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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
6.2.3.2 Dosimetrists
Dosimetrists play a large role in both online and offline ART. In offline ART, their
role may be more straightforward—optimizing the new adapted plan using the
image from re-simulation following the same approach as the initial plan. However,
they need to have an intimate understanding of the planning strategies and concerns
in the adaptive approach. Their role in OnART can be much more involved. There
is less time to digest the case, and even less time to re-contour and re-optimize the
plan. This demands thorough training and experience from dosimetrists so that they
are comfortable in the adaptive environment.
6.2.3.3 Physicians
Physicians play an instrumental role in the ART workflow, primarily in deciding
when to adapt, and after re-optimizing, whether to accept the new plan or choose to
deliver the original plan. They may plan to adapt ahead of time or know when to
trigger adaptation based on the circumstances during treatment, such as the changed
positions of internal organs or changes in the tumor size and shape. In some cases, they
may drive the decision to escalate dose based on what they see during treatment [47].
This demands new expertise from physicians, including new trials to investigate
new approaches and the impact of ART. Physicians also play important roles in
reviewing and approving imaging, contours, and plans. The fast-paced OnART
environment thus demands an increased presence and engagement at the treatment
machine.
6.2.3.4 Physicists
Along with sharing the roles of the dosimetrists, physicists provide the technical
expertise to commission and implement the ART workflow in their clinics. They
must understand the details of the imaging, registration, contouring, optimization,
dose calculation, and QA to safely bring each stage to operation. They should also
ensure continuous safe operation of all procedures by working with therapists,
dosimetrists, and physicians. Physicists are also responsible for establishing QA
procedures and educating the rest of the team on executing those procedures [37].
6.2.3.5 Collaboration
Although each member of the radiotherapy team bears many unique responsibilities
in the adaptive workflow, it is ultimately a highly collaborative environment.
Physician input on imaging and re-planning are crucial to aid the therapists and
dosimetrists in their roles. Physics input for safe and effective treatment delivery help
inform the physicians. The room is often full of different team members working
closely together. Furthermore, a strong collaborative effort is necessary to continue
pushing the field of ART forward. Kristy Brock discussed this importance in her
2019 article, calling for collaboration between physicians, physicists, and industry
partners to further improve the clinical workflow, increase technical accuracy, and
enable the best decision-making ability in ART [28]. AI provides a promising avenue
to push the boundaries of every aspect of ART, and it will require a strong effort
from all parties to develop and implement this technology safely and accurately.
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6.3 Considerations for implementing online ART
6.3.1 Time as a limiting factor
The effective administration of ART, which responds promptly to changes in tumor
and surrounding tissue conditions, requires reliable and efficient clinical execution.
With advancements in rapid imaging acquisition and computational resources,
OnART has emerged to address inter-fraction variations based on pre-treatment inroom imaging. While offline ART primarily targets systematic errors manifested
between treatments, OnART can accommodate both random and systematic errors
in daily anatomy and set-up changes, thereby maximizing dosimetric benefits [48–50].
However, the successful implementation of OnART imposes unique requirements on
time and resource allocation.
Currently, only a limited number of OnART systems are available for clinical use,
which can be categorized into CT-based OnART, represented by Varian Ethos, and
MR-based OnART, exemplified by ViewRay MRIdian and Elekta Unity.
In an initial investigation into clinical OnART efficiency, a single institutional
study examined the treatment logs of 450 CT-based OnART fractions for various
sites, including the prostate, GYN, breast, lung, HN, as well as abdomen. They
reported an average duration of 37 ± 16 min for daily adaptive treatments, from
patient simulation to delivery completion. Specifically, the additional steps for
generating online adaptive plans took an average of 20 min [51]. Similarly, another
institution implementing CT-based ART reported on over 1000 fractions, indicating
an average time of 34.52 ± 11.42 min from start to finish, with physicist/physicianinvolved steps totaling around 20 min [52].
