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Artificial Intelligence in Adaptive Radiation Therapy
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IOP Publishing
Artificial Intelligence in Adaptive Radiation Therapy
Yi Wang and X. Sharon Qi
Chapter 13
Artificial intelligence empowered response
prediction and adaptation
Denis Dudas and Issam El Naqa
Treatment response prediction is an important part of radiation therapy management,
as it describes and explains relationships between pre- or on-treatment variables
(imaging data, treatment planning data, demographics, clinical, -omics data, etc) and
follow-up outcomes (tumor control probability, radiation toxicities, survival time,
etc). Such knowledge, combined with advanced imaging techniques, offers a promising opportunity for the adaptation of planning dose distribution, target and organ
delineations, and dose prescription to maximize the treatment’sbenefits while
minimizing its side effects.
In the last two decades, adaptive radiotherapy (ART) has mostly been represented by treatment plan modifications based on on-treatment imaging data,
combined with simple population-based response models or even no response
models. In recent years, more advanced artificial intelligence (AI) techniques, such
as neural networks and other deep learning applications, have been adopted and
successfully implemented in treatment response modeling. Consequently, treatment
adaptation has become more personalized, benefiting from individual-based AI
response models. This, together with various explainability methods of machine
learning models, has made AI-based ART one of the most promising areas of
personalized radiation oncology with great potential in future years.
In this chapter, we provide an overview of the data resources typically utilized in
response modeling, a summary and examples of traditional and AI-based response
models, and we also discuss current trends and challenges in AI-based responseadaptive radiotherapy.
13.1 Data resources for response modeling in radiotherapy
Response modeling is currently an integral part of radiation oncology. It focuses on
relating patient, clinical, and treatment details with specifi c endpoints, e.g. treatment
doi:10.1088/978-0-7503-6119-4ch13 13-1 ª IOP Publishing Ltd 2025. All rights,
including for text and data mining (TDM), artificial intelligence (AI) training, and similar technologies, are reserved.
Artificial Intelligence in Adaptive Radiation Therapy
outcomes or time to follow-up events [1, 2]. The aim is to extract meaningful insights
and provide a reliable prediction of modeled events, which could be effectively used
in treatment planning, assessment, and adaptation. Therefore, it can sometimes be
thought of as a data science applied in radiotherapy. Consequently, we must first
define the data resources typically used in this area before discussing how AI
empowers response prediction and treatment adaptation.
13.1.1 Clinical data
Patient demographics and clinical details are often among the first data considered
in treatment response modeling and assessment since they are usually easily
accessible and considerably impact the final treatment outcome.
Various demographic details can play an essential role in response prediction.
One of the most significant factors is age. As shown by several studies, older patients
tend to exhibit worse treatment results and are more likely to be non-compliant with
radiotherapy treatment [3–5]. Other typical demographic information that is often
employed in outcome modeling and can be significant predictors are gender [6],
ethnicity and race [7, 8], household income [9], marriage status [10–12], smoking
status [13], and many others.
Any oncology disease and its treatment require a thorough consideration of the
patient’s complete medical condition. Therefore, relevant comorbidities and physiological details (e.g. cardiac function tests, pulmonary function tests, body mass
index, etc) might provide an essential insight into a patient’s prognosis and possible
outcomes. As many studies presented, comorbidities are highly associated with
overall clinical outcomes and survival [14–17]. However, in most patients, there are
multiple coexisting medical conditions, which makes it difficult for interpretation
and complex consideration with respect to the primary condition under investigation. Consequently, it is crucial to have an effective, accurate, and robust method for
measuring total comorbidity burden. For this purpose, different comorbidity indices
exist. The most typical, with general purpose, are the Elixhauser comorbidity index
(EI) and the Charlson comorbidity index (CCI) [18, 19].
Another relevant resource of clinical details is information involving the tumor,
whether its biology, site, or stage. One of the most critical factors is the tumor’s
histology, which can reveal its aggressivity or radiosensitivity. Tumor histology also
often predicts the probability of tumor local control (LC) and provides a valuable
insight for dose prescription [20–22]. Additionally, larger tumors, in terms of volume
and maximum tumor diameter (MTD), show poorer prognosis. Multiple studies
identified MTD and tumor volume as significant predictors of LC and progressionfree survival (PFS) [23–26]. This indicates a similar association between the patient’s
prognosis and cancer staging [27–29], since the stage is determined at diagnosis
according to the primary tumor’s size and its spread to regional or distant nodes.
