggesting the potential presence of an emergent ability. Finally, although
ChatGPT (GPT-4) performed well overall, its intrinsic properties did not allow
for further improvement when scoring based on a majority vote across trials.
In contrast, a team of medical physicists were able to greatly outperform
ChatGPT (GPT-4) using a majority vote. This study suggests a great potential
for LLMs to work alongside radiation oncology experts as highly knowledgeable
assistants.

##  1 Introduction

The advent of large language models (LLM) has completely transformed natural
language processing (NLP) [1]. The traditional paradigm of NLP follows the
typical pipeline of creating customized solutions for downstream applications
through supervised training. For example, a pre-trained BERT [2] model must be
modified with additional network layers and then fine-tuned on labeled
training data to perform tasks such as sequence classification or question
answering. In some situations, it might also be beneficial or necessary to
further pre-train such models on domain specific data to attain acceptable
performance [3, 4]. For example, AgriBERT [5] was pre-trained on agriculture-
related text data, to properly address NLP tasks in the food and agriculture
domain. However, the expansive size and exceptional few-shot learning
capabilities enable LLMs to solve NLP problems through in-context learning,
which reduces or even eliminates the need for annotated training samples [6,
7]. During in-context learning, LLMs generalize from a few examples (or no
examples at all) based on prompts, which typically are descriptive user inputs
that characterize desired responses from LLMs [6, 8]. For example, "summarize
the following text" is a straightforward prompt that asks the language model
to produce a summary for the input text. In general, LLMs provides a novel and
simplified workflow for NLP that could potentially do away with supervised
fine-tuning and its associated intricacies such as hyper-parameter tuning and
model architecture modification. Furthermore, in-context learning
significantly reduces the need for expensive and time-consuming human
annotation [6, 9]. It is especially desirable in medicine and science due to
the limited data available in these domains [10, 11, 4, 12].

In recent months, the world has witnessed the rise of of ChatGPT
111https://openai.com/blog/chatgpt, which has enjoyed significant global
popularity given its unprecedented language capabilities and accessibility to
the general public through a chatbot interface. ChatGPT is based on the
powerful GPT-3 model [6], one of the first large language models in history.
The 175-billion-parameters GPT-3 was trained on a large data collection that
encapsulated diverse Internet data (including the Common Crawl
222http://commoncrawl.org/ and Wikipedia 333https://www.wikipedia.org/). It
demonstrates exceptional performance in a variety of NLP tasks spanning from
text summarization to named entity recognition (NER) through its text
generation objective (indeed, many NLP tasks can be translated to some forms
of text generation). ChatGPT inherits these capabilities from GPT-3, along
with the massive knowledge on diverse topics stored in the parameter space.
More importantly, ChatGPT was trained through Reinforcement Learning from
Human Feedback (RLHF), a reinforcement learning process that incorporates
human preferences and human ranked values through user feedback. This process
tunes the model to generate outputs that are most appealing and relevant to
human users. The capabilities of ChatGPT empowers diverse practical
applications ranging from essay writing to code generation [13].

One of the most powerful LLM to date is GPT-4
444https://openai.com/research/gpt-4, a successor to GPT-3. While OpenAI has
not revealed much about its technical details yet, GPT-4 has demonstrated
superior performance over the GPT-3.5-based ChatGPT in various scenarios [14,
9, 12, 15]. In fact, as of March 2023, GPT-4 is powering Microsoft’s search
engine, Bing [16], which demonstrates the potential of LLM-based search. In
addition, unlike its predecessors, GPT-4 is a multi-modal model that accepts
image inputs, which undoubtedly leads to more interesting applications.

GPT-4 has been shown to perform exceptionally well on various academic and
professional benchmarks [14]. For example, GPT-4 passes the USMLE exam with a
$>$20% margin [17]. In fact, GPT-4 scores at over the 90th percentile on the
SAT, the Uniform Bar Exam and the verbal section of the GRE (see Figure 4 in
the "GPT-4 Technical Report" [14]), where almost all of them included a
multiple-choice component. Indeed, multiple-choice examinations are common for
evaluating LLMs [18, 14, 19]. Most multiple-choice exams that have been used
to evaluate LLMs are based on topics that are among the most well represented
in academics. For example, in 2022, the AP physics exam had 144,526 test-
takers [20], the LSAT had 128,893 test-takers [21], the GRE had approximately
342,000 test-takers [22]. As a result of the large numbers of test-takers
taking these exams as well as the importance placed on scores in determining
university admittance, there exists an exceeding amount of resources
(including text data accessible on the internet). Regardless of the particular
LLM under evaluation, the ease of access and overall ubiquity of these tests
and relevant test preparation materials effectively preclude a high
performance when evaluating LLMs on these tests. It is therefore important to
also study LLMs on more obscure and specialized topics where the size of the
training data is likely much smaller. In 2022, there were only 162 medical
school graduates, who applied for radiation oncology residency programs [23].
Radiation oncology physics therefore represents a topic that is relatively
unknown to the general population and may therefore be a more fair test in
evaluating LLMs as compared to highly represented knowledge-bases. Obscure
topics may represent the greatest educational opportunity and also the
greatest risk for the general population in the context of LLMs, as the
responses may be more relied upon while being less accurate and with mistakes
being less likely to be noticed.

An important factor in evaluating the accuracy of LLMs is to ensure that the
test questions are left out of the training data [24], i.e. not contaminated.
The best way to ensure this is to create new questions for testing. In this
study, a multiple-choice examination has been created for this purpose. Four
transformer-based LLMs have been chosen for evaluation: ChatGPT (GPT-3.5) [6],
ChatGPT (GPT-4) [14], Bard (LaMDA) [25], and BLOOMZ [26]. These results are
compared to radiation oncology experts as well as non-experts. Additionally,
ChatGPT (GPT-4) is further explored on how to improve its answers and on its
deductive reasoning capabilities.

##  2 Related Work

###  2.1 Large language models

Transformer-based pre-trained language models (PLMs), such as BERT [2] and GPT
[27], have revolutionized natural language processing. Surpassing previous
methods (e.g., RNN-based models) in numerous tasks, they have promoted
interest in and accessibility of language models [28]. Generally, PLMs can be
categorized into three types: autoregressive models (like GPT), masked
language models (such as BERT), and encoder-decoder models (e.g., BART [29]
and T5 [30]). More recently, there is a rise of very large language models,
including GPT-3 [6], Bloom [31], PaLM [32], and OPT [33]. Rooted in the
transformer architecture, these models draw inspiration from the likes of BERT
and GPT but are developed at much larger scales.

The objective of large language models is to accurately learn contextual and
domain-specific latent feature representations from input text [28]. For
example, the vector representation of "discharge" might vary considerably
between medical and general domains. Smaller language programs often require
continual pre-training and supervised fine-tuning on downstream tasks to
achieve acceptable performance [3, 4]. However, very large language models
could potentially eliminate the need for fine-tuning while maintaining
competitive results [6].

Besides the progress in model architecture, scale and training strategies,
large language models can be further aligned with human preferences through
reinforcement learning from human feedback (RLHF) [34]. This approach has been
implemented in various LLMs, such as Sparrow [35], InstructGPT [36], and
ChatGPT. InstructGPT was based on GPT-3 and was trained through a process
during which user preferences were prioritized through human-generated ranking
feedback. As a successor to InstructGPT, ChatGPT also employs RLHF, focusing
on adhering to prompts and generating comprehensive responses. OpenAI also
implemented guardrails to prevent the generation of biased and undesirable
outputs [37]. ChatGPT has become a highly successful AI chatbot, capitalizing
on GPT-3.5’s capabilities to facilitate human-like interactions.

