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Artificial Intelligence in Adaptive Radiation Therapy
Figure 15.1. Comparison of online and offline ART. (Reproduced with permission from [1]. Copyright 2019
Elsevier.)
process can be initiated either at a planned date/fraction or, more commonly, in
response to anatomical changes and/or disease progression/response observed on
either image-guided radiation therapy (IGRT) and/or on-treatment diagnostic
imaging. The ART process has been shown to provide dosimetric benefitforthe
head-and-neck [2] and has been investigated to trigger dose escalation for nonsmall cell lung cancer [3].
While offline ART can be beneficial, the accelerated timeline for treatment
planning, as compared to initial treatment planning, produces a burden on staff and
can potentially lead to errors [4]. A survey by Krishnatrry et al [5] found that while
many centers employ offline ART (84% of respondents), there are noted barriers to
ART which need to be overcome for increased utilization of ART, most prominently a lack of proper equipment (i.e. delivery systems and planning tools
optimized for adaptive radiotherapy) which was reported by 48% of respondents.
In a separate survey conducted by Betholet et al, 63% of respondents ranked ‘human
resources’ as either the primary or secondary barrier to the implementation/
expansion of ART [6]. A majority of respondents also listed technical limitations
and equipment/financial resources as highly important.
15.1.1 Patient and site selection
Offline adaptive radiotherapy addresses progressive changes to the treatment
volume or organs at risk (OARs), such as patient weight loss or tumor regression.
Monitoring for anatomical or physiological changes can be done by the observation
of tumor change during image review or by determining thresholds for changes seen
in daily CBCT imaging [7–9]. Routine scans (e.g. weekly quality assurance (QA)
simulation during proton therapy workflow) may also be used to appreciate changes
at regular intervals.
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Typical sites for offline adaptive therapy are the head and neck and thorax,
although anticipating which patients will have the most dosimetric benefit from
adaptation at the time of initial planning is difficult. There have been several studies
exploring the ability to predict patients that will exhibit anatomical changes
throughout treatment [10]. For example, Lee et al [11] used a deep learning model
to predict the geometric evolution of lung and esophagus contours throughout
treatment, and used weekly CBCTs to update the model’s predictions on a patientspecific basis. Wang et al [12] built a convolutional neural network (CNN) to predict
lung tumor shrinkage using weekly MRIs throughout the course of treatment. Even
with these developments, determining which patients will benefit most from offline
adaptive therapy is not straightforward at the time of initial planning. Instead,
monitoring of tumor or OAR change using routine imaging is typically used.
The process of offline adaptation is employed when visible changes to the tumor
or normal tissue are indicated, or functional changes are shown mid-treatment [3].
Daily anatomical changes (e.g. bladder filling) are not appropriate for offline
adaptive therapy, given the timeframe for re-simulation and planning. A majority
of centers consider adaptation on an ad hoc basis [6], although there have been
protocols designed to trigger once dosimetric thresholds have been met [13].
Typically, this process requires registration and contour propagation from the
planning CT to CBCT [14] automated contour and recalculation of the planned dose
to the current daily anatomy.
Direct plan recalculation and dosimetric evaluation using CBCT alone can lead to
erroneous results because the Hounsfield units (HUs) in CBCT may not share a one-toone correspondence with the HUs in treatment planning CTs. To mitigate this, a
synthetic CT (sCT) can be created, either using artificial neural networks or deformable
image registration (DIR). In the neural network (NN) approach [15–18] models are
typically trained to learn a mapping from the CBCT HU domain to the CT HU domain,
allowing for accurate recalculation. In the DIR-based workflow, which is more
commonly implemented in clinics [19], the CT numbers from the planning CT are
propagated to the anatomy of the day based on the daily CBCT according to the
deformation vector fields from the registration [20–22]. While either of these approaches
are typically superior to direct calculation on the CBCT, they can both lead to errors and
should only be implemented with proper QA and reviewed with clinical judgment [23].
