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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 2
Introduction to artificial intelligence in
radiation therapy
Elizabeth Huynh
The radiation therapy workflow is complex, involving many steps to see a patient
through from pre-treatment initial consultation to post-treatment follow-up
appointments. Each of these steps is labor intensive and involves decision-making
guided by a vast amount of information. This information has the potential to be
used by artificial intelligence (AI) to inform decision making, automate and improve
processes, decrease appointment times, and ultimately improve the workflow to
provide better care for cancer patients. This chapter provides an overview of the
radiation therapy workflow and highlights points throughout the workflow where
AI is currently being used clinically, and has the potential to be used in the future.
2.1 Introduction
Radiation therapy (RT) is a critical treatment for patients with cancer. While RT is
most commonly recognized for treating cancerous lesions and improving symptoms
from growing tumors, RT is also a treatment option for non-cancerous medical
conditions such as cardiac radioablation for ventricular tachycardia [1], trigeminal
neuralgia [2], and arteriovenous malformations [3]. The clinical RT workflow is
complex, involving the expertise of multiple role groups including radiation
oncologists, medical physicists, dosimetrists, therapists, and administrative staff.
Each step of the workflow involves manual input into various hardware and
software technologies to prepare, plan, and deliver radiation to the intended target
within the patient and minimize the dose to healthy tissue. In this chapter we provide
an overview of the RT workflow, staff roles within RT, and examples of how AI can
transform, and in some cases already has, the field of RT.
2.1.1 Radiation therapy workflow
The RT workflow for each patient can be broken down into seven steps: the decision
to treat with RT, simulation imaging, treatment planning, plan approval, plan
doi:10.1088/978-0-7503-6119-4ch2 2-1 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 2.1. Overview of the RT workflow. (Reproduced and adapted with permission from [4]. Copyright 2020
Springer Nature.)
quality assurance (QA), delivery of radiation, and follow-up care (figure 2.1).
The clinical workflow begins when the patient is referred to a radiation oncologist
for consideration of RT, where the radiation oncologist reviews a wealth of the
patient’s data. For example, the radiation oncologist will perform a review of the
patient’s symptoms, comorbidities, medical history, and a physical examination.
The patient’s prior diagnostic imaging studies, pathological and genomic data are
also evaluated. Using all this information, the radiation oncologist will assess the
risk and severity of potential side effects from RT and the potential benefit from RT
for their disease. The radiation oncologist will then formulate a plan for RT by
determining the dose to prescribe to the target, the number of treatment fractions
and frequency (e.g. daily, every other day, twice daily, etc), and the dose limits for
surrounding normal tissues, or organs-at-risk (OARs). The radiation oncologists use
information from nationally accepted standards, evidence from clinical trials, and
evaluation of the individual patient’s anatomy to determine the optimal dose to give
to the target over a certain number of fractions and how much dose to limit to the
OARs. This treatment intent is then presented to the patient for consent.
After the patient has consented to RT, the patient will attend simulation
appointments to gather the data necessary to create the treatment plan tailored to
the individual patient, primarily images to create the treatment plan. At the
simulation appointment, the patient will be put in the position they will be treated
in, and in most cases, the patient will be immobilized to reduce the likelihood of the
patient moving during radiation delivery at treatment. The position the patient will
be treated in is dependent on many factors such as the area in the body that will be
treated, the radiation technique that will be used to treat the patient, and patient
tolerability, that is, will the patient be able to remain in that position for the duration
of the treatment. Patients to be treated with RT often have comorbidities, prior
surgery or previous injuries that make certain positions intolerable for long
durations of time; in these cases, exceptions from the standard treatment position
will be made to accommodate patient comfort. Immobilization devices are hardware
that facilitate positioning the patient in a reproducible manner for treatment. For
example, for head and neck cancer patients, patients may be immobilized in a
thermoplastic mask that covers the patient’s head and neck and attaches to the
treatment couch, which ensures that the patient’s head and neck are in a consistent
position throughout simulation and treatment.
Modern RT treatment planning requires a three-dimensional image of the
patient, to identify and delineate the target and OARs, and a method for performing
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Artificial Intelligence in Adaptive Radiation Therapy
dose calculation. Currently, the majority of patients will receive a computed
tomography (CT) scan where the target and OARs are contoured, and the
Hounsfield units are used for determining the electron density for dose calculation.