RLHF incorporates human feedback into the generation and selection of optimal
results by training a reward model based on human annotators’ rankings of
generated outcomes [38]. This reward model then rewards outputs that best
correspond to human preferences and values. We believe these groundbreaking
innovations make ChatGPT the perfect candidate for this study.

The recent development of GPT-4 has significantly advanced the state-of-the-
art of language models. GPT-4 demonstrates enhanced reasoning abilities,
creativity, image comprehension, context understanding, and multi-modal
abilities, leading to more sophisticated and diverse responses. The success of
large GPT models spurs exploration into specialized variants for specific
fields, such as dedicated large language models for medical and healthcare
applications, which could potentially revolutionize these domains.

###  2.2 Language models and examination

Large language models have exceptional natural language comprehension
abilities. In addition, they are trained on massive data that supplies
substantial knowledge. These characteristics make large language models ideal
candidates for academic and professional benchmarks.

OpenAI recently released the first study in the literature that evaluates
large language models on academic and professional exams designed for educated
humans [14]. The results indicate that GPT-4 performs extremely well on a wide
variety of subjects ranging from the Uniform Bar Exam to GRE. In addition, a
study from Microsoft indicates that GPT-4 can pass USMLE, the professional
exam for medical residents, by a large margin [17].

This study is the first evaluation of large language models in the realms of
radiation oncology and medical physics, and we believe it can inspire future
research in evaluating LLMs on highly-specialized branches of medicine.

###  2.3 Prompt engineering

Collecting and labeling data for training or fine-tuning NLP models can be
resource-intensive and costly, especially in the medical domain [4, 9, 12].
Recent studies suggest that by employing prompts, large-scale pre-trained
language models (PLMs) can be adapted to downstream tasks without the need for
fine-tuning [6, 8].

A prompt consists of a set of instructions that customizes or refines the
LLM’s response. Prompts extend beyond merely describing the task or specifying
output formats. Indeed, they can be engineered to create novel interactions.
For example, it is possible to prompt ChatGPT to emulate a cybersecurity
breach with simulated terminal commands [39]. In addition, prompts can also be
used to generate additional prompts through a self-adaptation process [39].

The emergence of prompt engineering signifies the start of a new era in
natural language processing [8]. There is no doubt that carefully crafted
prompts have much potential for diverse and sophisticated applications.
However, determining the ideal prompt poses a unique challenge in the age of
large language models. Currently, prompts can be designed manually or
generated automatically [40, 8]. Although automatically produced prompts may
outperform manual prompts in certain tasks [8], they often suffer from poor
human-readability and explainability [41, 8]. Consequently, manual prompt
generation may be favored in domains where interpretability is crucial, such
as clinical practices and research. In this study, we design a suite of
prompts and chain-of-thought prompts based on our experience in radiation
oncology and medical physics and evaluate their impact on large language
models.

##  3 Methods

A 100-question multiple-choice examination on radiation oncology physics was
created for this study by an experienced medical physicist, designed to match
the curriculum for radiation oncology resident education as recommended by the
American Society for Radiation Oncology (ASTRO) [42]. This exam includes
questions on the following topics: basic physics (12 questions), radiation
measurements (10 questions), treatment planning (20 questions), imaging
modalities and applications in radiotherapy (17 questions), brachytherapy (13
questions), advanced treatment planning and special procedures (16 questions),
and safety, quality assurance (QA), and radiation protection (12 questions).
Of the 100 questions, 17 require numeric calculation (math-based). The exam
questions are listed in the Appendix, Section B.

###  3.1 Comparison between LLM scores and human scores

The 100-question multiple-choice test on radiation oncology physics was
inputted to each LLM in 5 separate trials (Trial 1 - Trial 5), except BLOOMZ,
which was only tested in one trial. Each trial, beginning on a new thread or
after reset, began with an initialization prompt notifying the LLM that it was
about to be tested. Next, the LLM was prompted with instructions and 20
questions in batches until the exam was complete. In each trial, the
instructions indicated to the LLM that it should only return the correct
answer with no justification. The instructions were included in each batch
since it was observed that the LLMs were less likely to follow the
instructions otherwise. In cases where the LLM could not accept 20 questions
at a time, batches of 10 questions were used instead (Bard). In cases where
not all the answers were returned by the LLM, the next batch would include the
non-answered question(s) as well as the entire next batch. These occurrences
were rare. In each test trial, the global prompt and instructions prompt were
phrased differently in order to account for response-noise due to prompt-
noise. The initialization prompts and instructions prompts are given in Table
1.

Table 1: The LLM prompts used in each trial. Trial |  Initialization prompt |  Instructions prompt
---|---|---
Trial 1 |  I am a radiation therapy researcher. My research group would like to study the answers given by ChatGPT on the topic of radiation oncology physics. I will now proceed to ask questions about radiation oncology physics. |  Instructions: For each multiple choice question, provide the correct answer without any justification.
Trial 2 |  I want to evaluate your knowledge on radiation oncology physics by asking some multiple choice questions. |  Please give only the question label and the letter for the correct answer.
Trial 3 |  Please answer the following practice questions as if you were a resident preparing for board certification exams. |  Only give the correct answer in your response. Do not explain your answers.
Trial 4 |  We want to test your understanding of radiation oncology physics. For this reason, we have created some questions to ask you. |  In your response, only report the question label and the corresponding answer.
Trial 5 |  I will ask you some multiple-choice questions. |  Instructions: Only respond with the correct letter choice.

LLM test scores and their distributions were compared between each other as
well as with scores from two human groups, medical physicists and non-experts.
The medical physicists group included four experienced board-certified medical
physicists, three medical physics residents, and two medical physics research
fellows. The non-expert group included six individuals with advanced degrees
in either electrical engineering, computer engineering, or computer science,
but with no known prior experience or education on radiation oncology physics.
Each human test-taker was allowed 3 hours to take the exam, closed book, also
permitting the use of a basic calculator. In comparing the LLM scores and
human scores, the mean scores, consistency in scores, and confidence in
answers were evaluated.

To quantify the overall consistency of scoring success, the standard deviation
and average correlation between trials, defined as the average of the upper
values of the Pearson correlation matrix between trials, were calculated. The
average correlation indicates how consistent the correct scores were between
trials where 1 is interpreted as the distribution being identical, 0 is
equivalent to the distribution being purely random, and -1 is interpreted as
the distribution being perfectly anti-correlated.

In order to quantify the degree of confidence in the answers given by the LLMs
and human groups, the number of correct answers for each question were counted
across all trials. For example, if each LLM answered the same question
correctly 5 times, then the percentage of questions where all 5 answers were
correct was incremented by 1% (since there are 100 questions). Additionally,
the test results were compared to the expected distribution that would occur
if the test-taker were guessing at random. The expected number of correct
answers in 5 trials, when randomly guessing, is approximately $0.25\times
5=1.25$ on average (98/100 questions have 4 choices, 2/100 have 5 choices).
Using this value, the number of correct answer occurrences for each question
can be estimated following the resultant Poisson distribution.

Finally, ChatGPT (GPT-3.5 and GPT-4) and Bard scores were compared to human
scores where the scores were calculated based on majority vote.

###  3.2 Improving ChatGPT (GPT-4) accuracy - explain first, then answer

Due to the nature of transformer-based LLMs predicting the next word based on
the prior context, it has been shown that the accuracy of responses can be
improved if a sufficiently large LLM is prompted to develop the answer in a
step-wise manner [24, 43, 44]. ChatGPT (GPT-4) was evaluated using this
strategy to see if its score could be improved by prompting it to explain
first, then answer. The initialization prompt was the same as in Trial 1,
however the instructions prompt for Trial 1 was changed to the following:
"Instructions: For each multiple choice question, first give an explanation
for the answer followed by the correct answer (letter choice)." These test
results were then compared with the original non-justified test results.