15.1.2 Re-simulation
The re-simulation process for offline adaptive therapy is often the same as the initial
CT simulation [1]. For disease sites affected by motion, maintaining the same
motion management protocol, such as respiratory gating, as used during initial
simulation helps ensure consistency in planning and delivery, when still clinically
appropriate. In most cases, the immobilization equipment from the initial radiotherapy course is preserved. However, one potential cause of ad hoc offline adaption
is immobilization equipment no longer fitting properly, as can happen when patients
undergo significant weight loss during the course of treatment.
Another option for generating a new plan is to use the CBCT for deformable
registration, as was done by Bojechko et al [24]. Rather than creating a new planning
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Artificial Intelligence in Adaptive Radiation Therapy
CT, the Halcyon CBCT was used to create deformed structures on the initial
planning CT; this was possible due to the large field-of-view and soft tissue contrast.
Theoretically, this workflow could be implemented when changes to the patient’s
anatomy are apparent and the CBCT has a large enough field-of-view to create an
adequate structure deformation map back to the planning CT.
15.1.3 Re-planning
After re-simulation is performed, the planning process begins with either delineating
or propagating previous contours. Deformable image registration has been used for
the propagation of targets in CBCT-based offline re-planning for patients with
oropharyngeal tumors [25]. Mencarelli et al [26] found that DIR accuracy for both
normal and tumor tissues was < 1 mm, but precision was variable, with precision
significantly degrading with larger intervals between the planning CT and follow-up
CBCT. DIR for target propagation is an attractive option because of the availability
of DIR algorithms within many treatment planning systems, however the accuracy
of the registration has been found to be dependent on the registration algorithm or
software [27]. AI-based automated contouring has also been demonstrated as a
feasible option to expedite re-planning in adaptive settings [28, 29].
15.1.4 Plan summation and evaluation
Offline adaptive summation of dose can be used for the summation of entirely new
plans, as described above, or on a regular basis to monitor how the original planned
dose compares to what was actually delivered. For re-simulation and re-planning,
once a new plan is generated, summation with the initial plan is needed to estimate
the total dose in the course of treatment. The accuracy of the combined dose is
limited by the uncertainty of the image registration between the initial and new CT
scan [30], slice thickness, and dose grid sizes. These limitations are amplified in
regions of marked tumor growth or regression, making the resulting plan sum,
potentially, less accurate [31]. DIR is commonly used to register images where the
shape or size of targets and organs differ between the new planning image and initial
CT image. There are many deep learning methods employed in image registration
[32], including reinforced learning [33], generative adversarial network mapping [34],
and unsupervised transformation prediction [35]. The deformation vector fields from
the DIR dictate the dose accumulation, so uncertainty in the DIR process
propagates throughout the plan summation [36]. Because of this, validation of
image registration algorithms is paramount. The AAPM Task Group 132 [31]
provides guidelines on metrics for evaluating the accuracy of an image registration
algorithm. The new and initial plans can also be calculated on different grid sizes,
leading to differences in interpolation between the grid points. The observed dose
summation is based on the alignment of the treatment planning system (TPS) dose
calculation matrices from image registration, and the sum is displayed as the
interpolation between the two matrices. Care must be taken when interpreting the
plan sum in areas with steep dose gradients and for small structures.
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Artificial Intelligence in Adaptive Radiation Therapy
Dose summation may also be performed on a daily basis with daily dose
mapping. Daily dose mapping relies on remapping the calculated dose to the daily
imaging, which is subject to the uncertainties discussed above. Because the dose
accumulation is used to monitor delivered dose and incorporate this into decisions
about the plan going forward, there is a need for high accuracy. These so-called
‘dose of the day’ studies that consider dose calculation on deformed CTs found dose
calculation errors on the order of 1%–2%, depending on the site considered [21, 22,
37]. The effect of the deformation vector field used for calculation of the daily dose
depends on the dose heterogeneity and gradients of the dose distribution, and it
relies on the assumption that the dose mapping transformation is valid across the
entire registered images [38]. Particularly in regions of anatomical changes (e.g.
tumor shrinkage), the direct one-to-one mapping from one image to another is not
always straightforward in deformable image registrations. Even so, Murr et al [38]
recommend using registration algorithms that maintain this mapping strategy when
resampling dose. In short, daily dose summation is resource‑intensive and prone to
additional uncertainties that can complicate interpretation, thus clinical teams must
support its use with a robust QA program to guide treatment decisions.
