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Artificial Intelligence in Adaptive Radiation Therapy
Figure 15.4. Risk priority number (RPN) scoring with and without reference planning review, using failure
mode and effects analysis of the faults from (a) simulation, (b) plan set-up, and (c) plan optimization processes.
The error bars illustrate the mean and standard deviation risk priority number for each failure mode.
(Reproduced with permission from [
63]. Copyright 2024 Elsevier.)
enhance dose shaping), and assign objective weights based on planning priorities.
Some platforms use quality-monitoring functions to guide iterative optimization and
halt refinement once specific clinical goals are satisfied, enabling efficient convergence. These fast optimizers are designed not only to meet planning objectives but
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Artificial Intelligence in Adaptive Radiation Therapy
also to further improve plan quality when time permits. Because these tools influence
both reference and adaptive plan quality, clinical teams must understand their
behavior and limitations when designing planning templates and workflows for
online ART.
The Ethos kV-CBCT online ART platform utilizes a proprietary intelligent
optimization engine (IOE) to automatically generate plans from a planning goal
template submitted to the TPS. The IOE is a hands-off ‘algorithm that orchestrates
the plan optimization’ by seeking to ‘perform all the actions necessary to generate
high-quality dose distributions that meet the clinical expectations for the plan and
ensure that the plan is diametrically accurate’ [103]. This is made possible by
automated creation of helper/optimization structures, deriving non-overlapping
structures in the presence of overlapping structures with opposing objectives, and
assigning objective weights based on the hierarchy of the planning goal template
submitted to the dose preview workspace [104]. The IOE functions by first translating clinical goals into photon optimizer objectives, then generating piecewise
quality functions (Q-functions) for monitoring and influencing the optimization
process. The form of each function prototype (e.g. target upper/lower dose and
organ upper dose) is derived from known features of a good distribution and
generated by assigning a goal priority and relative goal value, allowing each function
to be placed on a priority–quality plane (P, Q)[103]. The optimizer seeks to iterate
until the Q-function meets an individual goal point (P
, Qi), then this goal does not
i
contribute to additional optimizations for lower priority functions. Additionally, the
IOE is designed to further reduce organ and target upper dose levels once all
planning goals are achieved.
For each goal template submitted to th e TPS in the dose preview workspace,
several preselected IMRT (7, 9, or 12 equidistant fields) and VMAT (two and
three full arcs, two partial arcs) plans are automatically optimized and calculated
using a collimator rotation of zero, although custom geometries can be exported
from Eclipse on a patient-by-patient basis. The superior reference plan geometry,
i.e. the plan selected for adaptive treatment, defines the optimization objective
template and geometry utilized for daily online ART plan generation. Many
groups have investigated the quality and clinical acceptability of E thos IOE
automated plans for multiple beam geometries using standardized planning
templates. It has been thoroughly demonstrated that, given a well-designed
template, the IOE automatically generates high-quality standard fractionation
plans for sites in the male and female pelvis [66, 67, 105] and head and neck [106],
with similar and sometimes improved performance compared to manually
generated Eclipse plans [69, 104].
Pogue et al demonstrated that the IOE can automatically produce plans similar in
quality to knowledge-based planning models for locally advanced lung cancer [107].
Furthermore, Visak et al and Roberfroid et al investigated the feasibility of using UNet machine learning models to develop IOE head-and-neck and prostate planning
goals, respectively, on a patient-by-patient basis; they each observed that AI-guided
planning was superior to standard template planning [108, 109]. Additionally,
despite the IOE being designed for organ avoidance planning with homogeneous
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Artificial Intelligence in Adaptive Radiation Therapy
target coverage, multiple groups have effectively developed automated or semi-automated stereotactic planning techniques for APBI [110] and lung and brain
tumors [47, 111]. However, Ethos V2.0 offers a ‘High-Fidelity’ stereotactic planning
selection which largely mitigates the need for many of the complex stereotactic
planning strategies outlined above, with improved online treatment efficiency
observed [112, 113]. It should also be mentioned that several groups observed that
the IOE IMRT plan dosimetry and optimization time was superior to VMAT, likely
due to increased degrees of freedom when gantry angle is included in the
optimization objective function [105, 107]. Given that reference planning defines
online ART optimization, daily VMAT treatments require more time than IMRT
plans, causing some clinics to exclusively adapt using IMRT [105].
