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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5525_Библиотеки_им_академика_М_И_Перельмана.pdf
Artificial Intelligence in Adaptive Radiation Therapy
In many clinics, once an inverse plan is generated, patient-specific quality
assurance (PSQA) is typically conducted through either measurement-based or
calculation-based methods, a practice also observed in pre-plans. Measurementbased QA involves transferring and recalculating the plan on a QA phantom (film/
chamber, 2D array, 3D array) and performing a gamma analysis between the
measured and expected dose distribution [18]. Although measurement-based QA is a
common and reliable practice, efficiently identifying catastrophic delivery errors, it
has limitations. Critics argue that in most instances, IMRT QA passes, and it is
inefficient in detecting subtle plan delivery errors [19]. As covered later, it is also
impractical to remove the patient during online adaptive to QA the plan. However,
this approach faces practical challenges. Performing measurement-based QA during
online adaptive therapy, where the goal is to shorten treatment duration, is
impractical. This limitation will be further detailed in section 7.1.4, where alternatives for online adaptive therapy QA will be explored.
7.1.3 Online imaging and daily re-planning
During online ART it is crucial to acquire daily images suitable for contouring and
planning. For x-ray guided ART, cone-beam CT (CBCT) or fan beam CT images
are typically acquired while in MR-guided ART. In the case of MR-guided ART, a
daily MRI is acquired, offering additional flexibility. Different weighted MR images
can be acquired, providing users with enhanced contrast to various OARs or targets.
For both workflows, following the daily image acquisition, contouring of anatomy
occurs.
One common challenge in most online ART workflows, excluding fan beam CT,
is the inability to directly employ daily images for dose calculation. Currently, this
issue is addressed by generating a synthetic CT or utilizing density overrides for dose
calculation. In x-ray guided ART, the reference planning CT undergoes automatic
deformation using the daily CBCT. In MR-guided ART workflows, the system
extracts the mean electron density of each delineated organ from CT and applies a
bulk density override based on the daily MR contours. One system offers two
solutions: direct dose calculation on CT through rigid/deformable registration with
the daily MR or utilizing bulk density overrides. Another system has the capability
to directly calculate the dose on a higher quality CBCT.
For online contour delineation, several AI-based approaches have been developed that will be covered in more detail in later sections of the chapter [20]. Various
delineation capabilities are also at the disposal to enhance workflow efficiency.
Many systems can propagate contours onto the daily image through either rigid or
deformable registration, offering a head start in the contouring process. As a
common practice, targets are often rigidly propagated, while OARs undergo
deformable propagation. Furthermore, AI-based methods can be employed for
initial contouring of targets and OARs on the daily image, providing an additional
layer for review and editing. Before progressing to the planning phase, contours,
especially those of targets, typically undergo approval by the physician.
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Artificial Intelligence in Adaptive Radiation Therapy
Various advanced systems strive to enhance optimization efficiency using distinct
approaches in their online planning methodologies. One such system employs an
intelligent optimization engine (IOE) to facilitate online re-optimization, acting as a
mediator between the human planner and the optimization algorithm. This system
utilizes a predefined set of clinical goals and priorities, creating a structured online
re-optimization process in line with the reference strategy.
Another system offers two types of online adaptation: adapt-to-position (ATP)
and adapt-to-shape (ATS). The system maintains a fixed isocenter and couch
position, restricting user adjustments during the online process. ATP compensates
for this constraint through a segment aperture morphing (SAM) algorithm, adapting
the multi-leaf collimator (MLC) based on the beams’ eye view of the old and new
target projections. In this workflow, the user can only register reference contours to
the daily image without alterations [21]. Conversely, the ATS workflow allows
changes to the target and OAR, followed by warm-start optimization based on the
pre-planning strategy. Unlike the IOE system, users can modify the optimization
strategy on-the-fly.
Lastly, another system provides several online optimization strategies, including
BOT Optimize and Optimize Dose or Add Segments. BOT Optimize involves basic
segment weight optimization, while Optimize Dose utilizes the reference strategy,
initiating a new optimization or continuing from a previous starting point. This
system employs an ultra-fast Monte Carlo and optimizer, enabling multiple
iterations over the expected timeframe of online ART to achieve the optimal plan
for the day’s anatomy. The specific AI-related capabilities of these systems are not
disclosed.