15.1.5 Patient specific quality assurance
For offline ART, treatment plans should go through the same process as any new
plan, even if the workflow is slightly compressed compared to initial planning. This
workflow includes plan quality review by both the physicist and physician, and
typically measurement of the delivered plan. While independent measurement of the
dose distribution is the gold standard for patient-specific quality assurance (PSQA)
in radiation oncology, the majority of errors in the treatment planning process are
not caught by measurement [39].
Because of the added burden of PSQA measurement, there is interest in
techniques to eliminate the explicit measurement in lieu of other safety checks. In
a prospective study by Wall et al [40], a virtual QA system was tested to replace
physical dose measurement with predicted dose measurement. In this study, a
machine learning model was trained to extract plan complexity features from
radiation treatment plans and predict differences between planned and measured
dose, based on 579 historical measurements. The model had a mean absolute error
of 1% and if used to determine whether PSQA measurement was needed for a given
plan, would yield a 69% reduction in QA workload.
Deep learning approaches have also been employed to predict PSQA results.
Zeng et al employed a self-attention network with a modified U-Net to predict
measured dose distributions on a PSQA measurement device [41]. Rather than
predicting dose, Kimura et al [42] trained a CNN to detect MLC positioning errors
during delivery of VMAT plans. Developments such as these, which eliminate the
need for machine time to deliver PSQA, could have significant impact for offline
adaption because ensuring adequate time for PSQA measurement can add delays to
the ART process.
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Artificial Intelligence in Adaptive Radiation Therapy
15.1.6 Limitations and future directions
One of the current limitations of offline ART is its ad hoc nature. In the patterns of
practice for adaptive and real-time radiation therapy (POP-ART) survey of 177
radiation therapy centers, Bertholet et al found that while over half of the surveyed
centers performed offline ART, less than a third had specific ART protocols [6]. AI
has been shown to be able to predict treatment changes during RT in the head and
neck, further studies such as this could be used to develop prospective protocols for
offline adaptation, triggering re-simulation and re-planning and specific time points.
Integration of AI into the clinic also has high potential for utility in developing
thresholds to trigger offline adaption [43]. Examples of AI applications in the offline
workflow include automated segmentation on CBCT images, allowing for tracking
of target or OAR shrinkage or growth [44]. Using corrected CBCT images, dose
prediction based on daily imaging, whether from deep learning or knowledge-based
planning algorithms, may also be effective triggers for offline ART, alerting the
treatment team when certain dose metrics are exceeded.
Offline ART typically takes 1–3 days to go from re-simulation to treatment
commencement of the revised plan. This timescale means offline ART has limited
ability to adapt to either rapid or frequent changes in daily anatomy, for example
variable rectum or bladder filling in the pelvis. In disease sites that respond rapidly to
radiation, for example head-and-neck or lung tumors may shrink over the course of
1–3 days, offline ART can lead to planners ‘chasing’ anatomical changes because the
response time is similar to the time needed to create a new treatment plan [1, 45].
15.2 Clinical considerations for CBCT/CT-based online ART
Online ART is an emerging field with a limited number of commercially available
systems. Notable examples include Varian Ethos, Elekta Evo, and United Imaging’s
uRT-linac 506c.
The Varian Ethos kV-CBCT-guided online ART treatment system is at the time
of writing the only Food and Drug Administration (FDA)-cleared commercial
system to utilize on-board CBCT imaging for online ART. Ethos consists of a
Halcyon O-ring linear accelerator (Varian Medical Systems, Inc., Palo Alto, CA)
with integrated online adaptive software capabilities. The accelerator features a
6MVflattening filter free (FFF) beam with jaw-less collimation via a dual layer and
staggered 10 mm multileaf collimator (MLC) banks, enabling 5 mm effective MLC
resolution and decreased intra-leaf leakage compared to single layer MLCs. The
MLCs allow a maximum exposure area of 28 cm × 28 cm, and the compact
accelerator and closed bore design allow four revolutions per minute [46]. These
features combined with the 800 MU/min maximum dose rate enable faster treatments compared to flattened beam treatments on C-arm linear accelerators. The
online TPS produces both IMRT and VMAT plans, which are calculated using
Acuros XB with dose-to-medium “… reporting mode” [47].
