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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
which may be used to adapt the treatment plan while the patient is on the table in
cases where there is large interfraction movement or deformation of critical
structures. Plan adaption allows for the target and organs at risk (OARs) to be
modified or re-contoured and may involve fluence re-optimization using the original
objectives or full re-optimization using new planning objectives [16].
Other low-field MR-linac devices are currently in development and, in the future,
may become more prevalent in the clinic. One example of such a system is the
MagnetTx Aurora-RT which received FDA premarket clearance in 2022 [17] and
treated its first patient in 2023 [15]. The Aurora-RT is a 0.5 T MR-linac which
utilizes an open bore in-line design to mitigate the electron return effect [15]. The
Aurora-RT is starting to image patients as part of an ongoing clinical trial
(NCT04358913) [18, 19] and data on clinical experience with this system should
become available in the near future. Since there is limited clinical data available on
this system, this chapter will focus on the two currently available clinical devices: the
high-field Elekta Unity and low-field ViewRay MRIdian MR-linac devices.
16.3 MRI-guided ART workflow
16.3.1 Offline MRI-guided ART workflow
Offline adaptive treatment is possible with MR-linac technology, and the workflow
is similar to offline adaptive workflows for conventional linear accelerators where
the adaptation takes place between treatment fractions. Imaging acquired at the
time of treatment is assessed to determine if adapting the plan would be useful in
order to maximize the dose to the target and minimize the dose to surrounding
tissue. Typically, the decision for adaptation is based on a pre-defined clinical
threshold for plan performance and is dependent on the decision of the treating
physician. Assessment of the base plan may be done manually or by using complex
automated tools to estimate the cumulative dose that would result from choosing
whether to adapt the base plan [20]. The goal of adaptive treatment is to improve
clinical outcomes by modifying the plan to account for changes in the target or OAR
size, shape, and function or changes due to patient weight loss or gain [20].
Considering MRgRT using an MR-linac, the image acquired at treatment is an
MRI which alone cannot be used for planning since it inherently lacks the electron
density information necessary for dose calculation. Because of this, the MR-linac
planning workflow can be divided into a CT-sim workflow or MRI-sim workflow.
The options are to either use a CT-sim image for planning which is registered to
secondary MR images (CT-sim workflow), or to create a synthetic CT (sCT) image
from the acquired MRI-sim which does not require a CT-sim to be acquired (MRIsim workflow). If the image acquired during treatment is of sufficient quality, it may
be used as the basis for adaptive planning using one of these workflows.
Alternatively, a re-simulation CT or MRI may be acquired for the patient.
After an appropriate image for dose calculation is acquired, the target and OAR
contours may be adjusted as needed. The base plan may be recalculated on the new
image or if the recalculated plan is not deemed clinically acceptable, a newly
optimized plan may be created based on the updated image and contours [20]. The
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Artificial Intelligence in Adaptive Radiation Therapy
optimization process for offline adaptive planning does not differ from conventional
treatment planning. Following plan creation, quality assurance (QA) must be
performed for that plan, in accordance with standard workflows for plan QA prior
to plan delivery. Since this is an entirely new plan compared to the base plan, it
should be treated as an independent plan for plan review and QA purposes. The
delivery of the offline adapted plan does not differ from the delivery of a conventional plan. Considering the timescale of offline adaptive treatment, it is not suited to
correct for anatomical changes that occur at a high frequency (occur within a
fraction) but more for gradual changes that may occur once or infrequently over the
entire course of treatment [20].
16.3.2 Online MRI-guided ART workflow
The largest difference between online and offline adaptive treatment is the timescale
of the process. While offline adaptive plans are adjusted between treatment sessions,
over the course of a few days, online adaptive treatments take place entirely during
the treatment session, meaning online ART can account for both systematic and
random variations in anatomy [21]. Imaging of the patient, assessment of the need
for ART, re-planning, and plan QA all occur while the patient is on the table for
online ART [20]. Because of this, the workflow is compressed into a short time frame
on the scale of minutes. Online ART requires specialized treatment planning systems
highly integrated with the treatment delivery unit as well as necessary time allocation
to ensure the adaptation can be done during the treatment slot and the availability of
physicians, physicists, dosimetrists, and therapists trained in the ART workflow
must be accounted for [20]. Since online adaptation has these specific requirements,
it is best used in situations where the need to adapt is predictable and known ahead
of treatment initiation [20]. For example, sites in the abdomen and pelvis prone to
daily anatomic changes are good candidates for online ART [20]. It is likely that
online ART will be needed multiple times over the course of treatment and, as such,
it is important that the base plan is robust and employs straightforward optimization
techniques and structures for efficient re-planning during treatment. For example,
optimization structures such as rings and tuning target structures may be avoided
and structures far from the target should not be used for plan optimization [22].