In MR-based OnART, the current workflow entails slightly prolonged t reatment sessions attributable to extended acquisition times and additional steps in
treatment planning. An institutional inquiry into 80 adapted pelvic and abdominal
SBRT fractions, conducted with the 0.35 T MR-based ViewRay MRIdian system,
unveiled an average overall session duration of 54 min, with the adaptive steps
pertaining to planning and evaluation consuming 31 min [42]. Similarly, another
institution employing a 1.5 T MR-based Elekta platform for 65 liver and pancreas
SBRT cases reported an average session duration just below 70 min (ranging from
50 to 90 min) [53].
The extended on-couch time during OnART imposes considerable pressure on
patient immobilization, as any movement by the patient during re-planning can
compromise the effectiveness of plan adaptation itself. This underscores the critical
need for fast and reliable solutions in re-planning, encompassing contouring,
registration, re-optimization, plan evaluation, and QA. On the other hand, the
increased workload associated with plan regeneration for each treatment fraction
stresses departmental resources. Consequently, there is a pressing need for enhanced
workflow efficiency and the adoption of automated processes. Considerations for
rapid plan generation and workflow automation will be discussed in detail in the
following sections.
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6.3.2 Implications for fast and reliable re-planning
6.3.2.1 OAR contouring
Based on current institutional reports, the additional time allocated for OnART replanning varies depending on the imaging modality: approximately 20 min for CTbased systems and 30 min for MR-based systems [42, 49, 53]. The bottleneck in this
process remains the contouring of OARs, which consumes over 10 min in both CTand MR-based OnART sessions, with some complicated treatment sites requiring
up to 24 min [54]. While deformable registration-based propagation has been widely
adopted in clinical practice to expedite these steps, it still involves significant manual
editing and evaluation.
Recently, there has been a surge in the development of deep learning (DL) based
auto-contouring algorithms. These algorithms have demonstrated comparable
quality to manual delineation on both daily KVCT or MR images and have
significantly accelerated contouring procedures to within seconds. Specifically,
several DL-based auto-segmentation networks have exhibited high accuracy in
OAR delineation and are readily available for clinical integration in prostate,
cervical, and HN cancers [55, 56]. Some studies have even achieved one-shot autocontouring on KVCT for up to 117 OARs throughout the body [57–59]. These
developments lay a robust foundation for minimal manual edits in current radiotherapy workflows for a broader range of RT applications, including total marrow
irradiation and cranial-spinal irradiation.
Moreover, attempts have been made to integrate labor-free auto-contouring into
OnART workflows for pelvic cancer treatments [60, 61]. While these attempts have
demonstrated satisfactory quality without manual edits for most patients, there are
still instances where manual intervention is required. Hence, continuous scrutiny of
daily auto-segmentation is necessary for its full integration into clinical OnART.
Additionally, the majority of these DL-based studies are primarily tailored for
the pelvic or HN regions [59], which are anatomically more rigid and therefore
exhibit fewer interfractional alterations. However, there remains a shortage of
auto-contouring methods for the thoracic and abdominal regions, where more
interfractional anatomical changes are anticipated. Despite the challenges of autocontouring in the thoracic and abdominal regions, it is anticipated that OnART will
offer greater dosimetric benefits in these areas. Hence, there is an urgent need for
further research in this domain.
6.3.2.2 Deformable imaging registration (DIR)
DIR plays a fundamental role in the OnART workflow, encompassing tasks such as
aligning planning CT scans with daily imaging, propagating target volumes, and
accumulating doses. With the accessibility of parallel computing within modern
OnART platforms, DIR operations themselves are not inherently time intensive.
However, the inherent uncertainties associated with DIR methodologies pose
challenges to downstream processes, necessitating meticulous manual scrutiny and
consequently elongating the duration of the OnART workflow.