###  3.3 Testing ChatGPT (GPT-4) on its deductive reasoning ability

In a multiple-choice question, an LLM will be most successful when the
question and answer are often used in the same context. However, what happens
if the correct answer has no shared context with the question, such as when
the answer is "None of the above"? In this case, the LLM must deduce the
correct answer by rejecting all the other answers, all of which likely share
context with the question. This scenario would seem to be especially
challenging for an LLM. To study the deductive reasoning ability of ChatGPT
(GPT-4), each question of the 100-question multiple-choice exam was modified.
Each correct answer was removed and replaced with "None of the above choices
is the correct answer." Such a context-reduction transformation cannot be used
on a human since a human would notice the pattern. Because of this, there are
likely to be no examples of this sort of transformation to be found for tests
that were designed for humans and were subsequently used in the training data
for LLMs. It is assumed, then, that an LLM would not notice this pattern. The
modified exam was given to ChatGPT (GPT-4) using the Trial 1 prompts and was
subsequently tested for improving accuracy by explaining first, then answering
as described in Section 3.2.

##  4 Results

###  4.1 Comparison between LLM scores and human scores

The raw marks and mean test scores are shown in Figures 1 and 2 respectively,
where the LLM mean test scores represent the mean of 5 trials (except for
BLOOMZ - 1 trial) and the mean test scores for humans represent the mean of
their respective groups (see Section 3.1). Each LLM was able to outperform the
non-expert human group overall while only ChatGPT (GPT-4) outperformed the
medical physicist group. For math-based questions, the medical physicists
outperformed ChatGPT (GPT-4).

Figure 1: Raw marks for each test where the rows are separate tests and the
columns are the test questions. Dark shaded squares represent correct answers.
Figure 2: Mean test scores for each LLM by category.

As can be observed in the raw marks shown in Figure 1, each LLM and human
group showed variability between trials, not only in terms of uncertainty in
the overall score, but also in terms of the number of times each question was
answered correctly. The standard deviation and average correlation between
trials are reported in Figures 3(a) and 3(b). The LLMs were much more
consistent in their scores and answers as compared to the human groups,
showing both a low standard deviation in scoring and a high average
correlation between trials.

(a) Standard deviation in total scores.

(b) Average correlation between trials.

Figure 3: Consistency in scoring for LLMs and humans.

From the results shown in 4, Bard slightly outperformed the non-expert group,
however both groups performed similarly to a random guesser. ChatGPT (GPT-3.5
and GPT-4) and the medical physicists showed no similarity to random guessing.
ChatGPT (GPT-3.5) was either confident, getting 35% of answers correct in each
trial, or confused, getting 28% of answers incorrect. ChatGPT (GPT-4) was even
more confident, getting 67% of questions correct in each trial, however it
also showed a propensity for confusion, getting 14% of questions incorrect in
each trial. As a group, the medical physicists were neither extremely
confident, nor confused, however tending towards agreement in selecting the
correct answers.

(a)

(b)

(c)

(d)

(e)

Figure 4: Confidence in answers. The number of correct answer occurences per-
question for each LLM and human group. The dashed red curve indicates the
expected distribution if the answers were randomly selected based on the
Poisson distribution.

Although ChatGPT (GPT-3.5 and GPT-4) scored well overall, their scoring
distributions, shown in Figure 4, suggested that if the LLMs could work
together, there would be very little improvement in scoring, since they tended
to be either confident or confused with low variability. Bard (LaMDA) and the
non-expert groups would also likely show little improvement in working
together as their answers tended towards random success. However, because
medical physicists tended towards agreement on correct answers, it would be
expected that their score would improve considerably when working together. To
test for this, the answers for each group were combined using a "majority
vote". For each question, the most common answer choice was chosen as the
group answer. In the case of a tie, one answer among the most common answer
choices was chosen randomly. Figure 5 shows the scoring results when utilizing
a majority vote. As shown, ChatGPT (GPT-3.5 and GPT-4) improved very slightly,
1%. Bard (LaMDA) and the non-expert group improved by 4% and 3% respectively.
However, the medical physicist group improved greatly, by 23%.

Figure 5: Scores by category, tabulated by majority vote among trials for LLMs
and within the group for humans.

###  4.2 Improving ChatGPT (GPT-4) accuracy - explain first, then answer

Figure 6 shows the results for having prompted ChatGPT (GPT-4) to explain
first, then answer, therefore allowing the answer to develop. ChatGPT’s
(GPT-4) overall score improved by 5%, exceeding each prior trial. The greatest
improvement was in the brachytherapy and math-based questions categories.
These results are in agreement with prior studies that found this capability
to be an emergent characteristic for sufficiently large LLMs [44]. Sample
responses from ChatGPT (GPT-4) are given in the Appendix, Section A.

Figure 6: The improvement for Trial 1 as due to using the explain first, then
answer method.

###  4.3 Testing ChatGPT (GPT-4) on its deductive reasoning ability

Figure 7 shows the results for the deductive reasoning test where the correct
answer was replaced by "None of the above choices is the correct answer" in
all 100 questions. Overall, ChatGPT (GPT-4) performed much more poorly as
compared to the original test. Although the performance was generally worse,
the explain first, then answer method was especially important in improving
its ability to deductively reason through the questions. Without explaining
first, ChatGPT (GPT-4) got 0% of math-based questions correct, which improved
to 65% after incorporating the explain first, then answer method, only one
question less accurate than the original trial also using the explain first,
then answer method.

Figure 7: The scores for Trial 1 after replacing the correct answer with "None
of the above choices is the correct answer.", a method for testing for
deductive reasoning, and subsequent improvement as due to using the explain
first, then answer method.

##  5 Discussion

The aim of this study was to evaluate LLMs on a highly-specialized topic,
radiation oncology physics, based on a 100-question multiple choice exam that
was specifically designed for this study. The exam can be found in the
Appendix, Section B. The scoring results from the non-expert group suggest
that the general population knows very little about radiation oncology physics
as their scores were similar to random guessing. Bard (LaMDA) slightly
outperformed the non-experts while BLOOMZ and ChatGPT (GPT-3.5 and GPT-4)
greatly outperformed the non-experts. Amazingly, GPT-4 was able to outperform
the average medical physicist in nearly all subcategories and improved its
answer accuracy when prompted to explain its reasoning before answering
(Figures 2 and 6). ChatGPT (GPT-4) showed a surprising ability to deductively
reason in answering all 100 questions where each correct answer was modified
to be "None of the above choices is the correct answer.", particularly when it
was prompted to explain first, then answer, scoring 55% overall. This result
is somewhat perplexing and could potentially be an emergent property. Emergent
properties are known to occur as the number of parameters is increased in LLMs
[44]. This novel method may be a useful method in determining whether
deductive reasoning improves with the number of parameters going forward.