Equal importance must be placed on any OARs coplanar to the target as they may
move closer to the target and high dose region at the time of treatment [20, 22]. The
specifics of online MR ART workflows for high-field and low-field systems are
discussed below.
16.3.2.1 High field online ART workflow
A diagram representing the workflow for online ART using the Elekta Unity system
is shown in figure 16.3. The online ART workflow begins with daily MR assessment.
The patient is first cleared for MR safety, and then set up on the treatment couch,
replicating the simulation position. A 3D MR image is acquired for adaptive
treatment planning. Upon completion of imaging acquisition, the images are
automatically transferred over to the online Monaco treatment planning system.
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 16.3. Diagram depicting the online adaptive workflow of the Elekta Unity system.
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Once the MR image is imported into online Monaco, an automatic rigid registration
is initiated, which allows for translation only. Manual registration can be performed
to adjust the fusion result. Once the registration has been reviewed and approved,
plan adaptation is initiated.
The treatment plan is adapted every fraction. The Elekta Unity system offers two
different plan adaptation approaches: adapt to position (ATP) and adapt to shape
(ATS) [23]. After image fusion, the physician will choose an adaption workflow
based on the anatomical change of that day. The consideration may include tumor
size and morphological shape changes, the proximity of OARs to the target
compared to simulation, or insufficient ATP plan quality if ATS is not the first
choice, etc. In the ATP workflow, the treatment iso-center is moved to a new
location (virtual couch shift) based on the image fusion. The treatment plan is then
recalculated or re-optimized based on the simulation CT using one of the four
different adaptation algorithms provided by the Monaco TPS: original segments,
adapt segments, optimize weights, and optimize shapes. This process is equivalent to
traditional IGRT approach because the plan adaptation does not consider the daily
anatomical variations. Instead of moving the couch, the plan iso-center is moved to
the treatment day’s position and plan is re-optimized or recalculated based on the
anatomy at simulation [24, 25]. In the ATS workflow, a deformable registration
between the simulation CT and daily MR scan is performed after the image fusion.
All contours are deformed or rigidly mapped from simulation CT scan to the daily
MR scan based on the user choice. Contours are then reviewed and edited if needed
by the physician. The entire reference plan is then copied to the MR scan, including
beam arrangement, IMRT constraints, planning goals, etc. A synthetic CT for dose
calculation is created via bulk density override based on the contours on the MR
scan and electron density information obtained from the simulation CT scan. The
treatment plan is re-optimized either from fluence, in which the segments in the
reference plan are discarded and fluence is re-optimized, or from segments based on
the reference plan. Compared to optimization from segmentations, optimization
from fluence produces a completely new plan with slightly longer plan optimization
time. It is recommended for substantial contour changes and/or IMRT constraint
updates.
Dosimetric criteria can be customized to each treatment site or treatment
template based on planning derivatives and they are initially set during reference
planning and adjustable during adaptive planning. Upon the completion of plan
adaptation, the dose–volume histogram (DVH) of the adaptive plan will be
compared with the original reference plan for evaluation. Once the adaptive plan
is approved, the plan will go through an independent secondary monitor unit (MU)
verification for plan consistency. A 3D independent dose calculation and gamma
comparison is preferred over point dose check [9].
A verification 3D MR scan can be acquired after the adaptive plan is approved.
The adaptive plan dose can be overlaid on the new scan to verify that the current
patient position is still valid for the adapted plan. Before beam-on, the therapist will
verify the MU of each beam and other beam parameters to ensure the plan transfer
is completed correctly and the correct plan is being delivered. During beam-on, the
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motion monitoring system can be turned on to monitor the patient motion
qualitatively. Therapists may interrupt the beam-on if significant patient motion is
observed. The latest CMM system provides automatic beam gating and intrafraction drift correction [12].
16.3.2.2 Low-field online ART workflow
The workflow for online ART using the low-field MRIdian system, depicted in
figure 16.4, begins with initial set-up of the patient and acquiring a volumetric MRI
for patient alignment [22]. Image-guided set-up is based on the daily MR image and
couch correction is applied based on registration of the daily MRI with the planning
image [26]. Deformable registration is then performed to register the daily MRI to
the primary planning image in order to transfer electron density information [16].
