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Artificial Intelligence in Adaptive Radiation Therapy
Figure 13.5. Grad-CAM analysis of dose distributions for patients with LR—examples of dose distribution
slices with Grad-CAM maps, including the time of local recurrence in years. (Reproduced with permission
from [
151]. Copyright 2024 Elsevier.)
importance of each neuron in the model behavior. Typically, it is applied to the last
convolutional layer, as it has the most significant impact on the model’s decision. Its
main advantage, and the reason why it is very popular in outcome models using
imaging data, is the ability to visualize the model’s focus region spatially. See, for
example, Grad-CAM applied in the deep learning model, predicting local failures in
NSCLC treated with stereotactic body radiation therapy (SBRT) published by
Dudas et al [151].
13.2.5 Sample use cases
13.2.5.1 Lung cancer—tumor local control prediction
A deep learning outcome model predicting local control in NSCLC patients treated
with SBRT was adopted in the study by Dudas et al [151](figure 13.6). The model
utilized multi-modality data. It was built from three separate neural networks—two
CNNs and one VAE. The CNNs were applied to extract features from planning CT
images and planning dose distribution, while the VAE was used to extract underlying features from clinical/demographic patient details. The outputs from all three
NN were then concatenated and fed into a discrete-time survival NN, as shown in
figure 13.6. The survival neural network predicted the conditional probability of
tumor local control.
The dataset was split according to TRIPOD criteria type 2b [160]. The first part,
80% of the data, were used for stratified five-fold cross-validation. The remaining
20% were kept as an independent dataset for final testing.
The explainability technique Grad-CAM was applied to identify the parts of the
data which are the most significant with respect to the local control prediction.
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 13.6. Diagram of the DL-based outcome model for prediction of local control in NSCLC patients
treated with SBRT. (Reproduced with permission from [
151]. Copyright 2024 Elsevier.)
Subsequently, the results were used to guide the design of planning dose criteria to
mitigate local failures.
13.2.5.2 Breast cancer—acute skin toxicity prediction
The study by Saednia et al [165] presented conventional ML models, based primarily
on a random forest approach, to predict acute skin toxicity (radiation dermatitis)
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Artificial Intelligence in Adaptive Radiation Therapy
after whole-breast RT. The model was trained on quantitative biomarkers, extracted
as surface temperature and texture features from thermal images, acquired before
and during the treatment (after the fifth, tenth, and fifteenth treatment fractions).
The dataset consisted of 90 patients, split into training (83%) and independent
testing (17%) subsets. The model was cross-validated using the leave-one-out
methodology. The highest prediction accuracy (testing accuracy = 0.87) was
associated with thermal biomarkers obtained after the fifth treatment fraction.
13.2.5.3 Head and neck cancer—loco-regional failure, distant metastasis, and
overall survival prediction
About 300 multi-institutional patients from The Cancer Imaging Archive were
involved in the study by Diamant et al [166] to develop DL-based models predicting
loco-regional failures (LRF), distant metastasis (DM), and overall survival (OS).
CNNs, followed by MLPs, were applied to extract features from pre-treatment CT
images and model the endpoints.
The training/validation dataset included 194 patients, while the independent
testing dataset included 106 patients. The AUCs of predicting DM, LRF, and OS
were about 0.88, 0.65, and 0.70, respectively. The study also demonstrated the use of
explainability techniques (grad-CAM), showing a significant difference in the
decision explanation of patients who did or did not develop distant metastasis.
13.2.5.4 Prostate cancer—disease progression prediction
Conventional ML methods were applied in the study by Nayan et al [167] to predict
a grade progression in very-low and low-risk prostate cancer patients. Various
clinical and demographic features, such as age, family history of prostate cancer,
tumor stage, PSA level, prostate size, biopsy characteristics, and use of a 5-alpha
reductase inhibitor were included in the modeling.