While ChatGPT (GPT-4) outperformed medical physicists overall, this study has
also provided evidence that individual LLMs cannot compete with a small number
of medical physicists working together (Figure 5). The likely reason is that
humans vary significantly in capabilities and knowledge from individual to
individual, even when their professional backgrounds are similar.
Additionally, while an answer in a multiple-choice question will either be
correct or incorrect, the scoring count distributions shown in Figure 4
indicated that the medical physicists were far less likely to be confused,
which, when aggregated over the whole group of medical physicists, allowed
them to select the correct answer at a much higher rate in a majority vote.
When ChatGPT (GPT-3.5 and GPT-4) was wrong, it was confidently wrong
(confused). Similarly, when it was correct, it was confidently correct. Our
results indicated that humans with expertise on a highly-specialized topic
knew when to guess, how to guess intelligently, and were less likely to be
wrong in their reasoning, even when the correct answer was not chosen. This
comparison may not be completely fair as it is possible that if the exact same
human could be tested repeatedly in the same manner as ChatGPT (GPT-3.5 and
GPT-4), they might also repeat answers and show a degree of confusion
individually. That point is arguably irrelevant, however, as there are many
experienced medical physicists and only few LLMs as capable as GPT-4. The high
degree of consistency in correct and incorrect answers for ChatGPT (GPT-3.5
and GPT-4) may be a sign of over-fitting (or memorization) in regards to
radiation oncology physics knowledge. Regardless, being that radiation
oncology physics is a highly-specialized topic, the performance of ChatGPT
(GPT-4) was extraordinary and will likely continue to improve in the near-
future. Practically speaking, this study suggests a great potential for
radiation oncology experts to work alongside ChatGPT (GPT-4), using it as a
highly knowledgeable assistant.

A weakness in evaluating LLMs using exams such as the one presented in this
study is that this exam is not representative of the detailed and nuanced
daily clinical work being performed by medical physicists and radiation
oncology specialists. The relative performance between LLMs and medical
physicists on radiation oncology physics exams reported in this study may
therefore misrepresent the degree of equivalency between LLMs and individual
medical physicists. Furthermore, GPT-4’s high performance on this
certification-like exam, covering a highly specialized topic, suggests a
degree of superficiality in the knowledge being assessed. Otherwise, we would
have to entertain the possibility of GPT-4 being competent enough to fulfill
the role of a medical physicist, which seems highly improbable. The radiation
oncology community, and possibly the wider medical community, may therefore
need to reevaluate certification procedures, as the necessity for humans to
invest significant effort in acquiring such superficial knowledge will
diminish as LLMs continue to advance. With this in mind, LLMs could
potentially be used as a test for superficiality. Perhaps a greater focus on
knowledge not known by the LLM should be more greatly emphasized.

###  5.1 Applying large language models in radiation oncology

This study is a continuation of a line of research that applies state-of-the-
art NLP methods to radiation oncology. For example, Rezayi et al. [11] trained
BioBERT on a large corpus of radiation oncology literature and a curated and
anonymized text dataset from a hospital to build ClinicalRadioBERT, a
specialized language model for radiation oncology. Liao et al. [45] proposed a
framework of directing the attention of transformer-based language models to
more important input tokens that significantly affect classification
decisions. This method is particularly important for few-shot learning with
few annotated samples, which is a common challenge in radiation oncology where
it is difficult to collect and curate large amounts of multi-institution
patient data that match certain requirements due to the concern of patient
privacy. On a related note, ChatGPT has demonstrated superior performance as
an effective text data augmentation approach over state-of-the-art text data
augmentation methods in terms of testing accuracy and distribution of the
augmented samples [9], which can also be used to address the few-shot learning
challenge.

In addition, LLMs can be employed for innovative applications such as data de-
identification. For example, GPT-4 outperforms ChatGPT and other language
model competitors in de-identifying clinical notes with a 99% accuracy [12].
This is of extreme importance to radiation oncology and all medicine
specialities in general, since it is often cumbersome to anonymize data for
cross-institution clinical collaboration and academic research. Some other
applications of language models include building domain-specific knowledge
graphs for oncology [46] without manual annotation from clinicians or other
domain experts.

###  5.2 Multi-modal models in radiation oncology

Multi-modal models are the future of language models [1] and are important in
medical diagnosis [47]. Some early LLM studies with multi-modal data include
ChatCAD [48], a framework to integrate images and texts for computer-aided
diagnosis. It supports various diagnosis networks such as those for lesion
segmentation and report generation. In this framework, ChatGPT can be used to
enhance the outputs of these networks.

GPT-4 supports multi-modal inputs such as images, which further unlocks the
potential of large language models in radiation oncology. It is necessary to
investigate future models and applications that integrate text, images,
dosimetric data, and other modalities into the diagnosis and treatment
pipelines. We believe such multi-modal models display inherent affinity to the
human brain [1] and future LLM models for medicine can receive inspirations
from advances in both neuroscience and NLP.

##  6 Conclusion

This study demonstrated that LLMs, particularly ChatGPT (GPT-4), can excel in
answering questions on a highly-specialized topic like radiation oncology
physics as well as exhibit surprising capabilities in deductive reasoning.
While GPT-4 outperformed individual medical physicists on average, a small
group of medical physicists working together still achieved better results,
demonstrating the importance of diversity in knowledge and expertise among
human professionals - and perhaps for LLMs as well. This further highlights
the potential benefits of combining LLMs’ capabilities with human insight and
experience to enhance knowledge and decision-making. Overall, the findings
suggest a promising future for LLMs in specialized fields.

## Acknowledgments

We would like to thank the many individuals (Yuzhen Ding, Yiran Cui, Yonghui
Fan, Yikang Li, Riti Paul, Nupur Thakur, Sachin Chhabra, Chenbin Liu, Yang Li,
Man Zhao, Yuchao Hu, Shumei Jia, Lian Zhang, Yao Xu, Hongying Feng, and Yunze
Yang) that volunteered to take the radiation oncology physics exam.

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Appendix

##  Appendix A Explain first, then answer responses from ChatGPT (GPT-4)

In order to show how the explain first, then answer prompt works in ChatGPT
(GPT-4), we selected 10 questions here for demonstration, starting from the
61${}^{\text{st}}$ question of the 100-question found in the Appendix, Section
B.

  1. 61.

1 Curie (Ci) is equal to $3.7\times 10^{10}$ Becquerels (Bq). Therefore, 10 Ci
of 192Ir is equivalent to $37\times 10^{10}$ Bq or 370 GBq.
Answer: C. Its decay rate is 37 GBq.

  2. 62.

The equation for effective half-life ($T_{\text{eff}}$) is
$\frac{1}{T_{\text{eff}}}=\frac{1}{T_{\text{phys}}}+\frac{1}{T_{\text{bio}}}$,
where $T_{\text{phys}}$ is the physical half-life and $T_{\text{bio}}$ is the
biological half-life. Solving for $T_{\text{bio}}$ gives
$T_{\text{bio}}=\left(\frac{1}{T_{\text{eff}}}-\frac{1}{T_{\text{phys}}}\right)^{-1}=\left(\frac{1}{2}-\frac{1}{6}\right)^{-1}=3\,\text{hours}$.
Answer: D. 3 hours

  3. 63.

The initial dose rate ($D_{0}$) can be calculated using the formula
$D_{0}=\frac{D_{\text{total}}}{(T\times\ln(2))}$, where $D_{\text{total}}$ is
the total dose and $T$ is the half-life of the isotope. For 125I, the half-
life is approximately 60 days.
$D_{0}=\frac{120\,\text{Gy}}{(60\,\text{days}\times\ln(2))}\approx
5.9\,\text{cGy/h}$.
Answer: B. 5.9 cGy/h

  4. 64.

According to AAPM TG-43, 125I has the greatest anisotropy correction among the
listed isotopes.
Answer: D. 125I

  5. 65.

AAPM TG-43 does consider the absorption and scattering in the source, so this
statement is not a limitation of the protocol.
Answer: A. The absorption and scattering in the source is not considered.

  6. 66.

The radial dose function, $g(r)$, is approximately constant for a 192Ir source
for values of $r$ less than 5 cm because the attenuation is compensated by the
increased scatter.
Answer: C. Attenuation is compensated by the increased scatter.

  7. 67.