The original contours can either be rigidly copied or deformed to the newly acquired
MRI [16]. The target structure is rigidly propagated to the new image and may be
manually edited by the physician. The adaptive planner can manually edit critical
OAR contours and these will be approved by the physician [22]. As this is one of the
most time-consuming tasks of the online ART workflow, it is suggested that only
contours within a 2–3 cm radius of the target need to be manually re-contoured as
this should be the region with the highest dose gradients [27–29]. The planning target
Figure 16.4. Diagram depicting the workflow for online adaptive treatment using the ViewRay system.
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volume (PTV) and tuning structures may be automatically generated from the edited
contours by applying pre-defined rules for their generation (i.e. a defined rule of
expanding the GTV 3 mm isotropically to create the PTV). After contours have been
edited and approved by the physician, the dose is recalculated using an electron
density image derived via registration of the initial simulation image to the daily
MRI [22]. The electron density map can be reviewed during this step and, if
necessary, the electron density in specific areas of the image may be overridden using
contours to correct for any errors. This recalculated dose is the ‘predicted dose’ that
would be delivered to the newly revised contours by the original treatment plan if
there is no adaptation [22]. The physician reviews the predicted dose and daily
anatomy to make the clinical decision of whether to treat with the predicted dose or
to adapt the plan. If the plan is to be adapted, the TPS performs IMRT optimization
using the same beams and optimization weighting as the base plan. If the dosimetry
of this adapted plan is not adequate, beam angle and optimization weights may be
edited until an acceptable plan is achieved [22]. Note that editing the optimization of
the plan adds a signifi cant increase in time to this workflow and it is preferred to
make few or no changes to the optimization if possible. A final evaluation of the
dosimetry is performed and a decision is made by the physician to treat with the
adapted plan, initial plan, or delay treatment [22]. The plan must have QA
performed before treatment; however, phantom or EPID measurement-based
IMRT QA is not possible as the patient should not be moved from the
table during the online ART process [22]. Instead, QA is performed by comparing
the TPS dose to the dose calculated using a secondary Monte Carlo tool provided by
ViewRay that recalculates the planned dose using the MLC leaf positions and beamon times of the adapted plan and presents this data as a DVH and gamma analysis
[16, 22]. Although this online QA process has some limitations in the fact that it uses
the same beam model for both calculations (possibly obscuring errors in the beam
model) [22], adaptive plans have shown to be robust when patient-specific QA was
retrospectively performed using a multidetector array [30]. After final approval of
the plan and plan QA, the treatment is delivered to the patient in the same fashion as
a non-adapted plan, using motion management strategies such as automated beam
hold if required. After delivery, the MRIdian system generates a report of the
delivery record including recorded MUs and MLC leaf positions compared to their
planned values and log files may be exported [16].
16.3.3 Challenges in MRI ART workflow
General challenges inherent to MRI adaptive workflows include sCT generation and
image registration. Both online and offline adaptive workflows rely on these
fundamental steps for dose calculation and large errors in accurate electron density
estimation may have a significant impact on dose calculation [31]. The CT-sim
workflow introduces challenges associated with error in image registration, including inconsistencies between images due to image quality, artifacts, long durations
between CT and MR scans, and dissimilarities in set-up positions [5]. These
inconsistencies are especially prevalent if the MRI from time of treatment is being
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registered to the original CT-sim as a large amount of time has passed between the
acquisition of these images. Speci fically, regions with inhomogeneities and large
variability due to physiological changes such as the abdomen, pelvis, and head and
neck present challenges in image registration [5]. Even deformable image registration techniques are associated with uncertainties up to 5 mm [32]. Registration is
also important in online ART as the contours are transferred from the planning
image to the daily image. Mitigating errors in this registration allows for a more
efficient online ART workflow. Meanwhile, the MRI-sim workflow includes
challenges related to the creation of the sCT.
In addition to being used to position the patient and evaluate their anatomy at
treatment, MRI is used for target delineation in both of the above workflows and, as
such, it must be of higher image quality than is usually required for image guidance
only. A balance of time efficiency and obtaining images with high resolution and
high SNR must be achieved in ART workflows [34]. AI deep learning algorithms
can be implemented in MR imaging to reduce scan times by reconstructing images
from under-sampled data [34]. Image distortions resulting from the main magnet
field inhomogeneities, gradient nonlinearities, motion, and eddy currents degrade
image quality and must be corrected [21]. This is particularly important in ART
where the MRI is used for re-planning and image quality may affect the accuracy of
dose calculation. Mitigating MRI distortion and artifacts may be achieved with
careful calibration of the scanner, strategic selection of pulse sequences, and image
processing algorithms—which may be accelerated using AI tools [21].