After the patient is immobilized in the treatment position, they will have a CT scan
in the treatment position which is used for treatment planning. Due to the improved
soft-tissue contrast of magnetic resonance imaging (MRI) over CT, patients may
also receive an MRI either in the treatment position or a diagnostic MRI may be
registered with the CT to provide more information for contouring. Additional
diagnostic scans such as positron emission tomography (PET) that provide functional imaging may also be used. The use of additional imaging scans requires the
image to be fused to the CT to align the anatomy of concern between the two
images. The collective information from these images is used to define the target to
be treated or gather information regarding the intrafraction motion of internal
organs.
The target and OARs are contoured on the CT scan, and a treatment plan is
created using computer software. The computer software, known as the treatment
planning system (TPS), has the radiation treatment machine (linac) and radiation
interactions modeled to determine the machine parameters required for treatment
delivery and provide a visual depiction of the radiation dose distribution within the
patient. The TPS receives input for each patient, including the CT scan, contours
and dose prescribed. Various manual inputs are entered to assign beam parameters
such as the gantry angle range, collimator angle, couch angle, jaw positions, beam
energy, etc, that are optimal for the individual patient’s plan. The treatment plan is
designed with an optimization engine that determines the optimal positions of the
multileaf collimators with the appropriate radiation fluence emanating from each
gantry angle. This complex process requires the expertise of a dosimetrist that can
change the optimization parameters to achieve the desired result as prescribed by the
radiation oncologist for the target to receive a pre-specified dose and minimize dose
to the OARs. The optimal plan is then approved by the radiation oncologist. While
reviewing the treatment plan, the radiation oncologist may request changes resulting
in a re-plan, in which the optimization parameters may be further changed by the
dosimetrist. The final approved treatment plan is a representation of the radiation
dose that will be delivered to the patient. This treatment plan is then sent to the
record and verify system whose main purpose is to reduce the risk of treatment
errors in RT. However, the record and verify system may also integrate the TPS, and
interface with the treatment imaging and delivery systems.
Before the patient is treated with the treatment plan, the plan undergoes various
QA procedures. A physicist reviews the plan ensuring that it meets all the technical
requirements for treatment and the plan is delivering the intended dose to the target
and adequately sparing OARs as prescribed by the radiation oncologist. If the
physicist identifies errors in the plan, the plan may be requested to be re-planned.
Further QA procedures may take place on the plan to verify that the plan as
modeled in the TPS is what will be delivered by the linac, a procedure known as
patient-specific IMRT or VMAT QA where the treatment plan is delivered to a
phantom and a comparison of the delivered dose is made with the TPS planned dose.
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Artificial Intelligence in Adaptive Radiation Therapy
Each piece of hardware and software that is part of the RT workflow, such as the
linac, CT scanners, and TPS, also undergo periodic routine QA measurements to
ensure that they are performing as expected, and if they are not, that appropriate
measures are taken to adjust the hardware or software to realign them with their
expected performance.
When the patient arrives at the linear accelerator for treatment delivery, the
therapists set up the patient in the treatment position with the immobilization
devices that were determined at simulation. The therapists take a series of images
using the imaging capabilities available on the linac to ensure that the patient is in
the same position that they were simulated in. For example, conventionally, linacs
are equipped with x-ray based imaging methods such as 2D kilovoltage or
megavoltage radiographs or cone beam CTs (CBCTs) that can acquire images
before, after or during treatment. More recently, linacs equipped with an MRI
imaging system (MRI-linacs) have become available and are seeing increased
clinical use for MRI-guided set-up, monitoring motion during treatment, and
adaptive RT. Other systems are available that can also assist in setting up the
patient and monitoring motion during treatment, for example surface monitoring
systems such as VisionRT [3, 5] or respiratory management systems such as the
Varian real-time position management (RPM) systems [6]. The radiation treatment
plan that was created specifically for the patient is then delivered.
The time f rom simulation to treatment delivery can range from hours to weeks.