The Elekta Evo (Elekta, Stockholm, Sweden) is a CT-guided adaptive radiotherapy (CTgART) system introduced in 2024. It integrates with the Versa HD
linear accelerator and Elekta ONE software ecosystem, including the TPS and
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Artificial Intelligence in Adaptive Radiation Therapy
oncology information system (OIS). The system uses Iris™, an AI-enhanced CBCT
solution for direct dose calculation and automated contouring, with planning
supported by MIM software and dose calculated using Monte Carlo algorithms
with dose-to-medium reporting. Evo supports IMRT (sliding window and step-andshoot) and VMAT delivery with a 6DoF couch, allowing for non-coplanar arrangements and precise IGRT. The adaptive workflow begins with CBCT acquisition,
registration, and AI-generated contours, which users can edit. Plans are recalculated
on the daily image and reviewed to determine whether adaptation is needed. If so,
optimization goals can be adjusted dynamically. Secondary dose calculation and
optional in vivo verification are available. A pre-treatment CBCT can verify stability
before delivery, and re-adaptation is supported.
Additionally, the uRT-linac 506c (United Imaging Healthcare Co. Ltd, Shanghai,
China) is a China Food and Drug Administration (CFDA) certified C-arm linac
equipped with fan-beam computed tomography (FBCT) capabilities [48, 49]. The
unit boasts a 16 slice helical CT imager coaxially attached to the linac gantry,
energies (maximum dose rate) of 6X (600 MU/minute) and 6FFF (1400 MU/
minute), dual layer collimating jaws, two opposing banks of 60 MLCs (0.5 cm width
MLCs in the central 20 cm and 1.0 cm width MLCs in the outer 20 cm), and a
maximum field size of 40 cm × 40 cm. A diagnostic‑quality helical CT acquired on
the CT‑integrated linac feeds VB‑Net autosegmentation of the target and OARs,
after which a hybrid voxel‑based optimizer (U‑Net dose‑prediction prior + preset
objectives) generates a single‑arc VMAT plan on‑couch. Couch shifts derived from
the CT are applied automatically during optimization, and any physician edits to
contours or objectives trigger instant re‑optimization to create an updated adaptive
plan. The approved plan is verified with in vivo EPID transit‑dose γ‑analysis (3%/
3 mm) and a low‑dose CT (or MV portals) before delivery [73].
15.2.1 Online-ART-specific challenges
Despite the early adoption of CBCT-based online ART by some institutions, many
technical challenges remain which prevent more widespread clinical adoption; these
challenges include, but are not limited to, uncertainties in dose calculations due to
sCT deformation [50–53], contouring limitations caused by suboptimal image
quality [54], the inability to perform traditional patient-specificQA[55, 56], and
significantly increased resource allocation compared to the standard-of-care [57, 58].
More physician, physicist, and dosimetrist time is required throughout the reference
planning process to evaluate the clinical objectives and carefully inspect the
contoured target and organ-at-risk structures, as adaptive plans must remain robust
to anatomical changes such as target deformation or shifts relative to nearby OARs
[25]. Online ART requires an adaptor, a clinical team member trained in organ
delineation, who is responsible for reviewing and, if necessary, editing all automatically generated contours of normal tissues and targets that influence plan optimization and evaluation. While often assigned to specific team members, this role can
be incorporated into various staffing models [59, 60]. Because these contours directly
impact the adapted plan, they must undergo careful and timely offline review by a
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Artificial Intelligence in Adaptive Radiation Therapy
physician, contributing to a significant increase in image review time compared to
standard IGRT workflows. Furthermore, online ART treatment times are substantially longer due to additional treatment processes and safety checks [61–63],
significantly minimizing patient throughput and/or extending the treatment day,
which subsequently affects hospital costs and staffing needs [58].