The decision of whether ART is needed and whether the offline or online
workflow is appropriate is another challenge of ART. While this is a clinical
decision that is ultimately left to the treating physician, guidelines and tools may be
implemented to reduce subjectivity in the decision. Factors such as the intent of
treatment, dosimetric impact of anatomy deformations, the number of fractions
remaining, and patient performance can all influence this decision of if and when to
adapt [20]. Defining a set of pre-specified OAR constraints to be the threshold for
adaptation may be helpful in reducing additional time for decision making during
treatment [20]. AI tools may be able to predict which patients will require adaptation
and when it would be ideal to adapt treatment based on extracting key features from
existing data [34]. AI models have been used in retrospective studies to predict
geometric changes in patients and predict when adapting during the treatment
course can maximize tumor control [35–37]. AI-powered automation may also be
used to review the cumulative dose impact of adapting a plan and may be a helpful
tool in predicting treatment outcomes as a result of ART [20, 34].
Both offline and online adaptive plan workflows suffer from uncertainty in total
dose accumulation using multiple plans over the course of treatment. This is an
ongoing challenge as both the anatomy and dosimetry change with adaptive
treatment. For structures that move considerably between treatments, it is difficult
to identify and track point volume doses [20]. Due to this uncertainty, more
conservative approaches to dose accumulation may be used, such as summing the
max point dose to an OAR over all treatment days and evaluating each new plan as
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if it were to be delivered for all total fractions to limit OAR dose assuming the ‘worst
case’ scenario [20].
Online adaptive workflows introduce additional challenges, particularly pertaining to the time and resource limitations during treatment. The online ART process is
significantly more time consuming than a conventional treatment, with mean total
delivery times of 75–90 min reported in the literature [22, 38]. Since the patient is on
the table for longer than a typical treatment session, it is important to be mindful of
the possibility of large anatomical changes occurring between imaging and delivery.
Bladder filling and stomach emptying are examples of processes that may occur
within the time it takes to perform online ART and, as such, speeding up the process
as much as safely possible may ensure that the delivered plan is still relevant to the
anatomy at time of beam-on [20]. Patient comfort should also be considered in initial
set-up and custom or non-typical immobilization may be used to increase patient
comfort and stability for long adaptive treatment sessions.
Not including treatment delivery, contouring was reported to be the most timeconsuming and error-prone step of the online ART workflow [22, 29, 38, 39].
Currently, contours being correctly deformed to daily images are dependent on the
accuracy of deformable registration, which was stated as a challenge above. Since
this process is not perfect, time must be allocated to manually checking the contour
accuracy and integrity. Advanced AI-driven auto-segmentation approaches are
promising in minimizing the time allocation for this part of the process to contour
both target and OAR volumes [40–43]. AI-based auto-segmentation methods have
been shown to perform similarly to manual contours drawn by human experts and
to be more accurate than atlas-based methods [34, 44, 45]. Commercial deep
learning auto-segmentation solutions have been evaluated in the literature and
have been found to provide high-quality contours that agree well with manually
drawn contours while offering substantial time savings [44, 46]. Of course, a welldefined and robust QA procedure for any auto-segmentation used would be required
for clinical use. AI-based tools may be useful in reducing the time and labor required
for QA of auto-segmentation workflows [34]. One example of this is using
automated contour refinement to speed up the process of correcting auto-segmented
contours [47]. Translation of these approaches into clinical practice faces many
challenges including quality of training data and logistical limitations [26].
The second most time-consuming step of the workflow, outside of delivery, is
typically re-planning [22, 38]. One approach to increase efficiency in re-planning is
using a plan library approach. This plan library would be based on predicable
changes in specific OARs that would affect target coverage, for example changes in
bladder or rectal filling [20]. Then, at the time of treatment, the most appropriate
plan would be chosen to be used for adaption based on the daily anatomy. Monte
Carlo dose algorithms must be used to account for the impact of the magnetic field
on secondary electrons, and a deep learning dose calculation has been shown to
accelerate Monte Carlo for adaptive planning over traditional calculations [48].
Efforts to improve the speed of plan optimization using approaches such as
automated beam angle optimization and knowledge-based planning (using databases of prior plan information) may be helpful to increase the efficiency of this step.
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AI-based approaches to treatment planning involve predicting the optimal dose
distribution and identifying machine parameters that must be used to achieve the
optimal distribution [34]. Some studies have demonstrated the ability of deep
learning approaches to predict optimal dose distributions and accelerate dose
calculations, which would increase the efficiency of ART [49–51]. Auto-planning
that can promise consistently high plan quality in a short period of time may become
more important for the future of online ART [52].
A limitation of the online ART workflow is the inability for measurement-based
IMRT QA while the patient is on the table. The secondary calculation check used by
the MRIdian system has a limitation in that it uses the same beam model for the
primary and secondary checks [22], therefore considering alternative or additional
IMRT QA solutions may enable more confidence in this part of the workflow.
