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Artificial Intelligence in Adaptive Radiation Therapy
Figure 9.14. A basic representation of utilizing a pre-trained network such as the VGGNet for semantic
segmentation. The red box indicates the original classification network, and the green box indicates early layers
which are often frozen in the initial training process.
non-medical images (RGB 0–255). Therefore, the kernels which come from this
training process are specifically tuned to t hese values. This does not mean that
simply translating the input image to a range of 0–255 is the complete solu ti on!
Many models have other pre-processing steps which occur prior to incorporation
into the model. Multiple pre-trained architectures in Tensorflow and PyTorch have
available pre-processing layers which can convert images from 0 to 255 into the
desired range for a pre-trained model during model creation
1
.
9.6.3.1.2 Image size
Within medicine we are often interested in not only 2D images, but 3D images as
well (CT, MRI, PET). The voxel dimensions of these images can vary greatly
depending on the acquisition parameters (field of view, slice thickness, etc). When
using convolutional neural networks, the kernels are invariant in size; meaning that
if a kernel is trained on a 3D image set with dimensions of 1 × 1 × 1 mm, that same
kernel will not likely have the same output if the input is scaled to 3 × 3 × 3 mm.
Proper understanding and accountability of the voxel size for training/validation/
test can be vital to an accurate knowledge of a model’s potential weaknesses and
biases. It is highly recommended that images be sampled to a uniform resolution, or
1
https://www.tensorflow.org/api_docs/python/tf/keras/applications/xception/preprocess_input
9-24
Artificial Intelligence in Adaptive Radiation Therapy
that the model be presented with an equal sampling of the resolution images that it
will later be expected to predict.
9.6.3.2 Deep learning
Deep learning is often criticized (fairly) as being a black box. While an explicit
explanation of everything which is occurring within a trained model can be difficult
to obtain, there are still several strategies which can be beneficial. First, an
evaluation of the kernels generated by a model can offer insight into the model’s
decision-making process. Second, when making a hand-crafted model, properly
visualizing the connections between each layer is an efficient method of troubleshooting errors. Both Tensorflow
2
and PyTorch3offer solutions for visualizing a
model which can help identify incorrect connections.
9.7 Summary
As we navigate the ever-expanding horizons of AI, the intersection of excitement
and caution becomes increasingly vital. Our discussions on AI registration and
segmentation underscore the transformative potential it has within ART. However,
while AI promises remarkable advancements, its implementation must be grounded
by the awareness of limitations and biases in every AI model. AI and adaptive
radiation therapy heralds a new era in personalized medicine, where each patient’s
treatment is guided by cutting-edge technology and personalized care.
We cannot wait to see what the future holds.
‘Do the best you can until you know better. Then when you know better, do
better.’—Maya Angelou
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IOP Publishing
Artificial Intelligence in Adaptive Radiation Therapy
Yi Wang and X. Sharon Qi
Chapter 10
Artificial intelligence-assisted dose prediction
and re-planning
Ivan Vazquez, Laurence E Court and Ming Yang
Artificial intelligence (AI), particularly deep learning, is revolutionizing adaptive
radiation therapy (ART) by enabling rapid dose prediction and treatment replanning. This chapter examines cutting-edge applications of deep learning for
dose prediction and re-planning in ART, highlighting their potential to dramatically
reduce planning time while maintaining or improving plan quality. Key topics
covered include convolutional neural network architectures for volumetric dose
prediction, strategies for training and evaluating dose prediction models, and
challenges in clinical implementation. The integration of AI tools into re-planning
workflows is discussed, including applications in auto-segmentation, beam orientation selection, and treatment parameter optimization. Emerging techniques such as
deep reinforcement learning for mimicking human planners are also discussed.
While deep learning approaches show immense promise for enhancing ART
efficiency and personalization, important considerations around data quality, model
interpretability, and quality assurance must be addressed for safe clinical deployment. Overall, this chapter provides a comprehensive overview of the current state
and future directions of AI-assisted dose prediction and re-planning in adaptive
radiation therapy.
10.1 Introduction
The integration of artificial intelligence (AI) into adaptive radiation therapy (ART)
represents a paradigm shift, with AI-powered tools showing promise in various
aspects of the treatment process, such as contouring, dose prediction, treatment
planning, and quality assurance. ART, as discussed in previous chapters, is a
transformative approach that enables clinicians to modify treatment plans based on
anatomical changes and treatment responses throughout the treatment course.
However, to make the most of ART’s benefits, it is crucial to estimate dose
distributions quickly and precisely and re-plan effectively when needed [1–4].
doi:10.1088/978-0-7503-6119-4ch10 10-1 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
Figure 10.1. Comparison of the traditional manual radiotherapy planning workflow (top) and the AI-assisted
workflow (bottom). AI tools can help streamline or fully automate key steps such as contouring, treatment
planning, quality assurance (QA), and treatment delivery.
Conventional radiotherapy planning, depicted in the top portion of figure 10.1,
usually demands a considerable amount of effort from human experts, and often
takes over one day to complete [5]. This complex task involves contouring of tumors
and critical structures, followed by iterative optimizations to maximize target
coverage while minimizing harm to healthy organs [6, 7]. Patient-specific factors
such as tumor size, location, and proximity to organs at risk (OARs) can add
complexity while lengthening the planning process, straining resources, and potentially impacting treatment outcomes [8]. Generally, the quality and consistency of
plans depend heavily on the expertise of the planning team, introducing variabilities
in patient care between and even within institutions [9–11]. The time-consuming and
labor-intensive nature of traditional planning methods, coupled with their reliance
on expert human involvement, poses significant challenges, especially in regions
facing shortages of trained professionals and limited access to radiotherapy
equipment.
Over the past decades, efforts have been made to automate key steps in ART,
including the estimation of suitable clinical dose distributions and the determination
of treatment parameters. Traditional knowledge-based planning (KBP) tools, which
rely on hand-engineered inputs and rule-based algorithms, were among the first
promising attempts to automate processes in the ART pipeline. More recently, KBP
methods have incorporated classical machine learning algorithms such as shallow
artificial neural networks (ANNs) to improve their performance [12, 13].
Nevertheless, typical planning times with KBP methods remain in the range of
minutes to hours, which might be unacceptable for online or real-time ART [12].
Currently, there is a growing trend towards the use of deep learning techniques
for dose prediction and automatic planning, which are expected to overtake
traditional KBP methods. This advancement brings previously unseen speeds and
automation capabilities to the ART workflows, fueling new promising ideas and
redefining the state-of-the-art in the field.
10-2