The dose rate at 5 mm (0.5 cm) from the cylinder surface is 700 cGy. The dose
at the cylinder surface (r = 1.5 cm) can be estimated using the inverse square
law:
$\text{Dose}_{\text{surface}}=\text{Dose}_{\text{5mm}}\times\left(\frac{5\,\text{mm}}{15\,\text{mm}}\right)^{2}\approx
700\,\text{cGy}\times\left(\frac{1}{9}\right)=77.8\,\text{cGy}$.
Answer: D. 500 cGy (closest to the calculated value)

  8. 68.

To get a uniform dose at 5 mm from the cylinder surface in HDR vaginal
treatment, the source dwell times are longer at the ends of the cylinder.
Answer: B. are longer at the ends.

  9. 69.

The prescription dose for permanent prostate implant seeds is lower when using
103Pd as opposed to 125I because the half-life for 103Pd is shorter.
Answer: A. The half-life for 103Pd is shorter.

  10. 70.

Source calibration is not performed for HDR daily QA, as it is typically done
less frequently (e.g., monthly or quarterly).
Answer: D. Source calibration.

##  Appendix B 100-question multiple-choice exam on radiation oncology physics

Correct answers = C, A, B, A, B, D, B, A, C, A, D, A, D, C, A, A, E, C, C, D,
D, C, A, A, D, C, B, D, C, A, D, D, A, B, C, A, D, C, A, C, D, C, A, C, D, B,
A, B, C, B, B, D, C, A, C, A, C, B, C, C, A, D, B, D, A, C, B, B, A, D, C, D,
C, C, A, B, D, C, A, B, C, D, C, A, A, B, C, A, D, C, B, C, A, C, A, A, C, D,
C, B

  1. 1.

Which of the following particles cannot be accelerated by an electric field?

    1. (a)

Protons

    2. (b)

Electrons

    3. (c)

Neutrons

    4. (d)

Positrons

  2. 2.

The mass of Carbon-12 (12C) is  than the sum of the masses of 6 protons and 6
neutrons.

    1. (a)

slightly smaller

    2. (b)

slightly larger

    3. (c)

equal to

    4. (d)

All of the above are possible.

  3. 3.

The binding energies for tungsten’s K, L, and M shells are 70, 11, and 2 keV,
respectively. A 50 keV photon interacts with tungsten through the
photoelectric effect. What are the possible energies of the emitted
photoelectron?

    1. (a)

a continuous spectrum from 2 keV to 70 keV

    2. (b)

39 keV and 48 keV

    3. (c)

9 keV, 59 keV and 68 keV

    4. (d)

9 keV

  4. 4.

Which of the following statements is true regarding the x-ray spectrum of an
x-ray tube?

    1. (a)

The maximum photon energy is determined by the kVp.

    2. (b)

The maximum photon energy is determined by the target material.

    3. (c)

The characteristic photon energies are determined by the tube current.

    4. (d)

The characteristic photon energies are determined by the filtration.

  5. 5.

The half value layer (HVL) for an x-ray beam is 2 mm aluminum. What is the
minimum thickness of aluminum required to reduce the x-ray exposure by 50%?

    1. (a)

1 mm

    2. (b)

2 mm

    3. (c)

3 mm

    4. (d)

4 mm

  6. 6.

Which of the following component provides the high-power RF fields to
accelerate electrons in a LINAC?

    1. (a)

Thyratron

    2. (b)

Target

    3. (c)

Electron gun

    4. (d)

Magnetron

  7. 7.

What is the purpose of the “tongue and groove” design of multileaf collimator
(MLC)?

    1. (a)

Reduce frictions between leaves.

    2. (b)

Reduce leakage doses between leaves.

    3. (c)

Increase speed of leaves.

    4. (d)

Increase field sizes for treatments.

  8. 8.

When a LINAC is used to treat patients in electron mode, which of the
following hardware is not needed?

    1. (a)

Target

    2. (b)

Electron gun

    3. (c)

Monitor chamber

    4. (d)

Scattering foil

  9. 9.

Compared to a flattened beam, the flattening filter free beam  .

    1. (a)

Has higher average energy.

    2. (b)

Is more penetrating.

    3. (c)

Has a higher dose rate.

    4. (d)

Increases treatment time.

  10. 10.

The mass attenuation coefficient has the highest dependence on the atomic
number of the medium and the energy of the incoming photon energy for  .

    1. (a)

Photoelectric effect

    2. (b)

Compton scattering

    3. (c)

Pair production

    4. (d)

Rayleigh scatter

  11. 11.

Which of the following is not an interaction between photons and a medium?

    1. (a)

Photoelectric effect

    2. (b)

Compton scattering

    3. (c)

Pair production

    4. (d)

Bremsstrahlung

  12. 12.

When the energy of the scattered photon is the minimum in a Compton
interaction, what is the angle of the emitted Compton electron relative to the
direction of the initial photon?

    1. (a)

0∘

    2. (b)

45∘

    3. (c)

90∘

    4. (d)

180∘

  13. 13.

What is the relationship between the mass attenuation coefficient $\mu/\rho$,
mass energy transfer coefficient $(\mu_{\text{tr}}/\rho)$, and the mass energy
absorption coefficient $(\mu_{\text{en}}/\rho)$?

    1. (a)

$\mu_{\text{tr}}/\rho\geq\mu_{\text{en}}/\rho\geq\mu/\rho$

    2. (b)

$\mu_{\text{en}}/\rho\geq\mu_{\text{tr}}/\rho\geq\mu/\rho$

    3. (c)

$\mu/\rho\geq\mu_{\text{en}}/\rho\geq\mu_{\text{tr}}/\rho$

    4. (d)

$\mu/\rho\geq\mu_{\text{tr}}/\rho\geq\mu_{\text{en}}/\rho$

  14. 14.

How does the KERMA compare to the absorbed dose for a 15 MV photon beam at 10
cm depth in a water phantom?

    1. (a)

The KERMA is greater than the absorbed dose.

    2. (b)

The KERMA is equal to the absorbed dose.

    3. (c)

The KERMA is less than the absorbed dose.

    4. (d)

The relationship between KERMA and the absorbed dose can’t be determined.

  15. 15.

Which of the following beams will not produce a buildup effect in the percent
depth dose curve?

    1. (a)

X-rays from a 100 kVp x-ray tube.

    2. (b)

Gamma-rays from 60Co.

    3. (c)

6 MV x-rays from a LINAC.

    4. (d)

18 MV x-rays from a LINAC.

  16. 16.

Which device below is the most suitable for measuring lateral beam profiles?

    1. (a)

Diodes

    2. (b)

Farmer chambers

    3. (c)

Plane parallel ionization chambers

    4. (d)

Geiger-Muller counter

  17. 17.

Four exposed films have the same optical density of 1.25. If all four films
are placed on top of each other, what is the fraction of light that can
transmit through?

    1. (a)

20%

    2. (b)

1%

    3. (c)

0.1%

    4. (d)

0.01%

    5. (e)

0.001%

  18. 18.

Which of the following devices is not suitable for in-vivo dose measurements?

    1. (a)

Diodes

    2. (b)

Thermoluminescent dosimeters (TLDs)

    3. (c)

Ionization chambers

    4. (d)

Optically stimulated luminescence dosimeters (OSLDs)

  19. 19.

According to TG-51, which of the following correction factors is not needed
when converting the measured charge to the generated charge in the ionization
chamber?

    1. (a)

Temperature and pressure correction

    2. (b)

Recombination correction

    3. (c)

Scatter correction

    4. (d)

Polarity correction

  20. 20.

For a farmer chamber calibrated at the standard temperature and pressure,
which is the PTP factor for a local temperature of 23∘C and pressure of 720
mmHg?