7.1.4 Quality assurance
As previously mentioned, it is impractical to remove the patient from the table to
perform a measurement-based QA. The emergence of calculation-based PSQA
utilizing independent dose calculation engines and treatment unit log file analysis is
very timely and well suited to QA of online adapted plans [22–26]. For calculationbased QA, in lieu of a physical measurement the planner can export the plan to an
independent second check (often Monte Carlo based) where gamma analysis can be
performed between the treatment planning system calculated dose and the recalculated dose. For log-based PSQA, the treatment machine log files that record the
location of multi-leaf collimators, gantry and collimator locations and can be used
to compare between expected and actual positions of the plan [27]. It is then possible
to reconstruct a 3D dose following treatment delivery and compare the delivered
dose distributions [28, 29]
Prior work has been completed that compares measurement-based and calculationbased PSQA methods. Commercially available platforms utilize an independent
convolution–superposition dose calculation algorithm to verify the TPS. An alternative option is utilizing Monte Carlo based methods [30–32]. After plan review and
QA, typically a physicist and physician will sign off on the treatment plan and QA.
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Artificial Intelligence in Adaptive Radiation Therapy
One area where AI can be utilized is in the decision-making process to decide whether
to adapt or not.
7.2 AI-driven ART
Technically speaking, artificial intelligence (AI) broadly refers to any intelligence
achieved by computer systems in contrast to human intelligence. Deep learning
(DL), as a subtype of techniques within the scope of AI, has achieved tremendous
success in a wide spectrum of different areas including medicine. With recent
increasing interest, AI and DL have been used interchangeably in many scenarios.
This practice is followed in the rest of this chapter unless mentioned otherwise.
Following the order of the major steps in the ART workflow as listed previously,
we will introduce the detailed applications of AI techniques in each step, and how
these AI techniques could improve the current ART workflow.
7.2.1 Simulation
As the crucial initial step of the ART workflow, simulation can benefit from the
emerging AI techniques in many different aspects. One of the most popular
applications of AI algorithms in simulation is CT image synthesis. A number of
studies have been carried out recently to convert images acquired from other
modalities, e.g. magnetic resonance images (MRI) [33–37], or on-board CBCT [7,
38–41], to synthetic CT (sCT) images which can be employed to generate the initial
plan for ART. Specifically, AI-based sCT generation algorithms based on MRI have
the potential to enable the MR-only simulation workflow [42, 43] for RT treatment
planning, eliminating the necessity of CT simulation. On top of the obvious benefit
of simplified workflow and reduced imaging dose, AI-based sCT images also share
the identical anatomy with the MR images. The treatment target and OARs
delineated from MR images can be directly utilized in sCT, removing the
uncertainty introduced by anatomical discrepancy between simulation MR and
CT images. CBCT-based sCT generation permits both online and offline re-planning
directly based off the most recent patient anatomy from CBCT without requiring resimulation. Given that CBCT is the most widely available on-board imaging
guidance, AI-based sCT generation holds great potential of realizing ART on a
wide range of conventional treatment platforms, particularly current c-arm linacs.
Generating sCT directly based on diagnostic scans can completely eliminate the
currently required extra step of simulation, introducing a brand new concept of AIdriven virtual simulation. A pioneering study [44] has been conducted to develop
and implement virtual simulation for hippocampus sparing whole brain treatment
on the x-ray guided ART system. This virtual simulation workflow not only saves
the overall cost and time from the patient side, but also helps clinics to release the
heavy scheduling and scanning burden on their CT simulators. It opens up the
possibility to substantially reduce the waiting time from diagnosis to radiation
treatment leading to further benefit in treatment quality and outcome [45].
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Artificial Intelligence in Adaptive Radiation Therapy
7.2.2 Pre-planning
Planning in ART commonly consists of two stages, i.e. the initial, reference or preplanning stage, and the re-planning stage, either online or offline. The pre-planning
process of ART is similar to that in the conventional workflow, but with some key
difference covered previously. Re-planning, on the other hand, has a more stringent
constraint on planning efficiency as it is highly desired to complete re-planning and
deliver treatment prior to further anatomical changes or patient movement. AIbased algorithms developed for conventional treatment planning are applicable to
improve the planning quality and efficiency of ART pre- and adaptive planning
while specifically designed approaches may provide better performance.
AI-based auto-segmentation. Both the pre- and adaptive planning process of ART
starts with defining contours of the treatment target and OARs in planning images,
which typically takes extensive manual work from both physicians and planners. AIbased automatic segmentation algorithms have been designed and are now implemented frequently in clinical practice to reduce the manual contouring time.