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Artificial Intelligence in Adaptive Radiation Therapy
The primary uncertainties associated with DIR stem from the underlying assumption of homogeneous deformation properties within the image domain [62]. Most
current DIR algorithms essentially seek mathematical or numerical solutions for the
best match in the image space. Without prior knowledge of differential tissue
properties and a physical basis for expansion, contraction, and pose changes, DIR
algorithms struggle to effectively distinguish highly elastic structures undergoing
substantial morphological transformations, such as the bladder in different fillings,
from rigid structures undergoing positional adjustments, such as pelvic bones in
different positions. This often leads to implausible movements.
Various algorithms have been proposed to address the heterogeneous biomechanical properties of tissues by introducing non-uniform constraints, including
contour-guided registration [63], shape-based regularization [64], or local rigidity
penalties [62]. To further leverage biomechanical properties, continuum mechanics
have been incorporated into the design of regularizers, enforcing smoothed and
diffeomorphic transformations for more physically realistic movement [65, 66].
However, the complexity of domain discretization and resolution schemes limits
their clinical integration, particularly in OnART settings.
In addition to the inherent plausibility challenges within current DIR algorithms,
complex medical scenarios, such as surgical resections, nasal and pulmonary
congestions, and tissue inflammations, can lead to missing correspondences between
moving and target images, further augmenting uncertainty [67, 68]. Consequently,
to better align with the requirements of the radiotherapy field, there is a need for sitespecific fine-tunings that incorporate both physical and medical contexts.
With the adoption of encoder–decoder architectures and spatial transformer
networks, deep learning-based DIR, such as Quicksilver [69] and Voxelmorph [70],
demonstrate potential in accommodating the heightened computational needs for
biomechanically realistic solutions. Coupled with various weakly supervised training
strategies adapting to specific medical conditions in radiation oncology, AI-based
DIR shows promise in providing solutions to increasingly complex radiotherapy
needs within acceptable timeframes in clinical OnART [71].
6.3.2.3 Plan generation
Upon determination of OAR and target volumes, and registration of planning to
treatment imaging, subsequent adjustments to plan parameters are necessary. Replanning procedures can be categorized into two main types: adapt to position
(ATP) and adapt to shape (ATS). ATP involves translating the original plan to the
new isocenter and optimizing the weights and shapes of MLC segments, while ATS
essentially regenerates a new plan. Although ATS affords higher degrees of MLC
optimization to adapt to the new anatomy, it is more time-consuming [72]. The time
required for plan regeneration can vary from seconds to minutes, depending on the
adaptation mode and plan complexity.
ATP has been demonstrated to be cost-effective for adapting daily treatments
with minor anatomy variations. For instance, a prospective analysis of HN cancer
OnART in ten patients using a 1.5 T MR-linac revealed that the ATP workflow
resulted in only a 2% dose difference in target coverage and a high gamma passing
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Artificial Intelligence in Adaptive Radiation Therapy
rate. Although dose differences in OARs were higher, the delivered dose on the
summation plan remained statistically comparable to the reference plan, even with
one or more constraint violations in at least two fractions [73]. However, it should be
noted that when patient offsets exceed 2 mm, the ATP workflow fails to reproduce
clinically acceptable dose distributions [74].
To maximize the dosimetric benefits of OnART, ATS with a higher degree of
optimization capability is preferred, if resources allow. Winkel et al investigated
OnART for five cases with various inter-fraction anatomical changes: single lymph
node, prostate, rectum, esophagus, and multiple lymph nodes [72]. In both replanning modes, various optimization methods with increased degrees of freedom
were explored, including adapting segments only, optimizing weights from segments, optimizing weights and shapes from segments, as well as two options
available only to ATS: optimizing weights from fl uence and optimizing weights
and shapes from fluence. As expected, treatments with higher anatomical complexity
and inter-fraction changes require optimization methods with greater degrees of
freedom to achieve clinically acceptable plans. For instance, while the ATP workflow with optimization of weights only from segments sufficed for a single lymph
node case, ATS workflow with optimization of weights from fluence was necessary
for rectum and esophagus cases to meet the same outcome. In the more complex
multiple lymph node case, full online ATS optimization had to be used to meet all
constraints [72].
