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Artificial Intelligence in Adaptive Radiation Therapy
2.2.2 Treatment simulation
Currently, CT is the gold standard imaging acquired for radiation therapy.
However, CT images are prone to metal artifacts from metal implants such as
metal screws in the spine and metal dental fillings, which obscure the imaging field of
view and clinically relevant structures, which can make contouring in these areas
challenging. While many commercial CT scanners have metal artifact reduction
softwares, these softwares improve the image quality but some artifacts are still
present and these softwares can also create their own types of artifacts [15]. AI
algorithms have been developed to generate metal artifact free CT images without
introducing other artifacts [16–18].
These CT im ages are often registered to other imaging moda lit ies, such as MRI
and PET, to assist contouring treatment targets, or previous RT CTs to assess dose
contributions from previous treatments in re-irradiation scenarios. The process of
aligning the two images during image registration can be challenging as the patient
may be in a different position in each scan; for example, the patient may have their
arms up in one scan versus arms down in another, or on a rounded couch top as in
diagnostic images versus a fl at couch top in CT simulations. These differences can
introduce uncertainties in the treatment planning process [19]. Commercially
available automatic registration tools have challenges when attempting to register
images of different modalities or in the presence of imaging artifacts. AI tools have
been developed to achieve better accuracy and robustness for image registration
[20, 21].
MRI has increasingly been utilized in RT, with MRI simulators accompanying
CT simulators becoming more common in RT departments. The improved softtissue visualization of MRI over CT enables radiation oncologists to better
distinguish tumors from surrounding OARs. However, these approaches require
an additional imaging scan and also introduce uncertainties due to image registration. MR-only clinical workflows have gained increasing interest as the patient
undergoes only a single MRI simulation and eliminates the MRI–CT registration
uncertainty. However, CT is still required for electron density information for the
dose calculation. There has been considerable work on creating synthetic CTs from
MRIs (CTs generated from only MRI information) [22], and even commercial
softwares are now available that have leveraged AI to create synthetic CTs for
disease sites such as the brain and pelvis [23, 24]. These AI tools use specialized MRI
sequences such as Dixon to generate synthetic CTs that have been reported to
accurately duplicate CT images, even in challenging areas such as the brain where
there may have been bone resection from surgery [25](figure 2.3).
2.2.3 Contouring
Contouring the treatment target and relevant OARs has historically been a very
manual process, requiring hours of work. Contouring the treatment target involves
utilizing the collection of medical information for the patient as well as an
understanding of the predicted progression of the cancer and any potential motion
that the target may undergo during treatment. The accuracy of the contours is
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 2.3. Example of synthetic CT (sCT) generation for three patients (a-c), with the MRI using the Dixon
sequence, sCT and CT. Blue region is the planning target volume (PTV) with the corresponding volumes on
the leftmost column, and the red box indicates the region of bone resection due to surgery. (Reproduced with
permission from [
25]. Copyright 2021 Springer Nature.)
important as the treatment plan dose distribution and analysis of this distribution is
dependent on the contours, and ultimately drives the dose delivered to the patient.
OAR contouring is often delegated to other role groups such as radiation therapists or
dosimetrists in the interest of efficiency and reducing workload for the radiation
oncologist. The radiation oncologist is ultimately responsible for reviewing and approving these contours, if they were not completed by themselves. A significant variation in
OAR contouring exists among clinicians, and the degree of variation is organ-dependent
[26, 27]. The under- or over contouring of an OAR can result in unnecessary increased
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Artificial Intelligence in Adaptive Radiation Therapy
dose to the critical organ or under treatment of the target. Analysis of the treatment plan,
assessing the dose to organs and treatment target, and their acceptability is heavily reliant
on the dose volume histogram (DVH), which is dependent on the OAR contouring.
Many research groups have developed AI tools to contour various organs throughout the
body such as in the head and neck region [28–30], thoracic [31] and abdominal organs
[32], and cardiac substructures [33, 34]. Several radiation oncology focused commercially
available AI-based auto contouring solutions are available such as Contour ProtegeAI+
by MiM (OH, USA), Limbus AI (Canada), Deep Learning Segmentation within the
RayStation Treatment Planning System (RaySearch Laboratories, Stockholm, Sweden),
and AutoContour from Radformation (New York, USA). These AI auto contouring
softwares have been developed for nearly all relevant OARs in RT, primarily on CT scans
but also for MRI for some disease sites, and have offered substantial time savings ranging
from 15 to 90 min depending on the disease site [35]. Despite automation, staff are still
required to review these contours after applying the AI tools, as the contours may not be
of sufficient accuracy for clinical use. AI tools have also been developed to automate QA
of OAR contouring to ensure consistency and standardization [36].