The study investigated several ML approaches, including logistic regression (the
traditional and ML versions), SVM, RF, and ANN. Using a stratified random split,
the dataset was split in a ratio of 80% (training/validation) and 20% (independent
testing). The results showed a superior performance of ML approaches predicting
progression on active surveillance for prostate cancer.
13.2.5.5 Liver cancer—overall survival prediction
Three types of patient data (clinical, radiomics, and contrast-enhanced CT images)
were used in the study by Wei et al [168] for training a multi-modality DL-based
outcome model to predict the OS in hepatocellular carcinoma (HCC) patients
treated with SBRT.
Three separate models were trained: (i) VAE latent representation of clinical
details fed into DeepSurv NN [169]; (ii) VAE latent representation of radiomic
features fed into DeepSurv NN; and (iii) CNN-extracted features from CECT fed
into DeepSurv NN. Subsequently, all three models were combined in a joint survival
model for clinical, radiomic, and CECT features, as shown in figure 13.7. The joint
model reported the best performance (c-index 0.650; 95% CI 0.635–0.683).
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 13.7. VAE-based clinical survival NN, VAE-based radiomics survival NN, and CNN-based CECT
survival NN were jointly employed in the OS prediction of HCC patients treated with SBRT. (Reproduced
with permission from [
168]. Copyright 2021 Elsevier.)
The integrated gradient method provided the interpretation and concluded the
increased importance of normal tissue status for the OS.
13.3 AI response-based adaptive radiotherapy
Adaptive radiotherapy (ART) has been advancing since the late 1990s [170].
Typically, it is associated with anatomy-based modifications, requiring new PTV/
OARs delineations, or biology-based modifications using simple population-based
response models. However, with the emergence of AI, multi-modality imaging and
biotechnology, more advanced and individual-based approaches to response modeling were adopted. Consequently, significant improvements in the personalization
of ART can be achieved. Patient-specific response models can accurately predict
treatment outcomes while considering the actual state of a patient on treatment.
Therefore, it allows for personalized treatment adjustments during the treatment
course. This framework is often called knowledge-based response-adapted radiation
therapy (KBR-ART) [171]. Its diagram, in the context of current and previous
frameworks for ART, is in figure 13.8.
13.3.1 Requirements and challenges
All essential requirements and challenges related to response-based adaptive radiotherapy are encompassed in the acronym KBR-ART. These are knowledge,
response, and adaptation. The adaptation process is formulated based on
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 13.8. Comparison of previous, current and KBR frameworks for ART. (A) Previous framework—
treatment planning provided on imaging data, no adaptation during the course, outcomes estimated using
population-based response models. (B) Current framework—treatment planning provided on imaging data,
anatomy-based adaptation, outcomes estimated using population-based response models. (C) KBR-ART
framework—individualized treatment planning utilizing imaging and multiomics data for informed responsebased planning, personalized response-based adaptation, outcomes estimated using personalized response
models. (Reproduced from [
171]. CC BY 4.0.)
predictions from the response model, which, in turn, relies on the availability of
comprehensive patient datasets. As is evident, each KBR-ART component is
interdependent and cannot work without the rest.
The first requirement is knowledge (i.e. patient data). As introduced in section
13.1, various data resources prove valuable for outcome modeling. Particularly data
that capture characteristics of the patient, the patient’s disease, and planned
treatment. That includes clinical and demographic details (see section 13.1.1),
imaging biomarkers (see section 13.1.2), treatment planning data (see section
13.1.3), and multiomic biomarkers (see section 13.1.4).
The second requirement is a suitable and reliable response model. Modeling the
treatment response is a key part of modern oncology research, as it enables
treatment personalization. Radiotherapy outcomes are typically expressed in terms
of TCP and NTCP. Both can be modeled either analytically or via data-driven
models. While analytical methods have clear interpretation, ML/DL approaches are
often poorly interpretable with limited or no radiobiological understanding.
However, ML/DL-based outcome models usually outperform analytical models,
since they are capable of finding important underlying data representations.