To expedite the optimization process, prevalent commercial OnART workflows
frequently utilize atlas and protocol-based algorithms [75]. Atlas-based algorithms
draw upon a repository of approved contours and plans to establish associations
between geometry and DVH, enabling the prediction of achievable DVH for new
patients with similar contours and treatment objectives. Conversely, protocol-based
algorithmsbeginwithuser-defined templates containing clinical goals and priorities,
iteratively adjusting the DVH until an optimal plan is achieved. These algorithms can
be augmented by DL models [76], which advance DVH prediction to 2D and 3D dose
predictions [77]. Moreover, leveraging historical patient plans, additional re-planning
steps, such as beam orientation selection, fluence map generation, and delivery
parameter generation, can be seamlessly integrated into a single DL task, further
enhancing automation [78]. However, despite the demonstrated enhancement in plan
efficiency shown by DL-based algorithms in preclinical validation, comprehensive QA
procedures are indispensable for their clinical integration into OnART.
Currently, there exists a trade-off between the time and resources allocated for
plan regeneration and the dosimetric benefits attained. Future research on sophisticated optimization algorithms and large-scale toxicity analysis are imperative to
substantiate the appropriate balance between planning time and dosimetric gain.
6.3.2.4 Quality assurance (QA)
Following treatment plan generation, QA becomes imperative prior to treatment
delivery. Since on-table patient-specific QA is unfeasible, leading commercially
available OnART platforms, including Varian Ethos, Viewray MRIdian, and Elekta
Unity, employ a rapid secondary dose calculation (SDC) method [42, 79, 80].
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Although vendor-supplied QA tools have demonstrated good agreement with posttreatment QA measurements in small-scale studies [81], failure mode and effects
analysis (FMEA) has revealed a 38% increase in the risk priority number for ART,
with significant risks associated with segmentation and treatment planning processes [82]. Complementary to vendor-provided SDC, Rippke and colleagues have
developed a QA analysis that re-examines additional factors, including absolute
volume changes and gaps in structures, electron density maps, and fluence
modulation complexity [83]. Their study revealed that errors, particularly those
related to contours, which may occur during OnART, can be identified through
supplementary QA measures, underscoring the importance of adopting additional
QA steps to ensure the safe delivery of OnART.
Moreover, beyond the errors inherent in conventional treatment planning and
delivery, OnART introduces distinctive procedures that may contribute to additional uncertainties. Kluter et al confirmed the additional risks posed by OnART,
with approximately one-third of the risks being specific to MR-linac systems [84].
Additionally, the daily imaging utilized in OnART typically exhibits lower quality
compared to planning imaging. This not only affects the delineation of OARs and
targets but also influences downstream procedures, such as daily imaging-based dose
calculation and accumulation [49]. Consequently, frequent end-to-end verification of
the adaptive workflow is recommended [52, 83].
6.3.3 Implications for automated workflow
ART programs are more demanding on clinical staffing levels than SRS/SBRT
programs [51, 85]. Despite the widely accepted benefits of reduced toxicity and
improved target coverage, the increased logistical and resource burden of OnART
limits its widespread implementation. Current clinical decisions within OnART are
primarily based on physicians’ or physicists’ experiences. In the absence of a
standardized framework, the implementation of OnART varies with limited consensus on patient selection, time to adapt, and algorithms to choose. Therefore,
paramount to the urgent need for fast and accurate re-planning tools, the establishment of a roadmap for automated workflows necessitates a standardized and
quantitative framework to support clinical decisions. This framework should not
only define action thresholds to initiate an OnART but also guide clinical decisions
to progress through each step of the OnART process.