Conversely, the uRT-linac 506c TPS, uRT-TPOIS, utilizes a hybrid voxel-based
optimization approach, combining 3D U-Net network dose predictions with a preset
list of clinical objectives [73, 114, 115]. Stochastic gradient descent optimization is
utilized to obtain an optimal solution to the hybrid objective function, which is the
sum of voxel and DVH-based objective functions. This novel, automated treatment
planning system predicts the deliverable dose from a structure set containing target
and OAR contours via U-Net based deep learning, then minimizes the mean
squared error of calculated and predicted dose during optimization, resulting in the
generation of accurate plans during delivery. The Elekta Evo system uses a Monte
Carlo based treatment planning engine within the Elekta ONE TPS to support
adaptive plan generation. Clinical goals—defined through a planning intent—are
translated into optimization objectives that guide the generation of IMRT or
VMAT treatment plans. During re-optimization, users can interactively modify
objective priorities, dose constraints, and normalization values in real time. The
system supports iterative re-planning to improve dose distributions, with automated
handling of overlapping structures and dose shaping objectives.
15.2.6 Patient specific quality assurance
Patient-specific QA (PSQA) for all online ART reference plans is performed using
the same methods as those applied in standard-of-care treatment planning: patientspecific treatment plans are recalculated onto a phantom, then delivered at the
machine and measured, followed by three-dimensional analysis of dose agreement
between the TPS and measured doses. Zhao et al demonstrated that reference plans
agree well with measured doses for 16 patients receiving treatment to various sites
and with differing fractionations [56]. All ion chamber measurements were within
3% absolute dose difference and all cylindrical diode array measurements were
above 95% gamma passing rate (3%/2 mm with 10% threshold). Furthermore, Sibolt
et al performed measurement-based analysis (Delta4+, ScandiDos AB, Uppsala,
Sweden and portal dosimetry) and calculation-based analysis (Mobius3D, Varian
Medical Systems) of 32 bladder and rectum reference plans, finding that both agreed
excellently with Ethos using 3%/2 mm and 3%/3 mm gamma passing criteria,
respectively.
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Artificial Intelligence in Adaptive Radiation Therapy
Specialized software platforms have been developed to enable independent,
calculation-based dose verification for online adaptive radiotherapy, addressing
the limitations of traditional measurement-based QA in time-sensitive adaptive
workflows [116]. These tools should incorporate ultra-fast dose calculation engines
and support comprehensive 3D dosimetric evaluation, including gamma index
analysis and dose–volume histogram comparisons, to ensure the accuracy and
safety of delivered adaptive plans across various treatment systems.
15.2.7 Online ART workflow
The CT-based online ART treatment delivery workflow is dynamic and differs from
the standard-of-care in many ways, several of which can be visualized through a
representative workflow shown in figure 15.5 [61]. In both adaptive and nonadaptive workflows, patients undergo initial CBCT imaging. However, in online
ART, the daily CBCT is further used for organ and target segmentation, which
informs daily plan optimization, if applicable. After careful review by the clinical
team, either the scheduled (non-adaptive) or adaptive plan is selected for treatment.
If the adaptive plan is chosen, a secondary dose calculation is often performed for
quality assurance purposes. Due to the additional time required for contour review
and plan generation in the adaptive workflow, a secondary position verification scan
is recommended prior to treatment delivery.
Figure 15.5. Example CBCT-based online ART and non-adaptive treatment workflows utilized by Stanley
et al. (Reproduced from [
of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American
Association of Physicists in Medicine.)
61] with permission from John Wiley & Sons. Copyright 2023 The Authors. Journal
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Artificial Intelligence in Adaptive Radiation Therapy
15.2.7.1 Patient set-up and daily imaging
After the patient has been set up, the appropriate scanning protocol must be
selected. This step is critical, as the entire online ART workflow may depend on it.
For example, in some systems, the selected algorithm or site determines the
influencer structures, i.e. site-specific organs that guide the deformation of target
and OAR contours from the reference CT to the daily CBCT. There are two
primary reconstruction algorithms offered by Ethos: the analytical and standard
Feldkamp–Davis–Kress (FDK) algorithm [117] and the novel iterative CBCT
(iCBCT) algorithm, which reduces noise and increases contrast via penalized
likelihood statistical analysis [118, 119]. However, iCBCT assumes the patient is
static and is thus highly sensitive to anatomic motion [120]. Therefore, iCBCT
reconstruction should be utilized in the presence of small amounts of motion
(HN, pelvis, b rain, thorax/breast/abdomen utilizing breath-hold) and FDK
reconstruction should be selected given significant anatomic motion (i.e. freebreathe thorax, abdomen, or breast). Conversely, the uRT-linac 506 allows for kV
fan-beam and MV cone-beam CT images to be acquired simultaneously,
simplifying image registration and providing image quality sufficient for direct
dose calculation, as it is nearly free from image degradation due to photon scatter
[48]. Furthermore, the integration of kV and MV imaging enables a significant
reduction of artifacts derived from complex metals compared to traditional
artifact correction methods [121].