Common outcome modeling approaches were reviewed in section 13.2.
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Artificial Intelligence in Adaptive Radiation Therapy
Finally, the third requirement is an adaptation of decision-making, illustrated in
figure 13.8 by the adaptation function π. The question is, how can we use outcome
models in a strategically optimal manner to adapt a patient’s treatment plan? The
resolution of this challenge leverages modern AI techniques. Architectures such as
reinforcement learning (RL) have a crucial impact in this area, as they are
specifically suitable for adaptive systems and sequential decision-making [172–
177]. Their primary advantages lies in their ability to explore the effects of
decision-making several steps ahead, for example, in dose regimen adaptations
between radiotherapy fractions. Other ML methods applicable in KBR-ART
involve Bayesian networks [178] or quantum deep RL [174].
The core component of RL is the Markov decision process (MDP), a framework
applied in sequential decision-making models. Each step of the MDP can be
described as four-tuple (s
the system (s
(a
∈ A), R is the reward function for a given action, and γ is the importance rate,
t
∈ S), atis the action undertaken in step t (i.e. from state stto s
t
, at, Rt, γt), where stdenotes the current state of
t
t + 1
which sets how far in the future the reward is considered. The system assigning
actions to states (i.e. s
× at) is called the policy. The policy in KBR-ART is called the
t
adaptation function (π)[171, 172]. The ultimate goal of RL, and other MDP
methods, is to find a policy that maximizes cumulative reward in the modeled
problem. The objective of MDP (the Q function), being maximized, is mathematically expressed by the following equation:
The optimal policy
∞
⎡
p
() ( ()) ( )
Qsa Rs s, E , . 13.6
p
is determined as
Q
t
gp=
∑
⎢
t
=
⎣
tts,a
0
p
⎤
,
p
⎥
⎦
p
. However, equation (13.6)is
Qmax
impractical for implementation due to the infinite sum. A more effective formulation
is called the Bellman equation:
)
p
∼∼
Qsa E Rsa Qsa, , max , , 13.7
such that
() () ( ) ( )
+
i
1
∼
=
QQlim
i
→∞
i
p
, and
′
()
∼
sPsa
,
{}
′∼sPsa,()
state s to state s′. As proved earlier, the convergent point
g=+′′
′∈ ′∈
aA;sS
i
denotes the transition probability from
p
is optimal [179]. The Q
Q
function is often solved utilizing deep learning techniques—deep Q-networks
(DQNs).
In KBR-ART, MDP simulates the clinical workflow. Each step (i.e. delivery of
each fraction) is described by a patient state s
, reward Rt, and external action atto
t
maximize the outcomes. The patient state is commonly characterized by TCP/NTCP
models, utilizing various input data (see section 13.2.1). The reward is usually a
predefined combination of treatment outcomes, prioritizing maximum TCP and
minimum NTCP. The external action may involve, for example, dose escalation,
localized radiation boosting, adapting the number of fractions, adapting the target
volume or even boosting the treatment with other modalities, such as chemotherapy
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Artificial Intelligence in Adaptive Radiation Therapy
or immunotherapy. In practical deployment, the only option to change the current
patient state and maximize treatment benefits is by actions of the adaptation
function. The optimal decision-making for each state is trained in the RL model
via numerous trial/error (s
tRt
)
(s
, R
t + 1
a
t
) interactions.
t + 1
13.3.2 Prediction versus treatment optimization
Precision medicine can encompass many tasks in a real clinical environment,
such as anatomy-based or outcome-optimized treatment adaptations.
Personalized treatm ent adaptations may be performed before, during, and after
the treatment.