6.3.3.1 Action levels to initiate OnART
To establish a standardized decision-making framework, the first step involves
quantifying anatomical deviations, which greatly influence the decision to proceed
with OnART. Anatomical deviation primarily arises from inter-fraction motion,
which varies in magnitude across treatment sites. For instance, it can be on the scale
of millimeters in the prostate, whereas in the liver and pancreas, it can extend to
centimeters [86]. However, inter-subject variations exist, as deviations exceeding
1 cm in the prostate are detected occasionally [86]. Adopting the concept of 4D
planning, which integrates pre-treatment 4D imaging that is predictive of potential
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Artificial Intelligence in Adaptive Radiation Therapy
movement and incorporates adjusted intra-fraction margins into re-planning, has
the potential to greatly improve automated workflows and guide session scheduling.
Heterogeneous treatment responses also contribute to anatomical changes,
notably through within-course tumor shrinkage, which are commonly reported in
lung [ 87–89] and HN radiotherapy [90]. This shrinkage can vary significantly,
ranging from 1.2% per day in lung treatments to as high as 70% in HN treatments
[90, 91]. Such changes are often accompanied by secondary shifts in the surrounding
OARs. For instance, a reduction in volume of up to 30% in the parotid glands has
been observed during HN radiotherapy, along with a tendency to shift towards
higher dose regions [92]. Therefore, a quantitative metric comprehensively evaluating the overall anatomical deviation is in demand.
Furthermore, relying solely on univariate distance and volume as the primary
descriptors for quantifying anatomical changes may not adequately capture critical
changes with the most significant dosimetric impact. In the case of hollow structures
such as the bladder, rectum, esophagus, and ventricles, morphological alterations
often stem from variations in internal fillings, which may not substantially affect
dosimetry. Consequently, parameters developed over tissue wall thickness or
surfaces become more relevant for assessing dosimetric changes [93, 94].
Specifically, surface modeling of bladder inter-fraction motion changes has revealed
that while significant motion may occur, it predominantly affects the superior–
anterior bladder surface, with no discernible dosimetric impact on high-dose regions
proximal to the planning target volume (PTV) [95]. Further research is needed to
establish a quantitative relationship between anatomical changes and resultant
dosimetric consequences to facilitate OnART decision making.
6.3.3.2 Automated plan evaluation
As previously discussed in section 6.3.2, current auto-contouring methods often
require manual review. Given the absence of ground-truth contours on daily
imaging, expediting the review process necessitates offline selection and tuning of
auto-contouring algorithms. Quantitative measurements of contour accuracy commonly fall into two categories: overlapping-based metrics, such as the Dice
similarity coefficient (DSC) and Jaccard index [96], and distance-based metrics,
such as the Hausdorff distance (HD) [97]. Recently, a surface-based refinement of
overlapping metrics, known as surface DSC, has shown higher clinical acceptability
compared to traditional metrics [98, 99]. While there is no gold standard available
during OnART, evaluating the dosimetric consequences is more relevant for
assessing the clinical acceptability of auto-contours in scenarios with limited time
and resources.
Several studies have investigated the dosimetric impact of auto-segmented
contours on downstream processes and have revealed minimal differences compared
to manual contours. For instance, in a study involving 20 lung SBRT patients,
Vaassen et al compared DVH parameters among plans optimized using five contour
sets: fully manual, atlas-based, atlas-based with manual adjustment, deep learningbased, and deep learning-based with manual adjustment [100]. They found that the
dose variations resulting from automatic contour variations were comparable to or
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Artificial Intelligence in Adaptive Radiation Therapy
lower than the intra-observer contour variability. However, manual editing was
necessary for OARs with maximum dose constraints, such as the heart [100].