    1. (a)

0.97

    2. (b)

0.99

    3. (c)

1.03

    4. (d)

1.06

  21. 21.

How is the beam quality defined in TG-51?

    1. (a)

Nominal beam energy

    2. (b)

Tenth value layer

    3. (c)

Average beam energy

    4. (d)

%dd(10)x

  22. 22.

According to TG-51, a LINAC should have dose calibration in water tank  .

    1. (a)

Every day

    2. (b)

Every month

    3. (c)

Every year

    4. (d)

Every other year

  23. 23.

Why is TMR always larger than PDD at depths larger than dmax?

    1. (a)

PDD includes both beam attenuation and beam divergence.

    2. (b)

PDD includes beam attenuation but does not include beam divergence.

    3. (c)

TMR includes both beam attenuation and beam divergence.

    4. (d)

TMR does not include beam attenuation but does include beam divergence.

  24. 24.

Which of the following changes will result in a larger geometric penumbra?

    1. (a)

Increasing SSD.

    2. (b)

Increasing the source to collimator distance.

    3. (c)

Reducing the source size.

    4. (d)

Move the patient closer to the collimator.

  25. 25.

The output of a 6 MV beam is 1 cGy/MU at SSD=100 cm, depth=1.5 cm and field
size 10 cm x10 cm. What is the output at SSD=98.5 cm, depth=1.5 cm and field
size 10 cm x10 cm?

    1. (a)

0.985 cGy/MU

    2. (b)

0.97 cGy/MU

    3. (c)

1.015 cGy/MU

    4. (d)

1.03 cGy/MU

  26. 26.

For a single field SAD treatment, the output at dmax is 1.02 cGy and the TMR
at depth 10 cm is 0.7. What is the MU needed to delivery 100 cGy at depth 10
cm?

    1. (a)

100

    2. (b)

102

    3. (c)

140

    4. (d)

160

  27. 27.

What wedge angle will produce the most uniform dose distribution for two
treatment fields separated by 100∘?

    1. (a)

30∘

    2. (b)

40∘

    3. (c)

50∘

    4. (d)

60∘

  28. 28.

A patient’s spine is treated by two adjacent fields with SSD=100 cm. The field
lengths (symmetrical fields) in the superior-inferior directions are 20 cm and
24 cm, respectively. What is the gap in the skin to match the two spine fields
at depth 10 cm?

    1. (a)

1.0 cm

    2. (b)

1.5 cm

    3. (c)

2.0 cm

    4. (d)

2.2 cm

  29. 29.

For a craniospinal treatment treated with symmetrical fields, the field length
for the upper spine field is 25 cm, and the field size for the lateral cranial
fields are 20 cm x 20 cm. What is the collimator rotation angle for the
cranial fields to align with the divergence of the spine fields?

    1. (a)

3∘

    2. (b)

5∘

    3. (c)

7∘

    4. (d)

9∘

  30. 30.

The dose-volume-histogram (DVH) contains the following information except:

    1. (a)

Location of the hot spot

    2. (b)

Mean dose

    3. (c)

Minimum dose

    4. (d)

Maximum dose

  31. 31.

What dose D5%=20 Gy to the heart on a DVH curve means?

    1. (a)

5% of the heart receives exactly 20 Gy.

    2. (b)

5% of the heart receives less than 20 Gy.

    3. (c)

95% of the heart receives more than 20 Gy.

    4. (d)

95% of the heart receives less than 20 Gy.

  32. 32.

Which of the following parameters is sometimes changed by the dosimetrist
during treatment planning?

    1. (a)

Dose for each treatment fraction

    2. (b)

Total treatment dose

    3. (c)

Number of treatment fractions

    4. (d)

Number of beams

  33. 33.

For electron radiation therapy, which electron energy has the minimum PDD on
the surface?

    1. (a)

6 MeV

    2. (b)

9 MeV

    3. (c)

12 MeV

    4. (d)

16 MeV

    5. (e)

20 MeV

  34. 34.

Electron PDD has a low dose tail after the practical range. Which physical
process is related to the generation of this tail?

    1. (a)

Compton scattering

    2. (b)

Bremsstrahlung radiation

    3. (c)

Characteristic x-rays

    4. (d)

Pair production

  35. 35.

Which of the following treatments is suitable for SSD setup?

    1. (a)

Four fields box for a pelvic treatment

    2. (b)

Multiple fields IMRT treatment

    3. (c)

Electron boost to chest wall of breast cancer

    4. (d)

VMAT treatment with two arcs

  36. 36.

Which of the following statements is true for electron effective SSD?

    1. (a)

It increases with increasing electron energy.

    2. (b)

It is equal to the distance from the x-ray sources to the isocenter.

    3. (c)

It is larger than the distance from the x-ray sources to the isocenter.

    4. (d)

It is independent of electron energy.

  37. 37.

What is the range of a 6 MeV electron beam in the lung (0.25 g/cm3)?

    1. (a)

3 cm

    2. (b)

6 cm

    3. (c)

9 cm

    4. (d)

12 cm

  38. 38.

What is inverse planning in IMRT?

    1. (a)

Choosing best beam angles.

    2. (b)

Choosing appropriate beam modifiers such as wedges and compensators.

    3. (c)

Optimizing beam fluence based on the desired dose constraints.

    4. (d)

Adding beam block to protect organs at risk.

  39. 39.

What is the purpose of the leaf sequencing algorithm in IMRT planning?

    1. (a)

It converts optimized fluence to the achievable fluence by MLC.

    2. (b)

It converts cost functions to the optimized fluence.

    3. (c)

It converts achievable fluence to dose.

    4. (d)

It converts desirable constraints to cost functions.

  40. 40.

The dose gradient achievable in an IMRT plan is approximately  .

    1. (a)

0.1%/mm

    2. (b)

1%/mm

    3. (c)

10%/mm

    4. (d)

50%/mm

  41. 41.

Which of the following devices is not appropriate for IMRT patient QA?

    1. (a)

Electronic portal imaging device

    2. (b)

Film and ionization chamber

    3. (c)

Diode Array

    4. (d)

Single ionization chamber

  42. 42.

Which parameter below is not typically set by a dosimetrist during IMRT
treatment planning?

    1. (a)

Beam energy

    2. (b)

Beam angle

    3. (c)

Beam weight

    4. (d)

Upper and lower dose constraints

  43. 43.

For material with HU=10, what is the ratio of linear attenuation coefficients
between this material to water?

    1. (a)

1.01

    2. (b)

1.1

    3. (c)

0.99

    4. (d)

0.9

  44. 44.

Which of the following methods can improve the spatial resolution of CT?

    1. (a)

Increasing tube voltage.

    2. (b)

Increasing field of view.

    3. (c)

Decreasing field of view.

    4. (d)

Decreasing tube current.

  45. 45.

Why can’t MR images replace CT for accurate dose calculations in radiation
therapy?

    1. (a)

MR images have inferior soft tissue contrast than CT.

    2. (b)

MR images have worse spatial resolution than CT.

    3. (c)

MR images deliver more ionization doses to the patient than CT.

    4. (d)

MR images do not have information about electron density.

  46. 46.

The velocity of ultrasound in soft tissue is 1500 m/s. The time to receive a
signal after sending a pulse is 0.1 ms. What is the depth of the anatomical
interface?

    1. (a)

5 cm

    2. (b)

7.5 cm

    3. (c)

10 cm

    4. (d)

15 cm

  47. 47.

Which of the following imaging modalities utilizes non-ionizing radiation?

    1. (a)

MRI

    2. (b)

PET

    3. (c)

CT

    4. (d)

Radiograph

  48. 48.