15.2.7.2 Contouring
In online ART workflows, some structures may be automatically contoured using
DIR, while others may be segmented using deep learning models such as CNNs
[103]. The method of generation often depends on the anatomical site and available
system capabilities. Because these auto-generated contours may directly influence
downstream processes—such as target propagation, plan optimization, and dose
evaluation—it is essential that the clinical team has a strong understanding of how
each structure is generated and used. Careful review and editing of these contours
are critical to ensure clinical accuracy and safe adaptive plan delivery [103].
15.2.7.3 Plan calculation and selection
In some online ART systems, an sCT is generated by deforming the planning CT to
the daily CBCT using DIR, often relying on mutual information-based cost
functions and spline-based deformation models [122]. Dose calculation for both
scheduled (non-adaptive) and adaptive plans may then be performed on this sCT. In
other systems, dose can be calculated directly on the CBCT itself, provided the
image quality and HU accuracy are sufficient [23]. For systems using sCTs, rigid
alignment between the CBCT and sCT is typically performed prior to dose
calculation, sometimes using target-focused similarity metrics. The same planning
template used for reference planning is applied during daily adaptation, and the
scheduled and adaptive dose distributions are then overlaid on the CBCT anatomy
to support plan selection for treatment.
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Artificial Intelligence in Adaptive Radiation Therapy
15.2.7.4 Quality assurance
Daily online ART plans differ in MU and fluence compared to reference plans, and
should thus be evaluated with PSQA according to traditional professional standards.
However, removing the patient from the treatment couch after daily plan generation
to perform phantom-based measurements can introduce significant set-up uncertainty, which compromises the use of the reduced planning target volume margins
typically employed in online ART. For this reason, PSQA is not typically performed
for online adaptive plans prior to delivery.
Instead of performing phantom-based measurements, adapted plans along with
the corresponding daily CT and structure sets can be exported to an independent
secondary dose calculation system for verifi cation. Prior to adaptive treatment
delivery, it is recommended to compare key dosimetric metrics for target coverage
and OAR sparing between the primary and secondary calculations, and to evaluate
gamma passing rates using clinically appropriate criteria (e.g. 3%/2 mm or 5%/3 mm
with a 95% pass rate). Zhao et al demonstrated that adapted plan dose
calculations on the Ethos system showed good agreement with point dose measurements, patient-specific QA measurements, and independent secondary dose calculations [56]. Furthermore, studies have shown strong correlations between gamma
passing rates from secondary dose calculation systems and measurement-based QA
across both reference and daily adaptive plans, suggesting that independent dose
calculations may serve as an effective QA approach for CBCT-based online ART,
particularly when the reference plan has passed initial validation [55].
After secondary dose calculation and evaluation, many clinics will perform a
position verification CBCT to account for patient movement and/or anatomy
change since the initial CBCT [61, 119], although this may not be required by the
delivery system. Once shifts are applied, the patient is ready for treatment. For
patients without significant respiratory motion, surface-guided radiotherapy
(SGRT) systems can be used to monitor intrafraction motion by tracking the
displacement of the surface centroid in real time [123]. In cases requiring breath-hold
motion management, the vertical displacement component is often used to monitor
chest wall motion and ensure consistency with the planned breath-hold position. In
high-precision workflows such as breath-hold CBCT-guided stereotactic adaptive
radiotherapy (CT-STAR), deviations beyond a predefined threshold (e.g. 2 mm
vertically) can trigger the acquisition of an intrafraction CBCT to verify
target alignment [124]. Alternative motion management strategies may include
visually guided respiratory training, where patients adjust their breathing to match a
predefined amplitude window, supported by either commercial or in-house software
solutions. Furthermore, online electronic portal imaging device (EPID) analysis
could be used for motion monitoring. Peng et al used the EPID panel for monitoring
in vivo doses from adaptive radiation therapy for cervical cancer. If the global
gamma passing rate fell below 88% using a 3%/3 mm threshold, treatments were
suspended or terminated pending further investigation [68]. They observed that all
plans were at or above a 89% pass rate for six patients, supporting accurate uRTlinac 506c adaptive cervical cancer treatment delivery. The feasibility of this
methodology has also been demonstrated for rectal cancer patients [125]. Due to
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Artificial Intelligence in Adaptive Radiation Therapy
the inability to detect online ART plan deliverability issues without PSQA, Sun et al
developed a machine learning-based ensemble model for predicting gamma passing
rate for uRT-linac 506c SBRT plans. They observed areas under the receiver
operator characteristic curve of 0.87 and 0.84 for the 2%/2 mm and 1%/2 mm
criteria, respectively, using the ensemble of plan and radiomic models [126].