With the expansion of precision medicine and personalized treatment, a significant effort has been made to find predictors and markers that could be used to design
an optimal treatment, while having a reliable outcome prediction. The main idea is
to predict tumor’s response to radiotherapy before executing it, and adapt the
13-2
Artificial Intelligence in Adaptive Radiation Therapy
Figure 13.1. The patient is the most valuable source of data and information, that can be used to tailor patientspecific treatment. The usual workflow involves collecting specimens, extracting and annotating various
biomarkers and predictors (radiomics, dosiomics, genomics, proteomics, etc), and final data analysis with
design and validation of the outcome model. (Reproduced with permission from [
of Physics and Engineering in Medicine.)
92]. Copyright 2017 Institute
treatment to patient-specific conditions and factors. Precision medicine involves
numerous techniques and specimen types (image, tissue, blood, etc), that can
generate different biomarkers [30]. There are several groups of biomarkers, depending on the specimen, and some of them are further discussed in subsequent chapters.
Integration of information from more specimens and heterogeneous biomarkers is
called panomics [31, 32]. Figure 13.1 shows a typical outcome modeling workflow
and data-supporting resources.
13.1.2 Imaging (radiomics)
Imaging data is perhaps the most prevalent resource for information in radiotherapy. It is used to plan the treatment and extract numerous biomarkers and
features that are highly useful in outcome modeling. It provides patient-specific
anatomy and physiology, and thus it is a great contributor to precision medicine.
There are multiple imaging modalities in radiotherapy. The most common are CT,
MRI, and PET or SPECT. They use different imaging principles and provide
different information. Diagnostic modalities, such as CT, are usually used for
diagnosis and radiotherapy planning. They show patient anatomy and are used to
extract electron densities in the patient’s body, which is crucial for subsequent dose
calculation. Treatment planning involves localization and delineation of the tumor,
delineation of organs at risk (OARs) and final dose calculation [33]. On the other
hand, MRI and nuclear medicine modalities (PET and SPECT) can be characterized
as not only anatomical, but also biological, molecular, and functional imaging. For
example, MRI can be employed to quantify a tumor’s proliferation or necrosis [34,
35], while PET is more suitable for assessment of the tumor’s metabolism [36, 37]or
the overall cancer staging [38, 39]. The advantage of combining data from more
13-3
Artificial Intelligence in Adaptive Radiation Therapy
modalities gave rise to PET/CT, which is now commonly used for multiple tasks,
including diagnosis, planning, and on/post-treatment follow-up [40, 41].
Techniques involving the extraction of a large number of features from imaging
data, quantitative analysis, and relating this to treatment outcomes (clinical
endpoints) is called radiomics [1, 42–44]. There are two basic approaches—
feature-based (conventional radiomics) and featureless (deep learning radiomics)
[45, 46].
Feature-based methods utilize hand-crafted features, which are analytically
predefined and capture characteristics and patterns in the analysed data. Such
features are always associated with a specific region of interest (ROI) (i.e. OAR or
target volume). Therefore, feature-based radiomics can only be applied on segmented data (2D or 3D) [42, 45]. There are numerous different radiomic features,
and they can be divided into histogram-based, shape-based, texture-based, modelbased, and transform-based categories. Histogram-based features are standard
statistical descriptors in gray-level histograms and are often referred to as firstorder features. A typical example of a histogram-based feature in PET images is the
standardized uptake value (SUV). Shape-based features describe the ROI in terms of
geometrical properties (e.g. maximum diameter, sphericity, compactness, etc).
Texture-based features are often referred to as second-order features. They involve
descriptors of the relationships between neighboring pixels, such as gradient
features, the gray-level co-occurrence matrix (GLCM), the gray-level run-length
matrix (GLRLM), and many others. Model-based and transform-based features fall
into a group of higher-order features, which usually involve an application of
specific mathematical transformations and operations, for example, Gabor filters,
Markov random fields, Wavelet transforms, or fractal analysis [42, 45, 47, 48].