Variation in tumor segmentation can result in a decrease in the likelihood of tumor
control in the case of under contouring the target, and overdosing critical OARs in the
case of over contouring the target. Interclinician variation in tumor segmentation
exists among radiation oncologists, leading to a difference in treatment plan quality
and resulting in clinical outcomes such as survival [37–39]. AI auto contouring tools
for treatment targets have been developed by the scientific community for various
cancers such as nasopharyngeal carcinomas [40](figure 2.4), primary lung tumors [41],
oropharyngeal carcinomas [42], and hepatocellular carcinoma [43], and have shown
performance similar to that of a radiation oncologist.
While AI auto contouring tools offer significant time savings to render the RT
clinical workflow more efficient and improve reproducibility and standardization in
contouring, the accuracy of these contours are ultimately responsible to the
radiation oncologist. Therefore, these auto generated contours still need to be
reviewed by the clinical staff and radiation oncologist for accuracy and completeness
and, currently, still require some manual editing.
2.2.4 Treatment planning
The iterative manual process of treatment planning by dosimetrists can be intensely
time consuming and result in large variations in the quality of treatment plans [44].
There have been many approaches to automate the treatment planning process, such
as knowledge-based planning [45–47] and predicting objective function weights [48],
however, these approaches are usually designed for a specific disease site and are
limited in their ability to accommodate patient-specific challenges such as geometry
or previous treatment. As a result, the quality of the resulting plans often needs
further refinement by a dosimetrist.
Automating the treatment planning process with AI tools is of considerable
interest in the field of radiation oncology, and two general processes are involved.
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 2.4. Segmentation of a nasopharynx gross tumor volume shown on axial CT sl ices displayi ng the
manual segmentation (MS) by a radiation oncologist (fuchsia), and two AI algorithms: deep deconvolutional neural network (DDNN, blue) and a very deep convolutional network (VGG-16, green). The
DDNN algorithm outperformed the VGG-16 in segmenting the g ross t umor volu me. (Repro duced fr om
[
40]. CC BY 4.0.)
First, given the patient image (e.g. CT) and contours, an optimal dose distribution is
predicted, then the appropriate linac parameters to achieve that dose distribution are
identified. These AI tools use algorithms that have been trained with previous
treatment plans, learning the relationship between patient geometry and achievable
dose distributions with trade-offs. AI tools for automated treatment planning have
been developed for various disease sites such as prostate [14, 49], pancreas [50, 51]
(figure 2.5), and head and neck [52, 53] cancers. The machine learning treatment
planning module in the RayStation Treatment Planning System (RaySearch
Laboratories, Stockholm, Sweden) was the first commercially available treatment
planning system to have an AI tool available for automating treatment planning
with a model for head and neck cancer [54]. This AI tool comes with pre-trained
models from other institutions as well as the ability for a particular clinic to train
their own model using their own data.
In addition to the manual treatment planning process by dosimetrists, the dose
calculation in the treatment planning software can be time consuming. Typically,
dose calculation algorithms have a tradeoff between efficiency and accuracy,
where the more efficient algorithms are less accurate. AI tools have also been
developed to increase the speed of dose calculation algorithms without sacrificing
accuracy [55].
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 2.5. Example of a fluence map benchmark plan (A) and model-predicted (B) for pancreas SBRT using
an AI algorithm, with the difference between the benchmark and model-predicted fluence shown in (C), and
the corresponding treatment plans (D), (E), and difference in dose (F). (Reproduced from [
51]. CC BY 4.0.)
2.2.5 Quality assurance
A significant portion of a medical physicist’s time is spent performing and overseeing
QA tasks. These tasks exist to ensure patients are receiving the intended treatment,
identify mistakes that may have been made, and ensure that the technology involved
in RT is performing as expected. These QA tasks are often very time-consuming
manual repetitive processes. Every treatment plan has a secondary dose measurement performed on the plan, either through a secondary dose calculator, or through
a physical dose measurement delivered by the linac to a phantom, or sometimes
both. The physical dose measurements for every patient plan can be intensely time
consuming, and the majority of plans pass dose measurement. When a plan fails this
QA step, a physicist will investigate the cause of failure, whether it be the plan itself,
the performance of the linac, detector malfunction or user error. AI tools have been
developed to analyze treatment plans, and predict QA passing rates and possible
sources of failure [ 56–59]. This approach potentially eliminates the need for physical
dose measurements of individual plans, increasing efficiency and decreasing the
resources necessary to measure these plans.