Which of the following statements is true about the spatial resolution of PET
images?

    1. (a)

PET images spatial resolution is better than CT and MRI.

    2. (b)

PET images spatial resolution increases when the average energy of the emitted
positrons decrease.

    3. (c)

PET images spatial resolution increases when the number of detectors in the
ring decreases.

    4. (d)

PET images spatial resolution is better than 1 mm.

  49. 49.

Which information below is not stored in a DICOM RT plan file?

    1. (a)

Patient name

    2. (b)

Treatment angles

    3. (c)

CT scanner parameters, such as kVp, mAs

    4. (d)

Prescription dose

  50. 50.

A 4D CT has 10 phases, 200 slices in each phase. Each slice has 512x512
pixels, where each pixel uses 2 bytes. How long does it take to transfer this
4D CT over the internet with a transfer speed of 2 GB/second?

    1. (a)

0.2 s

    2. (b)

0.5 s

    3. (c)

1.0 s

    4. (d)

2.0 s

  51. 51.

As compared to diagnostic CT, which statement below is not true for CT
simulator?

    1. (a)

CT simulator usually has a larger bore size than diagnostic CT.

    2. (b)

CT simulator gives better image spatial resolution and contrast than
diagnostic CT.

    3. (c)

CT simulator needs extra laser for localizing the treatment isocenter.

    4. (d)

CT simulator needs accurate geometry and HU for the whole body.

  52. 52.

Which techniques below do not result in a reduced setup margin for planning
target volume (PTV)?

    1. (a)

Change weekly IGRT to daily IGRT.

    2. (b)

Change the setup from laser to IGRT.

    3. (c)

Add fiducial markers to the target.

    4. (d)

Change 3D CRT plan to IMRT plan.

  53. 53.

To get the best soft tissue contrast for a head and neck cancer treatment,
which IGRT method is preferred?

    1. (a)

MV portal imaging

    2. (b)

kV orthogonal planar images

    3. (c)

kV cone-beam CT

    4. (d)

Surface imaging

  54. 54.

Which of the following IGRT methods is not used for IMRT treatment?

    1. (a)

MV portal image.

    2. (b)

kV orthogonal planar images.

    3. (c)

kV cone-beam CT.

    4. (d)

CT on-rail.

  55. 55.

Which IGRT method gives the lowest dose?

    1. (a)

MV portal image

    2. (b)

MV cone-beam CT

    3. (c)

kV orthogonal planar images

    4. (d)

kV cone-beam CT

  56. 56.

Prostate motion can’t be tracked by  .

    1. (a)

Infrared surface imaging.

    2. (b)

Implanted RF beacons.

    3. (c)

Implanted fiducial markers and fluoroscopy imaging.

    4. (d)

MR images in MR-LINAC.

  57. 57.

Which of the following is not an advantage of free-breathing gating for
treating a lung tumor?

    1. (a)

Reduced internal target volume.

    2. (b)

Reduced dose to organ at risk.

    3. (c)

Reduced treatment time.

    4. (d)

Patient is comfortable in free breathing.

  58. 58.

4D CT is not needed for a  .

    1. (a)

tumor near the diaphragm

    2. (b)

tumor in the brain

    3. (c)

tumor in the lung

    4. (d)

tumor in the liver

  59. 59.

is not a method for management of respiratory motion in radiation therapy.

    1. (a)

Deep-inspiration breath hold (DIBH).

    2. (b)

Real-time tumor tracking.

    3. (c)

IGRT using cone-beam CT.

    4. (d)

Free-breathing respiratory gating.

  60. 60.

The half-life for a radioactive nuclide is 69 days. How much does it decay in
one day?

    1. (a)

0.5%

    2. (b)

0.7%

    3. (c)

1.0%

    4. (d)

1.5%

  61. 61.

Which statement is correct for an HDR source, 192Ir, with activity 10 Ci?

    1. (a)

Its decay rate is equivalent to about 10 grams of 226Ra.

    2. (b)

Its decay rate is equivalent to about 1 gram of 226Ra.

    3. (c)

Its decay rate is 37 GBq.

    4. (d)

Its decay rate is 3.7 GBq.

  62. 62.

The physical and effective half-lives for an unsealed isotope are 6 hours and
2 hours, respectively. What is the biological half-life?

    1. (a)

12 hours

    2. (b)

8 hours

    3. (c)

4 hours

    4. (d)

3 hours

  63. 63.

What is the initial dose rate for a prostate 125I seed implant to deliver a
total dose of 120 Gy?

    1. (a)

4.9 cGy/h

    2. (b)

5.9 cGy/h

    3. (c)

7.0 cGy/h

    4. (d)

7.9 cGy/h

  64. 64.

According to AAPM TG-43,  has the greatest anisotropy correction.

    1. (a)

192Ir

    2. (b)

137Cs

    3. (c)

226Ra

    4. (d)

125I

  65. 65.

Which of the following is not a limitation of AAPM TG-43?

    1. (a)

The absorption and scattering in the source is not considered.

    2. (b)

The applicator is treated as water.

    3. (c)

The dose delivered while the source is in transit is usually ignored.

    4. (d)

The patient is treated as water.

  66. 66.

Why is the radial dose function, g(r), approximately constant for an 192Ir
source for values of r less than 5 cm?

    1. (a)

There is no attenuation for the first 5 cm.

    2. (b)

Attenuation is included in the geometry factor.

    3. (c)

Attenuation is compensated by the increased scatter.

    4. (d)

Attenuation is included in the anisotropy factor.

  67. 67.

An HDR vaginal cylinder case with a 3 cm diameter is planned to deliver 700
cGy to vaginal tissue at 5 mm from the cylinder surface. What is the
approximate dose to the surface of the cylinder?

    1. (a)

1244 cGy

    2. (b)

933 cGy

    3. (c)

700 cGy

    4. (d)

500 cGy

  68. 68.

To get a uniform dose at 5 mm from the cylinder surface in HDR vaginal
treatment, the source dwell times  .

    1. (a)

are the same at all dwell points.

    2. (b)

are longer at the ends.

    3. (c)

are longer in the middle.

    4. (d)

are longer at the superior end than the inferior end.

  69. 69.

Why is the prescription dose for permanent prostate implant seeds lower when
using 103Pd as opposed to 125I?

    1. (a)

The half-life for 103Pd is shorter.

    2. (b)

The energy of emitted photon for 103Pd is larger.

    3. (c)

The radial dose function g(r) for 103Pd is larger.

    4. (d)

The anisotropy function for 103Pd is larger.

  70. 70.

is not performed for HDR daily QA.

    1. (a)

Source position accuracy check

    2. (b)

Survey meter function check

    3. (c)

Radiation monitor function check

    4. (d)

Source calibration

  71. 71.

is the most appropriate device to find a lost radionuclide seed in the
operation room.

    1. (a)

A well ionization chamber

    2. (b)

A Farmer chamber

    3. (c)

A Geiger-Muller counter

    4. (d)

A diode

  72. 72.

Which of the following tests is not a typical procedure for HDR brachytherapy?

    1. (a)

Calibrating the source when a new source is used.

    2. (b)

Changing the source at a frequency of 3-4 months.

    3. (c)

Performing daily QA in the days only when patient treatment is scheduled.

    4. (d)

Performing patient surveys only before the treatment.

  73. 73.

Which statement below is true for TBI treatment?

    1. (a)

Lung block is always needed when lateral opposed beams are used.

    2. (b)

FFF mode is used to increase the dose rate.

    3. (c)

The extended SSD increases the dose uniformity.

    4. (d)

The treatment is faster than it is for regular patients.