Retrospective QA of adaptive plans using film or diode arrays may be performed,
particularly during initial clinical adoption of MR ART or for highly complex plans
[29, 53, 54]. Offline log-file analysis or EPID measurements during delivery are other
methods that can be used to assess plan integrity immediately after treatment [55,
56]. Alternatively, a ‘simulated’ treatment could be run before delivery without
activating the beam to analyse log fi les to planned patterns before treatment,
although these strategies may not detect errors that can occur during delivery [39].
Additional in-house manual and automated checks at this point of the process
should be considered [39, 57].
The nature of online ART requires a significant resource burden, with attention
from physicians, physicists, dosimetrists, and therapists needed at the time of
treatment. The physician present at treatment may not be the attending physician
following the patient and may not be familiar with the clinical history. Similarly, the
physicist and dosimetrist present at adaptive treatment may not be the same
individuals that were involved in the initial plan creation and check. As such, clear
communication between these groups is essential to avoid any additional error and
confusion. Documentation outlining clear instructions for plan adaptation thresholds and re-creating derived contours for re-optimization should be used in the
online ART workflow [22].
MRI has the capability of providing functional and structural data together,
using strategies such as dynamic-contrast enhanced imaging and diffusion weighted
imaging [5]. This qualitative biological data could potentially be collected with the
MR-linac system and incorporated into decision making for adaptive therapy [26].
AI tools may also play a role in this emerging frontier of MRI ART as analysing
radiomic trends using AI may provide a more complete picture of tumor sensitivity
and behavior during and after adaptive treatment [5].
16.4 AI applications for MRI-guided ART
The implementation of online MR-guided ART has been recognized as a potential
advancement in enhancing the precision and efficacy of radiation therapy. The
integration of AI into ART aims to enhance precision, efficiency, and outcomes by
leveraging data-driven approaches to dynamically modify treatment plans. Despite
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its promise, the widespread clinical adoption of online MR-guided ART encounters
significant barriers, primarily attributed to its operational complexity and the
intensive demand for specialized human resources [58, 59]. This challenge underscores the necessity of AI integration to streamline the MR-guided ART workflow,
potentially leading to improved efficiency and cost-effectiveness in clinical applications. Areas of the MRI ART workflow that may benefit from AI-based automation
are introduced in the above section. Increasing efficiency and accuracy of these steps
has potential to reduce risks as well as the resource and time costs associated with
ART. Simulation-free ART may be a next possible avenue for MR-linac technology
if these challenges, and challenges associated with MR image quality can be
addressed with AI tools. The past decade has witnessed a significant increase of
AI applications aimed at augmenting various aspects of the radiotherapy workflow
[60, 61]. However, as of the current state of development, a dedicated commercial
solution to support MR-guided ART via AI integration is still not commercially
available. The principal areas where AI can be applied in MR-guided ART include
synthetic CT generation, auto-segmentation, and image registration.
16.4.1 Synthetic CT generation
This section is reproduced with permission from [67].
In the current clinical practice of MR-guided RT, the workflow typically involves
a two-step simulation: a CT and subsequent MR simulations. The necessity for CT
simulation stems from the inherent limitation of MR imaging to provide electron
density maps, which are crucial for accurate dose calculation in treatment planning
[62]. Advancing towards an MR-only radiotherapy framework necessitates the
development and integration of synthetic CT generation from MR images. This will
eliminate the need for conventional CT image in MR-guided RT workflow, thereby
reducing the additional radiation exposure and mitigating uncertainties associated
with the registration between MR and CT images.
Various methods have been proposed to address this issue, which can be mainly
divided into three categories: segmentation-based, atlas-based, and learning-based
methods [63–67]. Currently, segmentation-based methods are widely utilized in
clinical settings, where sCT images are created by assigning uniform bulk densities
to structures identified on MR images. However, these methods heavily rely on the
accuracy of organ segmentation and fail to account for heterogeneity within each
structure.
In recent years, learning-based methods, including traditional machine learning
and deep learning methods, have gained substantial attention for synthetic image
generation. These methods exploit self-learning and self-optimizing strategies to
learn the MR-CT mapping for sCT generation. Among them, deep learning
methods using convolutional neural networks (CNNs) have been demonstrated to
have more promising performance in sCT generation without the need for extracting
hand-crafted features [68]. For the deep learning methods, generally a model is
trained to establish a nonlinear mapping from the MR to CT domain based on a
large database of MR and CT pairs. Once the deep learning model has been trained,
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