During this time, the tumor can grow and OARs change position, resulting in
different anatomical positions from the treatment plan. Furthermore, the majority
of treatments occur over multiple fractions, where changes in anatomical position
and geometry can occur between fractions. Adaptive RT involves changing the
patient’s treatment plan based on updated information of their current anatomy on
that particular treatment day. More details on adaptive RT are provided in
chapter 6.
While the patient is on treatment, their chart and images are reviewed by RT staff
periodically throughout their treatment (e.g. weekly), to ensure that they are
receiving the treatment as intended. The radiation oncologist and other RT staff,
such as nurses, also meet with the patient throughout the course of their treatment to
discuss the treatment and any concerning side effects. After treatment, the patient
attends a series of follow-up appointments with the radiation oncologist to review
their response to RT including both toxicities and tumor response.
While the RT clinical workflow varies slightly at every institution, the general
steps of consultation, simulation, treatment planning, plan approval, plan QA,
treatment delivery and follow-up are foundational to the RT workflow.
2.1.2 Staff roles in radiation therapy
RT is a highly technical field involving the expertise and interactions of multiple role
groups and technologies. Each role group is highly trained in their particular role
through the clinical workflow. Staff that interact with the patient are considered
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 2.2. Representative example of staff role assignments throughout the RT workflow. (Reproduced with
permission from [
4]. Copyright 2020 Springer Nature.)
patient-facing ‘front-of-house’ roles, while staff roles that primarily do not interact
with the patient are considered back-of-house roles (figure 2.2).
Administrative staff are involved in booking the multiple patient appointments
for simulation, treatment, and on-treatment and follow-up visits with nursing and
radiation oncologists. They follow guidelines to determine the appropriate timing
and sequencing for multiple appointments for each patient. In a cancer center with
hundreds or thousands of patients each year and a series of different appointments
depending on disease site, cancer staging, and treatment plan, the manual booking
of these appointments can be challenging. The booking of these appointments may
occur in multiple softwares, such as the hospital electronic health record and the
record and verify system used by the cancer center. These staff must interact with
multiple softwares and with patients to discuss their appointments with them.
Radiation oncologists perform the initial consultation with the patient and using
the patient data available to them, evaluate the patient’s suitability for RT and
determine the radiation dose, fractionation, frequency of treatment, and limiting
doses to relevant OARs. After simulation, the radiation oncologist will determine
and contour the treatment target and relevant OARs. While other role groups may
contour OARs for the purposes of efficiency and workload, the radiation oncologist
is ultimately responsible for reviewing and finalizing these contours. The radiation
oncologist discusses and reviews the treatment plan with the dosimetrist, and takes
into consideration the medical condition and history of the patient. The radiation
oncologist approves the dose distribution to be delivered to the patient. They meet
with the patient throughout their treatment for management of side effects and
patient concerns, and follow-up with the patient after their treatment is complete for
continued management of side effects and assess treatment response.
Dosimetrists generate treatment plans based on the radiation oncologist’s
contours, and the prescribed dose to the treatment target and OARs, maximizing
the dose to the target and minimizing the dose to the OARs. This is a predominantly
manual trial-and-error process in which the dosimetrist chooses the appropriate
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Artificial Intelligence in Adaptive Radiation Therapy
beam parameters for the treatment plan based on the patient’s geometry and
anatomy. The dosimetrist will then manually optimize the treatment plan by
inputting objective functions, manipulating optimization tools available in the
TPS, and utilizing optimization contours created by the dosimetrist. Through
dosimetrist experience and consultation with the radiation oncologist, a treatment
plan is generated and reviewed with the radiation oncologist. For patients that have
been previously treated with RT, dosimetrists may need to utilize the dose
distribution from the previous treatment to adjust the current treatment plan to
achieve acceptable cumulative doses to the relevant OARs. This information is also
reviewed and approved by the radiation oncologist.