  74. 74.

The maximum field size for a LINAC is 40 cm x 40 cm, and the height of a TBI
patient is 180 cm. What is the minimum SSD required for the TBI treatment with
a collimator angle of 0o?

    1. (a)

250 cm

    2. (b)

350 cm

    3. (c)

450 cm

    4. (d)

550 cm

  75. 75.

In total skin electron therapy (TSET), why is the gantry angle about 20o such
that the central axis is above patient’s head and below patient’s feet?

    1. (a)

To minimize the dose from x-ray contaminations.

    2. (b)

To maximize the dose from x-ray contaminations.

    3. (c)

To increase the SSD.

    4. (d)

To reduce treatment time.

  76. 76.

is not an appropriate delivery method for intraoperative radiotherapy (IORT).

    1. (a)

MeV Electrons

    2. (b)

MV photons

    3. (c)

HDR Brachytherapy

    4. (d)

kV x-rays

  77. 77.

Which of the following statements correctly describe the lateral penumbras of
therapeutic proton beams?

    1. (a)

They are always larger than penumbras of MV x-rays in LINAC.

    2. (b)

They are always smaller than penumbras of MV x-rays in LINAC.

    3. (c)

They do not change with depth.

    4. (d)

They increase with depth.

  78. 78.

is not needed for a dedicated nozzle for proton pencil beam scanning.

    1. (a)

A dose monitor chamber

    2. (b)

Scanning magnets

    3. (c)

A range modulation wheel

    4. (d)

A spot position monitor

  79. 79.

Which of the following statements is correct regarding range straggling in
proton therapy?

    1. (a)

It is due to the statistical uncertainty in energy loss.

    2. (b)

It results in about 3.5

    3. (c)

It can be minimized by robust optimization.

    4. (d)

It is the same as range uncertainty.

  80. 80.

Why do proton machines require a large gantry to bend protons, but LINACs
don’t?

    1. (a)

Protons at 250 MeV have a higher speed than electrons at 6-20 MeV.

    2. (b)

The mass of protons is about 1800 times more than the mass of electrons.

    3. (c)

Protons have positive charge while electrons have negative charge.

    4. (d)

Protons do not generate x-rays by Bremsstrahlung radiation.

  81. 81.

Which of the following statements is incorrect regarding intensity-modulated
proton therapy (IMPT)?

    1. (a)

An IMPT plan can be robust with robustness optimization.

    2. (b)

IMPT is more conformal than double scattering.

    3. (c)

IMPT is less sensitive to motion than double scattering.

    4. (d)

IMPT by pencil beam scanning needs less hardware than double scattering.

  82. 82.

Which of the following statements about RBE for protons is correct?

    1. (a)

It is always a constant 1.1 for all depths.

    2. (b)

It is the highest at the shallow depth.

    3. (c)

It is the highest at the center of spread-out Bragg peak.

    4. (d)

It is the highest at the distal fall off depth.

  83. 83.

Which device is not appropriate to measure the lateral profile of small fields
in SRS?

    1. (a)

Diode

    2. (b)

Pin-point ionization chamber

    3. (c)

Farmer chamber

    4. (d)

Diamond detector

  84. 84.

What is the purpose of using tertiary collimators in SRS technique?

    1. (a)

To reduce the penumbra.

    2. (b)

To reduce treatment time.

    3. (c)

To reduce collisions.

    4. (d)

To merge multiple isocenters to a single isocenter.

  85. 85.

Which of the following statements is not true about SRS?

    1. (a)

A flattening filter free (FFF) can’t be used in SRS technique.

    2. (b)

A Winston-Lutz test is required to verify the coincidence between radiation
and mechanical isocenters.

    3. (c)

The SRS field may not have lateral charge particle equilibrium (CPE).

    4. (d)

Both frame-based and frame-less systems are feasible for SRS.

  86. 86.

When planning for SRS/SBRT, which of the following statements is not typically
a priority?

    1. (a)

Rapid dose fall off outside of target.

    2. (b)

Homogenous dose inside the target.

    3. (c)

Conformality index to be close to 1.

    4. (d)

Gradient index to be as small as possible.

  87. 87.

Compared to regular fractionated treatment,  is not required for SRS
technique.

    1. (a)

a smaller CT slice thickness

    2. (b)

a smaller dose calculation grid

    3. (c)

a higher output accuracy

    4. (d)

a higher precision in the coincidence of beam and gantry/couch isocenters.

  88. 88.

Which SRS delivery technique can treat two brain metastases simultaneously
using a single isocenter treatment plan.

    1. (a)

Arc plan with dynamic MLC.

    2. (b)

Arc plan with cones.

    3. (c)

Dynamic conformal Arc.

    4. (d)

Arc plan with static MLC.

  89. 89.

is a test for MLC leaf positions.

    1. (a)

Start shot for gantry isocentricity.

    2. (b)

Winston-Lutz test.

    3. (c)

Start shot for MLC isocentricity.

    4. (d)

Picket fence test.

  90. 90.

According to TG-142, which test does not need to be performed daily?

    1. (a)

Door interlock

    2. (b)

Output

    3. (c)

Light field and radiation field coincidence

    4. (d)

Laser accuracy

  91. 91.

Which of the following parameters is the most important for cone beam CT
(CBCT) used in IGRT?

    1. (a)

HU accuracy

    2. (b)

Coincidence between the CBCT isocenter and the radiation beam isocenter

    3. (c)

Spatial resolution

    4. (d)

Field of view

  92. 92.

According to TG-142, which is the tolerance for monthly output constancy?

    1. (a)

0.5%

    2. (b)

1%

    3. (c)

2%

    4. (d)

3%

  93. 93.

The quality assurance test for  is not addressed in TG-142.

    1. (a)

CT simulator

    2. (b)

MLC

    3. (c)

CBCT

    4. (d)

Planar MV imager

  94. 94.

Which of the following parameters is not needed in shielding calculations for
the 2nd barriers?

    1. (a)

Distance.

    2. (b)

Occupancy factor.

    3. (c)

Use factor.

    4. (d)

Workload.

  95. 95.

What is the occupancy factor used in shielding design for the nurse station
next to a LINAC room?

    1. (a)

1

    2. (b)

1/5

    3. (c)

1/20

    4. (d)

1/40

  96. 96.

The proper order of materials for LINAC treatment room doors (from inside to
outside) is  .

    1. (a)

Steel, Borated polyethylene, Lead

    2. (b)

Lead, Borated polyethylene, Steel

    3. (c)

Borated polyethylene, Lead, Steel

    4. (d)

Borated polyethylene, Steel, Lead

  97. 97.

How many TVLs are needed to reduce the exposure rate to 1/5 of the original
value?

    1. (a)

0.3

    2. (b)

0.5

    3. (c)

0.7

    4. (d)

1.0

  98. 98.

has the highest radiation weighting factor.

    1. (a)

6 MV x-rays

    2. (b)

250 MeV protons

    3. (c)

6 MeV electrons

    4. (d)

100 keV neutrons

  99. 99.

Which of the following errors is not considered a Radiation Medical Event for
a 5-fraction SBRT prostate treatment?

    1. (a)

A total dose deviation of 25%.

    2. (b)

The wrong patient was treated.

    3. (c)

A single fraction dose deviation of 45%.

    4. (d)

Dose was delivered to the liver.

  100. 100.

Which of the following factors is not needed for a FMEA process map in CT
simulation of lung cancer?

    1. (a)

Documenting immobilization devices used in CT sim.

    2. (b)

Verifying bore size of CT sim.

    3. (c)

Verifying HU accuracy of CT Sim in daily QA.

    4. (d)

Verifying correct patient before CT Sim.