The traditional approach involves adaptations as a result of on-treatment changes
in the patient’s anatomy and geometry (anatomy-based adaptations). Typically, a
new treatment plan must be created on new planning CT images when a significant
change in the geometry is detected. The adaptation can be offline (between
fractions), online (immediately prior to a fraction), or even real-time (during an
ongoing fraction) [180]. All three approaches may utilize various ML-empowered
tools, such as auto-image registration, auto-contouring, or auto-planning, but no
outcome model is necessary in such a framework. However, outcome modeling can
be applied to predict ART eligibility based on numerous imaging and multiomic
biomarkers [181, 182]. Other applications have demonstrated the promising role of
longitudinal CT radiomics in evaluating the point of the tumor’s most significant
change to trigger the anatomy-based ART [183]. In a study by Forouzannezhad et al
[184], a promising value of longitudinal CT, FDG-PET, and SPECT radiomics, to
predict survival in NSCLC patients for risk-adaptive cancer therapy, was presented.
One of the possible strategies involves treatment optimization prior to the initial
fraction. Suitable response models, predicting TCP and NTCP, can guide the dose
schedule adaptation to optimize outcomes [185].
Another potential, and perhaps more common , approach involves treatment
adaptation during t he treatment course, based on actual (mid-treatment)
patient data and information. Traditionally, baseline imaging biomarkers
(e.g. PET or MRI) would be evaluated before the treatment, followed by
mid-treatment evaluation. The adaptation in dose scheduling or target volume
definition would then be determined based on the comparison of these two
datasets [186, 187].
As discussed in the previous section, the state-of-the-art response-based ART is
KBR-ART. This framework is built on advanced AI models, comprising two
essential components: (i) the outcome model for predicting clinical outcomes and (ii)
the adaptation model optimizing the treatment, typically dose scheduling, based on
the outcome model prediction. Both models must work in an effective cooperation,
the RL model, where the reward function utilizes the outcome model. Most
commonly, the adaptation is not proposed between each fraction, but at a certain
point of the treatment, when it is possible to evaluate current outcomes and tailor the
adaptation [176].
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Artificial Intelligence in Adaptive Radiation Therapy
13.3.3 Sample use cases
13.3.3.1 Response-based dose prescription
In the study by Lou et al [185], a deep learning outcome model was applied to
individualize the dose prescription in lung cancer patients. The outcome model had
an autoencoder-like architecture to extract features from CT images, which were fed
into another neural network to predict the probability of tumor local control.
Prediction results were combined with other patient’s clinical data, in a regression
model, to provide a recommendation for personalized dose prescription, that
reduced tumor local failure probability below 5%.
Dose recommendations, provided by the model, had a wider range and were more
continuous than the actual delivered doses. It also suggested dose de-escalation in a
number of patients. Therefore, it provides a more flexible approach to treatment
personalization than regular clinical protocol. This framework allows personalizing
dose prescription according to the risk prediction, which potentially changes the
radiotherapy planning paradigm from binary decisions to response-informed
adjustments.
13.3.3.2 Response-based dose schedule adaptation
A framework for dose fractionation adaptation was presented in a study by Tseng
et al [177]. There were three complementary neural networks: GAN, DNN and
DQN. The GAN was used to generate more training data samples as the original
number of patients involved in this retrospective study was insufficient. Input
characteristics involved clinical, genetic, and imaging features. The DNN was
applied to reconstruct a radiotherapy artificial environment (RAE) to simulate
transition probabilities, for a given action, between different states (i.e. dose
schedule modifications). The last neural network (DQN) was responsible for the
evaluation of possible adaptation strategies and recommendation of the optimal
one, considering the treatment outcomes modeled by the RAE. This framework was
designed to provide dose schedule adaptations at about 2/3 period of the treatment,
based on dosimetric variables, clinical details, multiomics features, and PET radiomics, acquired before and after about 1/3 and 2/3 of scheduled fractions. The
presented results were benchmarked against real clinical dose adaptations, performed on patients enrolled in a dose-escalation clinical protocol.