Physicists are responsible for ensuring the software and hardware technologies
being used in RT are safe and accurate. When a new technology is introduced in the
clinic, a physicist must perform rigorous testing on the technology to ensure that the
technology is performing as expected, a process known as commissioning. For
example, when a new linac is commissioned, physicists perform numerous measurements on the linac and compare these results to how the radiation beam is modeled
in the TPS and expected performance. Once the technology is being used clinically,
routine QA measurements are either performed by the physicist or overseen by the
physicist on these software and hardware technologies at various frequencies. For
example, therapists may perform the daily QA on the linac, but the results are
reviewed by a physicist, whereas a physicist will perform annual QA measurements
on the linac. Patient-specific QA measurements may also be performed for a
treatment plan, and while these measurements may be performed by a physics
associate, assistant or trainee, a physicist provides the final approval before patient
treatment. Physicists review treatment plans prior to treatment to evaluate the
technical aspects of the treatment plan for suitability for treatment, and ensure that
the treatment plan is fulfilling the desired intentions of the radiation oncologist
through reviewing the contours, dose distribution, beam, and optimization parameters. The role of physicists is largely back-of-house working closely with the
technology involved in RT.
Therapists have a predominantly patient-facing role. At the treatment simulation
appointment, therapists are responsible for setting up the patient in the appropriate
treatment position, and acquiring identifying information about the patient such as
a photo for the patient records. Therapists also administer the radiation treatment at
each fraction and are responsible for patient safety and avoiding misadministration
of radiation. They set up the patient in the treatment position on the linac, acquire
images to ensure the patient is in the correct position, prompt the linac to deliver the
radiation treatment plan, and monitor the patient while treatment is being delivered.
Therapists have the most interaction with the patient out of all the role groups, and
are responsible for overseeing the patient’s health during treatment. If patient
concerns are noted during treatment, the therapists are responsible for advising the
patient if the concern is within their area of expertise, or to refer them to nurses or
the radiation oncologists. Therapists interact with the hardware and software
technologists involved in RT, they are the primary users of the linac on a daily
basis, and interact with the TPS and record and verify systems to perform QA tasks
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Artificial Intelligence in Adaptive Radiation Therapy
such as checking-in new patient charts to ensure all the required information is
present and accurate or complete tasks related to a patient completing treatment. In
some institutions, therapists may also be responsible for contouring OARs where the
radiation oncologist performs a final review of these contours, and generate simple
treatment plans for palliative patients.
While there are several other important role groups involved in RT such as
nurses, dietitians, information technology personnel, and engineers, the focus of AI
applications in RT have predominantly been to address challenges encountered by
the aforementioned role groups.
2.2 Overview of AI in radiation therapy
Numerous steps are required for treating a single patient in the RT workflow,
multiplied by the hundreds or thousands of patients that are treated at a single
cancer center, which can lead to variability in the quality of care among all staff
involved in the RT workflow. Throughout this overview of AI in RT, a glimpse of
where AI can be used to improve efficiency, accuracy, and standardization of patient
care are provided through examples at each step in the workflow. Greater detail on
the applications of AI in RT are provided in the following chapters.
2.2.1 Patient evaluation and dose prescription
The challenge for radiation oncologists as they evaluate the patient at consultation is
the myriad of available data related directly to the patient (e.g. medical history,
pathological and genomic data, etc) and clinical evidence from previous patients
through clinical trials detailing the risk of toxicities and benefits of treatment. As the
magnitude of these data continue to increase, AI tools have the potential to
automatically determine the most important clinical data to support radiation
oncologists in their clinical decision making. While at present, there are no
commercially available AI tools to do so, AI tools have been developed to assess
medical images [7] and electronic medical records [8–10], and have shown the
potential for predicting treatment outcomes [11–13]. Further development of these
AI tools for RT specific patients are required for the application of guiding decisions
on the treatment regime for RT patients.
While radiation oncologists use nationally accepted standards and evidence from
clinical trials to prescribe radiation dose to the tumor and dose constraints to the
OARs, often these goals for the treatment plan are not achievable due to the
arrangement of the tumor and surrounding anatomy, which is highly patient
dependent. Often, what is achievable for a particular treatment plan is only
determined after the treatment plan has been created and multiple iterations are
often required. AI tools can be applied to identify the achievable dose prescription
for a patient based on the particular patient’s anatomy [14], prior to treatment
planning, which would inform radiation oncologists and dosimetrists to develop a
clinically acceptable and achievable treatment plan with greater efficiency.
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