15.2.8 Offline contour and plan evaluation
Offline contour and plan evaluation in the context of CT-based adaptive radiotherapy extends beyond the initial adaptation process to encompass a comprehensive verification step post-delivery. This phase involves assessing the created plans
and contours to ensure concordance with reference plan contour definitions and
efficacy in responding to anatomical or physiological changes observed during
treatment. Additionally, offline review can be utilized as an opportunity to identify
potential changes to the planning directive that can result in an improved plan.
Following the delivery of adaptive radiotherapy, the verification process is crucial
for confirming the validity of the offline adaptation approach. This involves a
detailed analysis of the treated anatomy through comparison with the original
treatment plan and the overall objectives of the physician directive. An essential
aspect of this verification is the examination of the acquired and created images,
where the contours from the planning CT are either propagated or redrawn to the
daily anatomy, and the planned dose is recalculated on the current daily anatomy.
The goal is to confirm that the adapted plan aligns with the intended treatment goals
and adequately addresses any anatomical deviations that may have occurred during
the course of treatment. This requires a high level of understanding and communication amongst the treatment team of the goals for the particular patient.
Additionally, with systems that utilize sCT, a pivotal role of the offline assessment
is in ensuring the precision and reliability of the generated sCT, particularly in areas
of high heterogeneity [99]. The sCT should align closely with the actual patient
anatomy, and contours derived from the sCT should accurately represent the target
volumes and OARs, as discrepancies in contouring may lead to deviations in dose
calculation and subsequent treatment outcomes. While it is not possible to change
the sCT with current software versions, evaluation of large discrepancies in sCT can
necessitate the need for a re-simulation or changes to the structures and contours.
Lastly, offline dose accumulation review can be used to inform the reviewer of the
effects of anatomical variations on the summed, delivered dose. For Ethos, the
deformation vector field used in the structure guided DIR is utilized to propagate
dose from the sCT to the planning CT. Because of the high-impact that dose
mapping and accumulation has on online ART, the results should be closely
monitored and methods should be continuously improved [38]. An example of the
effects daily adaption may have on dose accumulation, hot and cold spots vary in
position daily with adaption, but occur in the same position every day during nonadaptive treatment, leading to greater target homogeneity with online ART [83].
Furthermore, Peng et al found good agreement between TPS accumulated adaptive
cervical cancer RT dose and three-dimensional dose reconstruction derived from
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Artificial Intelligence in Adaptive Radiation Therapy
two-dimensional EPID measurements for the uRT-linac 506c [68]. This novel
quality assurance step has even been utilized to verify excellent agreement between
planned and delivered dose for total bone marrow lymphoid IMRT in the nonadaptive setting [127].
15.2.9 Limitations and future directions
Despite the advancements in offline adaptive strategies, there are inherent limitations and areas for future development. Further integration of clear, site-specific
thresholds for determining which patients are the optimal candidates for online
ART is needed, and understanding the criteria that warrant adaptive planning is
essential for optimizing treatment outcomes. Active offline monitoring and utilization of innovative technologies and artificial intelligence holds promise for allowing
detection of subtle anatomical changes that may necessitate adaptation.
Additionally, the exploration of adaptive dose calculation time reduction
techniques, particularly with high-quality CBCTs versus sCT, represents a potential
avenue for future research. Integrating AI into the dose calculation and evaluation
processes may further refine the accuracy of adaptive strategies. Research efforts
should focus on developing robust models that can predict dosimetric changes based
on patient-specific characteristics and treatment parameters.
15.3 Summary
Offline CT-based ART and online CBCT-based ART represent two complementary
strategies for adapting radiotherapy plans in response to patient-specific anatomical
changes. Offline ART is typically triggered by anatomical changes observed on
routine imaging and involves re-simulation, re-contouring, and re-planning with
plan summation to assess cumulative dose. While beneficial, offline ART can be
resource-intensive and susceptible to registration uncertainties, requiring robust QA
and careful patient selection.
Online ART leverages on-board imaging systems and intelligent optimization
platforms to adapt treatment plans in real time, offering precision in daily plan
delivery. Clinical adoption remains limited due to challenges in sCT accuracy,
resource demands, and workflow complexity. Nevertheless, ongoing advances in AIbased automation, predictive modeling, and integrated QA frameworks are improving feasibility and clinical value.
Together, these approaches highlight the evolving landscape of adaptive radiotherapy, emphasizing the importance of streamlined workflows, reliable image
registration, intelligent planning tools, and thoughtful implementation strategies
to optimize treatment outcomes.
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