There has been a rapid rise of radiomics-related papers in recent years, thanks to
the improvement of standardization in radiomics and the development of accessible
tools for its implementation. One of the most utilized open-source platforms is
PyRadiomics [49].
After extracting hand-crafted features from images, it is usually necessary to apply
some dimensionality reduction techniques, since radiomics often leads to hundreds or
even thousands of different features to be analysed. The purpose is to select the most
significant features concerning the outcome prediction to decrease the complexity of
the model and its computational burden. Various techniques are commonly used to
tackle this problem. The most straightforward are based on collinearity analysis using,
for example, Pearson’s correlation coefficient or variance inflation factor (VIF) [50–
52]. More advanced approaches may involve feature transformation methods, such as
principal component analysis (PCA) or clustering [48, 53–56]. Another option is
sensitivity analysis by stepwise forward or backward feature elimination based on one
of the model order optimality measures, i.e. Akaike information criteria (AIC) or
Bayesian information criteria (BIC) [57–60].
The last step in feature-based methods is the model design. The main factor to be
considered is the purpose of the model, i.e. classifi
cation (supervised/unsupervised)
or time-to-event prediction. For each category, several different architectures can be
adopted [45].
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Artificial Intelligence in Adaptive Radiation Therapy
Featureless radiomics benefits mainly from advances in the deep learning area, as
it works with features that are learnt and extracted by neural networks directly. The
process of feature extraction and outcome prediction is then performed simultaneously within one model (see figure 13.2). The typical architecture used for deep
learning feature extraction from images is the convolutional neural network (CNN)
[61, 62]. Others may include autoencoders (AE) [45] or architectures which are
designed explicitly for sequence data, such as RNNs or transformers [63, 64].
13.1.3 Treatment planning (dosiomics)
Radiotherapy is a treatment that uses targeted application of ionizing radiation to
eradicate tumor cells. Therefore, it requires a complex planning procedure, including
imaging data acquisition and fusion (CT and potentially also PET and MRI), OAR
and target volume delineation, and radiation delivery planning. The main result of
the whole planning process is a 3D dose distribution and technical details for its
delivery in clinics. The dose distribution is a rich resource for information regarding
outcome modeling, since it is directly related to tumor local control [65–68],
radiation toxicities in OARs [67, 69, 70], and overall survival [71–73]. The approach
of extracting features from the planning dose distribution and relating it to specific
clinical endpoints is commonly referred to as dosiomics.
Traditional dose features associated with outcome modeling are dose–volume
metrics (i.e. histogram-based features), which are directly linked to the concept of
dose–volume histogram (DVH) describing the frequency distribution of dose levels
in the studied ROI. Typical examples are minimum/maximum dose (D
mean dose (D
x Gy (V
). DVH metrics are currently the key concept for treatment plan quality
x
), minimum dose to x% volume (Dx), and volume receiving at least
mean
min/Dmax
assessment. Perhaps the most popular frameworks for normal tissues are
Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) [74,
75] and Hypofractionated Treatment Effects in the Clinic (HyTEC) [76].
More advanced traditional metrics involve quantities, such as equivalent uniform
dose (EUD) or effective volume (V
). EUD is defined as a uniform dose delivered
eff
to the target volume, with equivalent outcomes as the real 3D dose distribution. The
generalized definition for EUD is called generalized equivalent uniform dose
(gEUD) [77], and it is applicable to both target volumes and OARs. V
is defined
eff
as a hypothetical portion of the target volume, which if it receives the prescription
dose while the rest of the volume receives 0 Gy, produces equivalent outcomes as the
actual dose distribution [78]. The motivation for gEUD and V
was in the reduction
eff
of DVH information into more complex and more straightforward quantity for
outcome prediction, as the most common models in clinics—tumor control
probability (TCP) and normal tissue complication probability (NTCP)—are inapplicable on 3D dose distribution.
Even though DVH metrics can be good predictors of various clinical endpoints,
their main drawback is their inability to account for dose–spatial relationships
within the ROI, as DVH is basically a 2D reduction of 3D dose distribution.
Moreover, using QUANTEC and HyTeC recommendations and population-based
),
13-5