Substantial research efforts have initially been devoted to automate the OAR
segmentation process since it is tedious, time-consuming, and relatively more
straightforward compared to target delineation, which is often performed by
physicians only, with the target appearing markedly different from patient to
patient. Numerous AI segmentation tools have been developed to perform automatic OAR segmentation for different body regions or disease sites. Head and neck
(HN) cancer [46–49] is commonly considered as a challenging site to contour since it
involves more than 20 OARs of significantly distinct shapes and volumes. These
studies demonstrated equivalent human-level accuracy of the developed AI-based
methods in defining most of the OARs for HN cancer. Similar performance was also
observed for other sites. Comprehensive clinical evaluations have shown that AIgenerated OAR contours with minor manual edits were able to greatly reduce the
time and inter-observer variations in OAR contouring [50, 51]. Further improvement in performance is warranted for some challenging OARs with complex
topologies, e.g. the sigmoid colon [52–54].
Novel AI-based tools [55–61] have been developed for automatic target delineation. Despite their encouraging performance, treatment targets in RT are still
primarily contoured by physicians. One reason is that treatment target definition
frequently needs to account for information from multiple resources including
diagnostic images, planning images, as well as the patient specific clinical characteristics extracted from radiology and pathology reports, which is beyond the capability
of the most current AI models. Further development on novel multi-modal
algorithms incorporating state-of-the-art segmentation models jointly with the
emerging large language models (LLMs) [62, 63] are highly desired.
AI-based automatic segmentation algorithms [64–68] have also been designed
specifically to serve the purpose of online ART re-planning. These methods often
take advantage of the readily available contours from the initial planning stage as
well as the previous ART sessions to enhance the segmentation performance for
successful adaptive planning. This is a relatively new topic and continued research
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Artificial Intelligence in Adaptive Radiation Therapy
efforts are required to fully utilize the rich patient-specific information embedded in
the contours and images available.
AI-based dose prediction. Predicting dose for high-quality treatment plans
provides explicit objectives for both pre- and adaptive planning of ART, and hence
can substantially reduce the time and efforts spent in the planning process. Since its
feasibility has been demonstrated in several pioneering studies [69–73], accurate
prediction of three-dimensional (3D) dose distribution uniquely enabled by AI
techniques have become an active research area [15, 74–81]. Several recent studies
have been conducted to further tailor the algorithms to fit the novel treatment
paradigm of ART specifically. For example, an intentional over-fit algorithm was
developed to adapt a population-based dose prediction model to a specific patient
for enhanced ART dose prediction performance [82]. This method was then further
extended to use training data from the initial plan and the first ART plan of only a
single patient to predict the patient-specific dose for subsequent ART sessions [83].
Motivated by the success of AI-based dose prediction, preliminary studies have
been performed to further model physician preference in treatment planning [84, 85]
using AI. Using prostate cancer stereotactic body RT as a test bed, this study [84]
illustrated the feasibility of training an AI-model to predict the probability for a given
plan being approved by physicians along with suggestions in plan improvement. These
models representing physician treatment intent can be used to guide the treatment
planning process and pre-check the generated plan, improving the overall efficiency,
consistency, and quality of treatment planning.
AI-based auto-planning. Treatment planning, aimed at designing personalized
high-quality plans, is typically accomplished jointly by human planners and
physicians in a time-consuming and labor-extensive trial-and-error manner. Given
the time constraints in clinical practice, suboptimal treatment plans can often be
accepted [86–88], deteriorating treatment quality [89]. It is highly desired to fully
automate and accelerate the planning process, particularly for the ART workflow,
which has far more stringent requirement on planning efficiency.
The goal of treatment planning is fundamentally different from dose prediction
and the reason is two-fold: first, treatment planning directly tackles the machine
parameters required to deliver the designed high-quality plan, while dose prediction
models only estimate a dose distribution. Realizing the predicted dose on a
treatment machine still needs to go through the planning process. Second, it is
possible that the predicted dose is not feasible to achieve on a treatment machine,
while treatment planning guarantees the feasibility by taking realistic machine
specifications and constraints into account in the planning process.
Extensive research efforts have been devoted to develop AI algorithms for direct
treatment planning. A typical approach is to incorporate AI models to predict the
fluence map based on the planning images as well as the treatment targets and OAR
contoured for the patient [90–96]. Post-processing is further required to determine
the machine parameters from the predicted fluence map in order to make it
deliverable. Note that a discrepancy often exists between the predicted and the final
fl
uence map calculated based on machine parameters as the machine constraints and
limitations are not explicitly modeled. Another group of methods incorporate
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Artificial Intelligence in Adaptive Radiation Therapy
reinforcement learning [97–99] (RL) techniques to establish AI planning agents
[100–108] for automatic planning. The training process of RL-based AI agents is
very similar to the trail-and-error strategy of humans. The established AI agents can
automatically operate the TPS to generate high-quality treatment plans, in lieu of
human planners. These plans are likely deliverable plans since they are directly
optimized and calculated using the TPS, identical to the way of generating clinical
treatment plans.
