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Artificial Intelligence in Adaptive Radiation Therapy
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Artificial Intelligence in Adaptive Radiation Therapy
Yi Wang and X. Sharon Qi
Chapter 7
Overview of artificial-intelligence driven
adaptive therapy workflow
Chenyang Shen, Justin Visak, Andrew Godley and Mu-Han Lin
In the landscape of adaptive therapy, which includes offline, online, and real-time
approaches, two clinically relevant workflows for adaptive radiation therapy (ART)
are distinguished: offline and online [1]. Offline ART is the less time constrained of the
pair and typically aims to address anatomical changes throughout the treatment
course via offline re-planning with or without patient re-simulation. While artificial
intelligence (AI) holds the potential to enhance both online and offline adaptive
therapy workflows, we will specifically concentrate on its application in online
adaptive therapy in this chapter, given its seamless translation to offline scenarios.
Online ART addresses inter-fraction changes by creating a new treatment plan while
the patient remains on the treatment unit. This departs from traditional image guided
RT (IGRT) where typically the same reference plan is delivered repeatedly throughout
the treatment course. Online ART plans can be delivered using x-ray [2]orMRI
guidance, both available commercially [3, 4]. Regardless of the imaging guidance, the
online ART components can be generalized into simulation, pre-planning, daily
imaging/re-planning, and quality assurance, all of which can be enhanced with AIdriven workflows. Delivering a robust and successful online ART treatment demands
a specialized treatment team and workflow compared to conventional delivery
methods. The intricacies of online ART often necessitate heightened clinical resources.
However, the integration of AI in online ART streamlines workflows, enhancing
efficiency and facilitating a more coordinated approach. Automating optimization
tasks with advanced algorithms lowers the entry barriers for general clinics, widening
the spectrum of clinical adoption. This chapter explores the integration of AI into
ART workflows, emphasizing its transformative potential in enhancing efficiency,
precision, and accessibility. ART, encompassing offline, online, and real-time
approaches, adapts treatment to anatomical and functional changes, necessitating
specialized workflows and clinical resources. The chapter delineates the components
of ART—simulation, pre-planning, daily imaging and re-planning, and quality
doi:10.1088/978-0-7503-6119-4ch7 7-1 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
assurance—highlighting AI’s role in automating processes such as segmentation, dose
prediction, and treatment optimization. AI-driven advancements in synthetic CT
generation, auto-contouring, and real-time plan adaptation streamline ART workflows, enabling widespread clinical adoption. The discussion extends to future
directions, envisioning AI-enabled real-time ART and functional adaptation, which
incorporate physiological responses to personalize treatments further. This integration
of AI into ART paves the way for more dynamic, precise, and patient-specific
radiation therapy, setting the stage for its evolution into a standard of care.
7.1 Components of ART workflow
7.1.1 Simulation
One fundamental difference between an image guided RT (IGRT) and online
adaptive radiation therapy (ART) workflow is the expected patient time on table.
The objective of a successful online ART program is to expedite the typical 5–7 days
simulation-to-treatment workflow to a more rapid timeframe, aiming for completion
within 1 hour. Early publications indicate this is now clinically feasible for select
sites due to recent emergence of commercial technology [5]. Whether x-ray or MRguided online ART is utilized, it is reasonable and practical to expect a patient’s time
on the table will be increased for treatment [6]. Therefore, one important component
of the online ART workflow is simulation to ensure the patient is comfortable and in
a reproducible position. Once images are acquired, and the plan adaptation begins,
it is imperative to minimize patient movement. Striking a balance between effective
immobilization and patient comfort is essential. For instance, consider the use of
compression in treating mobile tumors—while maximum compression may be
dosimetrically advantageous, patients may find it challenging to endure the extended
periods of online ART under full compression. Therefore, adopting a more
moderate level of compression that aligns with patient comfort becomes a pragmatic
approach, ensuring both effective treatment and patient tolerance in the dynamic
landscape of online adaptive therapy.
The simulation process of adaptive therapy closely resembles conventional
radiotherapy, with the key distinction lying in the evaluation of whether a patient
is a suitable candidate who would significantly benefit from adaptive therapy. This
consideration plays a crucial role in guiding clinical resource allocation. Minimally,
a 3D computed tomography CT simulation should be acquired with reproducible
patient marking. For patients with intra-fractional motion, a ten-phase 4DCT can
also be acquired to encompass the motion of the gross tumor volume referred to as
the internal target volume (ITV). For MR-guided workflows, an MR simulation
may also be completed in addition or in place of the CT simulation to improve target
delineation and assess MR image quality at the time of simulation. These reference
images will be brought into an offline treatment planning system for delineation and
planning. For MR-guided workflows, the MR images may be used as the primary
planning images for the patient’s treatment. AI can further enhance this decisionmaking process by predicting patient candidacy based on various factors, such as
optimizing the choice of imaging modalities for simulation and treatment or
7-2
Artificial Intelligence in Adaptive Radiation Therapy
estimating the expected time a patient might spend on adaptive therapy. This
predictive capability contributes to efficient resource management, enabling the
optimization of clinical slot times and ensuring a streamlined and personalized
approach to adaptive therapy. Additionally, MR-only simulation workflows are
becoming of increasing interest [7, 8], where AI is a critical component in synthetic
CT image generation.
7.1.2 Pre-planning
After simulation, all the images, including requested diagnostic images, will be
imported into the offline planning system and a reference plan will be generated. The
first step of preparing a treatment plan is identifying organs-at-risk (OARs) and
target delineation on the reference images. It is well understood that AI will enhance
this process through various means of automating segmentation [9–11].
In order to facilitate an efficient online re-planning workflow, meticulous
attention must be given to the pre-plan process. Unlike a conventional workflow
where all relevant OARs and targets are delineated on the reference image, the
practicality of considering all OARs during online ART may be limited. Therefore,
a strategic focus should be placed on the most proximal OARs to the intended
target, such as those within 3 cm of the planning target volume, streamlining the
planning process. To enhance the precision and efficiency of online contouring and
re-planning, it is crucial to prioritize and define the high-dose impact OARs during
pre-planning. Rather than including all OARs and requesting physicians to recontour each during online ART, the identification of key OARs allows for a more
targeted and streamlined approach to strike on the key elements of generating highquality plans during online ART. Similarly, any tuning structures essential for the
adaptive process should be designed in a manner that enables automatic replication
and dynamic adjustment based on the new anatomy of the day. This strategic
approach optimizes the adaptation workflow, ensuring not only precision but also
efficiency in the creation of adaptive plans during online ART.
Current ART workflows for x-ray and MR-guided treatments require inverse
planning techniques where the planner upfront defines patient-specific or population-based dose–volume histogram (DVH) objectives [12]. These objectives are
transferred to an optimization problem where a computer algorithm attempts to find
the most optimal solution. Similar to conventional treatments, inverse planning for
online ART is as much of an art as a science and is highly dependent on a planner’s
skill and experience [13]. It is well-documented that any institution is subject to this
‘inter-planner variability’ and therefore this carves an important aspect in the online
ART process for AI to enhance the reference planning process. Over the past
decade, it has been of global interest to the radiation therapy community to deploy
AI during the optimization process. Some examples are direct DVH prediction, 2D/
3D dose prediction, beam angle geometry, and attempts at mimicking a human-like
planning process [14–17]. Notably, the integration of AI in pre-plan holds the
promise of bridging the gap in planner experience levels. More details on this topic
will be covered later in the chapter.
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