15.2.2 Patient and site selection
Because of the increased resource allocation associated with CBCT-based online
ART, identifying high-yield treatments is necessary for clinics seeking to implement
online CBCT-guided ART. This is made possible by bifurcating patients based on
either body site or patient-specific metrics. Body sites typically selected for CT/
CBCT-based online ART include those in the pelvic region with variable bladder
and rectal filling (e.g. prostate [39, 64–66], gynecological [59, 62, 67, 68], anal/rectal
[62, 69–73], and bladder cancers [69, 74–78]), advanced disease where tumor
regression is likely (e.g. head-and-neck [79–82], lung cancers [83–86], and seminomas
[87]), sites with increased set-up uncertainty and target deformation (e.g. accelerated
partial breast irradiation (APBI) [88, 89]), and high dose per fraction treatments near
critical OARs (e.g. SBRT for abdominal oligometastases [90], ultracentral thoracic
disease [25], and pancreatic cancer [54, 91]).
More recently, multiple groups have focused on identifying higher yield patients
within specific treatment sites to further save resources, as some patients receive
minimal dosimetric benefit with adaption even if they are receiving treatment to a
site that typically benefits from online ART. Moazzezi et al first discussed the
rationale for selecting patients for CBCT-guided ART prior to treatment because
they observed that certain patients experienced greater adaptive benefit than others
for prostate cancer [
66]. Yock et al investigated the use of statistically derived
adaptive triggers for standard and hypo-fractionated pelvic treatments, allowing
patients to be bifurcated as either adaptive or non-adaptive based on the difference
between scheduled (initial plan recalculated on daily anatomy) and reference plan
metrics [92]. Ghimire et al utilized a LASSO machine learning regularization
technique to forecast online ART dosimetric benefit for cervical cancer patients
based solely on reference plan dose metrics, enabling a priori bifurcation of patients
into adaptive and non-adaptive workflows [93]. Furthermore, Pogue et al utilized
multiple supervised and unsupervised machine learning approaches for a priori
selection of optimal stereotactic APBI patients based on reference plan metrics, an
example of which is shown in figure 15.2 [94, 95]. These studies demonstrate the
feasibility of implementing models and techniques to identify patients who would
benefit from adaptive therapy, potentially supporting broader adoption of CBCTguided online ART by enabling more efficient triage of clinical resources.
15.2.3 Simulation
The standard CBCT-based online ART simulation process largely aligns with
standard-of-care procedures, with a few exceptions. For example, daily ART
auto-contours may erroneously include high-density structures if contrast was
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 15.2. (a) Receiver operating characteristic curves when using a univariate model, a multivariate
training model using the entire dataset, and using a leave-one-out cross-validation multivariate model.
Youden’s indices (circles) illustrate the thresholds resulting from maximum differences between true positive
and false positive rates. (b) Confusion matrix heat map for the univariate ipsilateral Breast V15Gy model.
(c) Confusion matrix heat map of the multivariate validation model. (Reproduced from [
The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd.
CC BY 4.0.)
95]. Copyright 2024
present in the planning CT scan, thus the clinical team needs to have a deep
understanding of the online ART algorithms’ performance under different clinical
conditions. It is important to note that some online ART platforms offer unique
simulation capabilities that are not available in conventional IGRT workflows.
Nellissen et al and Oldenburger et al investigated the feasibility of simulation-free
palliative workflows for single visit online adaptive treatments of painful bone
metastases [51, 96]. Reference plans were generated using previous high-quality
diagnostic CT images, and resulting daily sCT images allowed for highly conformal
adaptive plan delivery in single patient visits with acceptable timeframes.
Additionally, Price et al performed in silico analysis of hippocampal-sparing whole
brain RT using an atlas based MRI to CT registration technique; the patient-specific
MRI was registered with the closest match from a library of CT scans, then both
images were imported into Ethos for daily adaptive re-planning [97]. Atlas based
simulation resulted in adaptive plans with improved hippocampal sparing and 45
min adaptive sessions. Furthermore, Nelissen et al successfully performed simulation-free consultation and palliative treatment for bone metastases with high
patient satisfaction scores and two hour timeframes as part of the prospective
FAST-METS clinical trial [98]. Lastly, advancements in CBCT technology have
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 15.3. Workflow utilized by the ‘All-in-One’ uRT-linac 506c, illustrating that the entire treatment
process (simulation, contouring, planning, and delivery) is performed with the patient on the couch.