Similarly, in a study with 15 prostate cancer patients, Zabel et al found no significant
differences in clinically relevant dose–volume metrics between auto-segmented
bladder and rectum contours [101]. In a larger study involving 247 cervical cancer
patients, Rigaud et al reported differences in DVH metrics between auto-segmented
and manual contours to be within 1% and 1 Gy [102]. Nonetheless, not all
investigated auto-contouring workflows present negligible OAR dosimetric impacts
[100, 103]. Additional comparative analyses focused on site-specific dosimetric goals
are crucial for determining which OARs require increased scrutiny during online
review. This refined approach empowers clinicians to make nuanced decisions
aligned with individual patient needs and treatment objectives.
Treatment planning evaluation relies on dosimetric endpoints for both target
volumes and OARs. These endpoints are commonly expressed through DVHs,
including parameters such as maximum and minimum dose (D
dose received by at least n% of the structure’s volume (D
structure receiving at least n Gy (V
). Additionally, metrics such as conformity
nGy
), and the volume of
n%
max
and D
min
), the
index, homogeneity index, and gradient index provide further insights into dose
distribution beyond 1D DVHs. To expedite online plan evaluation, structured
checklists of these metrics are generated to assess plan adherence to constraints
[104]. The use of auto generated checklists was found to increase the error detection
by 20% [105]. However, given that not all constraints can always be met, there is
often a trade-off between target coverage and OAR sparing, particularly in cases
with close proximity to OARs and limited planning time. In such scenarios,
physician input is necessary to prioritize objectives. To integrate both objective
and subjective preferences and enable ranked acceptability, Ventura et al proposed
weighted scoring of dose constraints according to physician preferences, presented in
a graphical radar plot [106]. To yield more insights on plan quality other than the
commonly used DVH metrics, Ceballos et al further extracted 60 non-conventional
parameters that specifically probe for hot and cold spots, and 320 radiomic features
from the 3D dose distribution to train a random forest regressor for prostate cancer
RT plan quality evaluation. The resulting machine learning model using a
combination of DVH and dose radiomic features achieved high grading accuracy
compared to the physician’s evaluation [107].
In addition to determining whether a plan meets certain constraints, an
alternative approach to defining plan acceptability involves assessing whether the
current plan achieves the best possible dosimetric endpoints. This entails comparing
the current DVH or dose distribution with predicted values [108, 109]. By
integrating this evaluation criterion with the plan generation process using the
predicted DVH or dose to guide plan generation, the resulting plan is deemed
‘optimal’ without the need for further evaluation. Indeed, several assessments of
non-manually intervened auto-planning have demonstrated that such plans are noninferior to human-generated plans in multiple sites, including prostate, endometrial,
lung, and head and neck [110, 111]. Auto-planning even exhibits greater OAR
sparing [110], increased dose conformity, and reduction of integral dose [111].
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Artificial Intelligence in Adaptive Radiation Therapy
Coupled with the auto-segmentation process, a fully automated contouring and
planning workflow tested in nine prostate cancer IMRT patients achieved
acceptable target coverage and reduced mean dose to the bladder and rectum
[112]. Although still in its nascent stage, with advancements in auto-contouring and
auto-planning algorithms, the plan generation process itself shows promise for selfapproval in the future.
6.3.4 Clinical considerations
As AI technology continues to advance, it holds the promise of overcoming current
technical limitations. With ongoing progress in AI, the generation of treatment plans
can become increasingly efficient, with DL algorithms capable of producing plans in
as little as 20 s, while knowledge-based planning may take up to 15 min [113, 114].
This reduction in planning time has the potential to significantly decrease the cost of
ART, thereby increasing its accessibility and benefits for a larger patient population.
Simultaneously, alongside the broader adoption of OnART facilitated by these
technical advancements, the concept of ‘adaptive’ therapy undergoes an expansion.
At its fundamental level, adaptive therapy involves modifying existing plans to
accommodate known anatomical changes, while at its more sophisticated stage, it
encompasses dynamically adjusting clinical objectives in response to tumor behavior
and prognosis. However, uncertainties on treatment response, lack of knowledge of
toxicity, and inter-patient heterogeneity challenge the clinical decision making.