While this approach of reducing patient-specific QA measurements by using AI
tools eliminates the additional QA of the linac that is provided by physical dose
measurements, routine machine QA is performed at regular intervals (e.g. daily,
monthly, annually) to assess linac performance. These routine machine QA
measurements also require significant effort and time, where often the QA tests
pass. AI tools have been developed using longitudinal data to predict trends in the
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Artificial Intelligence in Adaptive Radiation Therapy
linac output as a tool to alert the need for preventative action [60]. AI tools have also
been developed to potentially improve TPS modeling of the linac, such as assisting in
reducing MLC positional errors [61] found between the treatment plan and delivery
by incorporating predicted MLC positions into the TPS. In addition, beam
modeling in the TPS can have adjustable parameters, for example, in the Eclipse
TPS (Varian Medical Systems, Palo Alto, CA), the transmission factor and
dosimetric leaf gap are adjustable MLCs parameters. Inaccuracies in these parameters will impact the dose distribution and contribution to disagreement between the
TPS and delivered dose. AI tools have been developed to detect and classify errors in
these MLC modeling parameters to improve the accuracy of beam modeling in the
TPS [62].
2.2.6 Treatment delivery
Modern linacs are equipped with kV imaging and CBCT for image guidance.
Compared to conventional CT, CBCTs often have more severe imaging artifacts
which can obscure the region of interest for patient set-up and potential use of
CBCT for adaptive RT. AI algorithms have been developed to improve the image
quality of CBCT to ultimately improve the accuracy of patient set-up [63] and
enable adaptive RT [64]. Acquisition of CBCTs can take tens of seconds, and be
subject to motion such as respiratory and internal motion. AI tools have been
developed for CBCT to reduce scan time and exposure dose by using high-speed
CBCTs with the AI tools to generate images with image quality suitable for imageguided RT [65].
Respiratory motion is one type of motion that is of concern in RT, particularly
for thoracic and abdominal cancers. While many different motion management
strategies exist, one strategy utilizes an external surrogate placed on the patient’s
chest, such as the Varian RPM system. This system follows the motion of the
surrogate throughout the patient’s breathing cycle to indicate when the patient is in
the correct breathing phase and indicate when the radiation treatment should be
delivered. There is an underlying assumption that the position of the surrogate
correlates directly with the movement of the tumor, however, this assumption fails
to capture the intricacies of tumor motion with respiration. AI tools have been
developed to correlate tumor position with the motion of external surrogates and
also predict tumor position for irregular breathing patterns and complex tumor
motion [66] and address latency between the external surrogate and radiation
delivery by the linac [67, 68].
2.2.7 Response assessment and toxicity management
Response to RT is typically assessed by evaluating the response of the tumor in
terms of the change in size based on the response evaluation criteria in solid tumors
[69] in medical images. AI algorithms have the potential to evaluate additional
imaging features such as texture and intensity, potentially providing greater
predictive power to cancer-specific outcomes. Studies have investigated the use of
AI algorithms on pre-, on- and post-treatment medical images to predict patient
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Artificial Intelligence in Adaptive Radiation Therapy
outcomes such as overall survival and development of distant metastasis and
locoregional recurrence for various cancers such as bladder [70, 71], lung [72–74],
and pancreatic [75] cancer. The overall goal of these applications of AI in response
to assessment of RT is to provide more information to the physician to enable
personalized treatment and earlier interventions to improve outcomes for cancer
patients.
Evaluation of a patient’s response to RT is not only the response of the tumor but
also the response of the surrounding critical OARs. One challenge that can be
encountered is the presence of radiation-induced toxicities, which can make the
detection of disease recurrence challenging. For example, in lung cancer patients, the
presence of radiation-induced fibrosis and local tumor recurrence can look similar
on CT scans and be overlooked. AI algorithms have been developed to analyze
imaging features in medical images to assist physicians in distinguishing between
radiation-induced tissue damage and cancer-specific outcomes [76]. Furthermore,
studies have investigated the potential of AI tools to predict the severity of toxicities
associated with RT such as acute dysphagia [77], xerostomia [78], pneumonitis [79,
80] and rectal toxicities [81]. These tools could enable physicians to predict toxicities
prior to RT and lead to anticipatory management before treatment and/or
secondary prevention of toxicities after treatment has been delivered.
2.3 Summary
Overall, the incorporation of AI tools in RT has the potential to improve efficiency
in the clinical workflow, which has already begun with the use of AI-based auto
contouring tools. Further incorporation of these tools will increase the standardization of clinical care and also has the potential to improve clinical care by
assisting clinicians as decision-support tools by potentially providing more insight
and ability to comprehend the large amounts of patient data available to guide
treatment decisions. The increase in the use of AI tools in the clinic has the potential
to ultimately change the scope and workload of the role groups involved, by
reducing workloads to focus on and identify the most important and clinically
relevant issues in improving patient care.
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