(Reproduced from [
Physicists in Medicine.)
73] with permission from John Wiley & Sons. Copyright 2023 American Association of
improved image quality to the point where direct dose calculation is now feasible,
potentially eliminating the need for a separate simulation scan [94–97].
Some treatment units integrate diagnostic-quality fan-beam CT scanners, enabling simulation and treatment to occur on the same couch without patient
repositioning. These ‘all-in-one’ systems could streamline workflow, although
technical parameters vary by vendor and may influence clinical implementation
and image quality considerations. Yu et al demonstrated excellent deliverability
using an all-in-one treatment unit (uRT-linac 506c) for ten rectal cancer patients,
with a maximum time of 30 min from the start of simulation CT to completion of
beam delivery and in vivo QA; the workflow is illustrated in figure 15.3 [73]. While
early online ART systems relied on sCT generation for dose calculation, introducing
potential uncertainties, advances in CBCT technology now enable direct dose
calculation on CBCT images, reducing reliance on sCTs and improving dosimetric
accuracy [23, 85, 99].
15.2.4 Pre-planning review
CBCT-based online ART treatment planning requires workflows and considerations
beyond standard practice, including evaluation of sCT generation (if applicable)
based on planning CT attributes, assessment of target and structure derivation
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Artificial Intelligence in Adaptive Radiation Therapy
accuracy, consistency in structure naming, automated management of high-density
regions and artifacts, and ensuring plan robustness. Because online ART planning
workflows are still evolving, all members of the clinical team require an enhanced
understanding of the technical aspects involved in reference plan generation and
adaptive planning to ensure safe and effective implementation. Clinic-specific
workflows vary, with some teams assigning reference planning to physicists, while
others rely on dosimetrists with physicist support [100]. The increase in planning
complexity with online ART could lead to more errors, and thus more unintended
re-plans. Wegener et al performed failure mode and effects analysis of their
institutional events relating to treatment with Ethos, finding that the highest number
of events occurred in the conceptualization and contouring phase (i.e. creation of
prescription, intent, and planning directives) [101]. Because of this, many clinics
have implemented intent reviews to reduce error rates and provide physicist
technical support earlier in the treatment planning process [61, 102].
Beyond verifying standard prescription details (e.g. treatment site, laterality,
dose, number of targets and phases, and treatment frequency), additional technical
components specific to online ART platforms should be reviewed to ensure proper
workflow performance during the intent review phase. Planning CT images should
generally be contrast-free and acquired in a consistent breathing state (e.g. freebreathing or breath-hold), particularly for systems that do not support phase gating.
CT datasets should also be of manageable size to support efficient TPS optimization.
The accuracy of daily auto-contours is often influenced by both predefined structure
classification codes and the quality of planning CT contours; therefore, structure
naming, coding, and contour accuracy should be carefully reviewed and corrected
when necessary to prevent propagation of errors during online contour generation.
Target and optimization structure derivations should be reviewed for accuracy and
robustness to interfractional anatomy change, particularly when delivering high
dose near critical OARs [25]. Lastly, the planner and/or physicist should ensure that
the planning template is consistent with planning goals, and that goals needed for
daily ART plan evaluation are in the appropriate priority level to be visualized at the
console during treatment delivery.
Rahman et al performed fault tree and failure mode and effects analysis,
observing a large reduction in risk priority number for many adaptive specific
portions of plan preparation (tasks between simulation and plan optimization) when
pre-planning reviews were performed [63]. These results, highlighted in figure 15.4,
illustrate the increased physics and dosimetry resource requirements compared to
standard IGRT workflows.
15.2.5 Reference planning
Online ART workflows require rapid plan generation to fit within the time
constraints of same-day adaptive treatment. To meet this demand, modern TPSs
incorporate intelligent optimization technologies that automate key components of
the planning process. These systems translate clinical goals into optimization
objectives, generate supporting structures as needed (e.g. to resolve overlap or
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