The integration of functional imaging into ART represents a dynamic area of
ongoing research, as functional responses often precede anatomical changes.
Advanced imaging modalities, particularly MRI and PET, offer insights into tumor
function, enabling early assessment of tumor response and resistance and thereby
supporting prescription adjustments [115]. Recent studies have highlighted the
predictive and prognostic value of diffusion-weighted imaging (DWI) across various
cancers, including rectum, cervix, prostate, HN and brain [116, 117]. Perfusionweighted MRI, notably dynamic susceptibility contrast (DSC)-MRI, provides
additional physiological information and demonstrates correlations with brain
glioma progression and treatment response in normal brain tissue [118, 119].
While DWI or DSC imaging modalities have an implicit correlation with the
underlying biology and physiology of tumor response [116], PET imaging is more
closely related to cellularity and proliferative activity, which are two major
indicators of tumor aggressiveness. Furthermore, PET has demonstrated the
capability to differentiate necrosis, fibrosis, or radiation therapy-induced inflammation, as well as hypoxic tumor cells, which are a hallmark of radioresistance [120].
With the increasing utilization of MR-linac and the emergence of PET-linac,
biologically adaptive OnART becomes feasible. However, the reproducibility of
quantitative functional biomarkers depends heavily on imaging devices and protocols, as significant inter-system and inter-sequence variability have been observed
[121, 122]. Consequently, quantitative functional measurements developed on
diagnostic imaging systems or sequences may not translate reproducibly to hybrid
MRI- or PET-linac scanners. Moreover, imaging processing methods introduce
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Artificial Intelligence in Adaptive Radiation Therapy
additional uncertainties to the development of quantitative biomarkers. For
example, the selection of diffusion resultant decay models and the fit quality
influence the accuracy of DWI-derived parameters [116]. RT-induced perfusion
changes in normal tissue may bias the DSC metrics if selected as a reference region,
potentially overestimating tumor physiological response [118]. Additionally, various
correction factors can be applied to calculate the standardized uptake value (SUV)
in PET, albeit contradictory results have been reported regarding which SUV
calculation method best correlates with the glucose metabolic rate [122].
To develop optimal strategies for integrating functional imaging into RT, larger
clinical trials with standardized imaging protocols across multiple institutions are
imperative. The rising application of DL-based imaging reconstruction and postprocessing allows for fast in-room scans with quality comparable to or exceeding
state-of-the-art diagnostic standards [123–125]. Complementing the technical
advancements in imaging acquisition is the increasing integration of quantitative
imaging and machine learning applications into the radiation oncology workflow,
notably through radiomics analyses. These developments offer an unprecedented
opportunity to refine the assessment of early treatment response, OAR toxicity, and
long-term clinical outcomes at an individual level [126, 127]. Such endeavors would
enable more robust and reliable outcome prediction, ultimately enhancing clinical
decision making.
6.4 Summary
In this chapter, we discussed the evolution of radiotherapy, which has seen
significant improvements to conformality and precision over the past few decades.
Each technological leap has highlighted the remaining assumptions in our processes
and pushed our field further forward. The rise in prevalence of image guidance
emphasized how we still plan on a static snapshot of the patient’s anatomy.
Adaptive radiotherapy evolved to address the day-to-day variations and systematic
changes in anatomy. In the early stages, technological limitations relegated
adaptation to offline re-simulation and re-planning. Recent years have seen a
widespread push toward OnART, but the current solutions remain bottlenecks to
clinical throughput. Several tasks in the adaptive workflow have high potential for
acceleration through AI automation, and these applications will be discussed in
detail in subsequent chapters.
References
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[2] IAEA 2008 Transition from 2-D radiotherapy to 3-D conformal and intensity modulated
radiotherapy Technical Report IAEA-TECDOC-1588 International Atomic Energy
Agency, Vienna
[3] Svensson G K 1984 Quality assurance in radiation therapy: physics efforts Int. J. Radiat.
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