A similar approach was adopted in the study by Niraula et al [175], which
introduced the Adaptive Radiotherapy Clinical Decision Support (ARCliDS)
software. They used GAN to generate more training samples, an artificial RT
environment (ARTE) to estimate treatment outcomes (TCP/NTCP), and an optimal
decision maker (ODM), built on DQN, to recommend an optimal treatment
adaptation. The ARTE combined a linear-quadratic-linear response model with
graph neural networks (GNNs), developed by Niraula et al [174]. The input
characteristics for TCP/NTCP modeling were dosimetric, clinical, multiomics, and
PET-radiomics features. The system was trained and validated in two different
treatment types for two different diseases (NSCLC and HCC). A diagram of the
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 13.9. (a) Diagram of response-adaptive radiotherapy using the ARCliDS system. A treatment plan is
created in phase 0, the patient’s treatment response is evaluated in phase 1, treatment adaptation is planned at
the end of phase 1, and it is executed in phase 2. (b) ARCliDS consists of two components—ARTE and ODM.
In learning mode, ARTE is trained in supervised learning, and its outputs are used in the reinforcement
learning of ODM. In operation mode, both components run simultaneously, providing outcome estimates and
optimal dose adaptation. (Reproduced with permission from [
175]. Copyright 2023 Springer Nature.)
framework and description of the ARCliDS operation/learning modes are shown in
figure 13.9.
13.4 Challenges and recommendations
Any AI deployment is generally associated with multiple challenges. However,
radiotherapy AI response modeling and response-based adaptations have several
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Artificial Intelligence in Adaptive Radiation Therapy
specific requirements, in addition to the general ones, of which all developers and
researchers in this field have to be aware.
A trustworthy and reliable response model in radiation oncology should always
be based on a comprehensive understanding of the biological mechanisms behind
disease specifics, progression, and control. Only then can the developer decide
whether the available data capture sufficient information for the desired prediction.
It is the lack of understanding of the disease biology and, hence, insufficient data
representation, which often limits the accuracy and trustworthiness of developed
response models. For the same reason, it is sometimes also difficult to select the most
indicative features for a given task. Input features are mostly selected based on
various statistical methods, not considering their real biological/clinical importance
for the modeled endpoint.
Another critical challenge for AI response modeling is the model’s architecture.
There is no universal architecture that fits all possible scenarios, clinical endpoints,
and the variability of input datasets. Therefore, the architecture is heavily influenced
by the developer’s skills and knowledge. The optimal architecture depends, for
example, on the problem definition (classification/regression), data type (structured/
unstructured), number of training samples, class imbalance ratio, and so on.
One of the most common issues in radiotherapy outcome modeling regarding
dataset quality is the imbalance of modeled endpoints. There are multiple methods
to overcome this problem, for example, over-sampling, under-sampling, class
weights, and the synthetic minority over-sampling technique (SMOTE) and its
variants [188–192]. However, the most effective appears to be generative models,
which can be utilized to generate new synthetic data points of any class. The
applicability of this concept to radiotherapy outcome modeling was recently
showcased in a study by Dudas et al [159].
The ultimate goal of all response and treatment adaptation models is their
translation into clinical practice. This can be accomplished only for models which
are properly validated and provide a robust explainability. The clinically preferred
validation process involves a prospective validation using a randomized controlled
trial or clinical trial. The randomized controlled trial is commonly regarded as the
optimal choice for prospective validation since it is expected to offer an unbiased
tool for causal inferences. An alternative to prospective validation is retrospective
validation, which evaluates models in past patients, who were not preselected for the
study. There are several scenarios for retrospective validation, and some of them are
described in the TRIPOD report [160]. The optimal approach for retrospective
validation involves a separate validation dataset, ideally from different institutions.
However, retrospective validation can never completely reduce all biases. For
example, it may suffer from biases such as clinicians‘/patients’ treatment preferences
or changes in care quality over time. Therefore, this is a drive towards prospective
validation via randomized clinical trials [193].
Existing challenges can be diminished if developers adhere to a suitable and
standardized checklist [1] (see the example in figure 13.10.
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