7.2.3 AI for delivery
Patient-specific quality assurance for online adapted plans has traditionally relied
heavily on calculation-based methods. While some studies have supported the
clinical feasibility of such approaches, there remains a gap in understanding the
correlation between the complexity of adapted plans and the quality of their
delivery. The advent of AI models presents a promising avenue for more efficient
real-time predictions without the need for extensive measurements and resources.
Initial efforts in patient-specific quality assurance focused on conventional IMRT or
VMAT, leveraging treatment plan complexity and linear accelerator performance
metrics to directly predict the gamma passing rate [109–114]. Building upon this
foundation, Hirashima et al took a pioneering step by incorporating radiomics
features extracted from dose distribution. This addition helped quantify plan
complexity, and machine learning techniques were employed to further enhance
prediction accuracy [115]. These advanced methods, initially applied to conventional
therapies, hold great potential for adaptation to the dynamic landscape of online
adaptive therapy. By integrating radiomics and machine learning, these models offer
a more comprehensive approach to predict the deliverability of adaptive plans,
representing a notable advancement in the field of patient-specific quality assurance
for adaptive therapy.
7.3 Outlook and future directions
ART is currently in its nascent stage, with its application primarily confined to
specific clinical scenarios or patients with the most pressing needs. Its full potential is
yet to be realized, and the trajectory of its advancement is poised for a significant
leap with the integration of AI. Throughout this chapter, we have delved into the
ways in which AI can markedly enhance the efficiency and precision of ART—two
critical domains that must undergo substantial improvements to elevate ART to the
status of standard care for all patients.
Now, let us explore the frontier of possibilities where AI could usher in the
ultimate evolution in both efficiency and precision—real-time ART. The concept of
real-time ART envisions a dynamic and adaptive treatment approach that seamlessly adjusts to the immediate physiological state of the patient during each session.
AI is anticipated to play a pivotal role in orchestrating this real-time adaptation,
ensuring that treatment strategies are continually refined based on the most up-todate information.
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Artificial Intelligence in Adaptive Radiation Therapy
Beyond real-time adaptation, another frontier emerges in the potential to tailor
patient treatments based on both the physics of radiation delivery and the functional
response of individual patients. This concept envisions a level of personalization that
extends beyond anatomical considerations to account for the unique physiological
responses of each patient. AI is poised to become a cornerstone in this paradigm,
contributing to the dynamic adaptation of treatment plans based on the interplay
between the physical attributes of radiation and the specific functional responses
exhibited by individual patients.
In essence, as AI continues to advance, its integration into the realm of ART
holds the promise of not only enhancing the efficiency and precision of existing
practices but also catalyzing the evolution of real-time adaptive therapies and
personalized treatments. The journey towards realizing the full potential of ART is
intricately entwined with the advancements AI brings to the field, marking a
paradigm shift towards a future where adaptive radiation therapy becomes a
standard and tailored approach for all patients
7.3.1 Real-time ART with AI
Having observed the current applications of AI in ART, it is worth considering how
AI could enable real-time ART. In this dynamic approach, there is no single
adaptive plan; instead, the system continuously adapts during the course of treatment, accounting for any changes in anatomy as treatment is delivered.
Prior to the commencement of real-time ART, a pivotal phase involves a
substantial refinement of the pre-treatment workflow, benefitting both the patient
and the physician. This transformative process initiates with the assistance of AI,
which collaborates with the physician in reviewing an exhaustive set of available
data, encompassing imaging, pathology, and patient history. The objective is to
recommend a comprehensive course of treatment, delineating the optimal modality,
radiation dose, and fractionation schedule.
In the subsequent steps, AI takes on the responsibility of contouring the target
and pertinent OARs by leveraging the highest quality image available for each
organ. These contours are amalgamated onto a synthetic CT, meticulously generated to replicate the treatment position. Following this, a collaborative review
involving both the physician and physicist ensures the accuracy and suitability of the
scan and contours.
Moving forward, AI takes a proactive role in generating a pre-plan designed for
deliverability, accompanied by specific optimization goals tailored for real-time
adaptive planning. To fortify the robustness of the pre-plan, AI continuously refines
its optimization goals through the generation of a range of daily images. These
images serve as testing grounds for the optimization goals, allowing AI to adapt and
update them based on the evolving nuances in the patient’s anatomy.
In essence, the integration of AI into the pre-treatment workflow not only
streamlines decision-making processes for the physician but also forms the foundation for subsequent real-time adaptive planning. Once AI generates a deliverable
pre-plan along with specific optimization goals, it employs its acquired knowledge
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Artificial Intelligence in Adaptive Radiation Therapy
from the pre-plan phase and continually adapts to changes observed in daily images.
This iterative process ensures that AI refines and updates its optimization goals
dynamically, fostering robustness in the adaptive planning phase.
At treatment, 4D imaging would occur on which AI would predict the dose that
will be delivered today for review. This prediction would be based on the daily
anatomy imaged, and both the observed motion in the 4D image, and an AI
expanded range of motion to provide a given certainty of coverage. Once the
expected dose distribution is approved, treatment would start.
Real-time ART could take many forms, one of which is imagined here. From a
2D image, AI would predict the full 3D image, contour target and OARs within the
field of view and re-optimize the plan, accounting for the dose already delivered,
knowing which beam angles were still available, and which of those could provide
better chances to deliver dose to the target and avoid OARs. After completion of one
arc, AI would determine if a second arc is needed to paint in any remaining areas of
coverage, using information from the first arc delivery to find the optimal gantry
angle and motion phase to deliver this dose.
Real-time adaptation represents the pinnacle of dose delivery, potentially
eliminating the requirement for patient immobilization. Given its dynamic nature,
the implementation of advanced AI algorithms becomes imperative for seamless
delivery. Upon completion of treatment, AI assumes the role of furnishing a
comprehensive summary of the delivered dose, encompassing both the current
session and the cumulative dose to date, coupled with an analysis of anatomical
changes.
7.3.2 Dose escalation and functional adaption with AI
The decision-making process for dose escalation will then rest in the capable hands
of both AI and the physician, strategically determining when such escalation is
justified. This pivotal decision will draw insights from the unique combination of
disease type and patient history, accentuated by the specific response of the
individual to the treatment. Here, AI emerges as a valuable ally, contributing
nuanced insights into both the physical and functional changes indicative of
treatment response, with a distinct emphasis on the significance of functional
alterations—an aspect that holds paramount importance in the pursuit of optimal
treatment outcomes.
Tools are already available to highlight changes in physical characteristics of
tumors between images [116]. AI will be able to review and collate the data to
identify relevant changes, requiring an increased target dose, or allowing treatment
to be shortened. Incorporated into this decision will be information about adjuvant
treatment the patient is receiving, particularly immunotherapy. AI may be able to
differentiate the response of the tumor to the different modalities and help the
physician decide how to proceed. In addition, AI will predict the best time to deliver
the therapies, where gaps in treatment may be beneficial to allow the tumor to
respond to treatment, in a manner described by PULSAR [88].
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Artificial Intelligence in Adaptive Radiation Therapy
Functional imaging, encompassing both positron emission tomography (PET)
and quantitative magnetic resonance imaging (MRI), has already made substantial
contributions to the pre-planning stage in radiation therapy. Furthermore, its utility
has extended to dynamic use during treatment adjustments, as evidenced in trials
such as the PET Lung trial [117]. The natural progression in this trajectory is
functional adaptation, heralding a transformative phase in cancer treatment. This
could be based on daily PET, by quantitative MR sequences on MR-guided
adaptive machines, or by importing separate PET and MR images into x-ray
guided systems to help with the adaption. A key realization is that a tumor’s
reduction in size may not necessarily correlate with a decrease in its core
functionality. In instances where the core remains highly functional, it could signify
the need for an increase in dose rather than a reduction due to conventional
measures such as tumor shrinkage. This nuanced understanding of both physical
and functional tumor characteristics necessitates the intervention of AI to determine
the optimal course of action.
The envisaged coupling of real-time adaptive therapy with AI-generated treatment predictions based on functional imaging marks a monumental leap forward in
patient care. This integration holds the promise of revolutionizing cancer treatment
by harnessing the dynamic capabilities of AI to navigate and adapt to the everevolving physical and functional aspects of tumors. The anticipation and development of sophisticated AI tools to actualize this vision underscore a progressive and
promising frontier in the realm of adaptive radiation therapy.
7.4 Summary
In summary, artificial intelligence (AI) serves as a catalyst for advancing adaptive
radiation therapy (ART) into mainstream clinical practice. By addressing key
challenges such as workflow efficiency, planning accuracy, and resource optimization, AI empowers clinicians to deliver more personalized and effective treatments.
The integration of AI into ART workflows not only reduces the complexity of realtime adaptation but also enhances decision-making through predictive modeling and
functional imaging insights. The chapter underscores the potential of AI-driven
ART to achieve superior treatment outcomes, paving the way for innovative,
patient-centered care in radiation oncology.
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