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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
We illustrate with an example. The National Clinical Trials Network (NCTN),
an initiative by the National Cancer Institute (NCI), stands as a cornerstone in the
United States’ comprehensive cancer research efforts (figure 19.2). Designed to
conduct extensive, multi-institutional clinical trials, the NCTN significantly
enhances patient care and propels our understanding of cancer f orward. The
network comprises five principal groups, with four focusing on adult oncology—
the Alliance for Clinical Trials in Oncology, the ECOG-ACRIN Cancer Research
Group, NRG Oncology, and the Southwest Oncology Group (SWOG) Cancer
Research Network—and the Children’s Oncology Group (COG ) dedicated to
pediatric cancer research [42].
Central to the network’sefficacy in radiation oncology is the Imaging and
Radiation Oncology Core (IROC). IROC’s mandate is to ensure quality and
uniformity in imaging and radiation therapy across the NCTN’s clinical trials.
This involves standardizing imaging protocols, harmonizing radiation therapy
techniques, and assuring quality across various trial sites. This standardization is
crucial in trials where imaging and radiation therapy are integral, ensuring reliable
and comparable data across different study locations.
Complementing the role of IROC in NRG Oncology is the Center for Innovation
in Radiation Oncology (CIRO). CIRO focuses on the development and integration
of novel radiation therapy techniques and technologies in clinical trials. It serves as a
hub for innovation, driving advancements in radiation therapy by fostering research
collaborations and implementing cutting-edge treatment approaches in clinical
settings. The work of CIRO is instrumental in pushing the boundaries of radiation
oncology, aligning with NRG Oncology’s mission to integrate radiation therapy
with other treatment modalities [43, 44].
Figure 19.2. Use of DTs for simulation, analysis, and monitoring. (Image credit: National Cancer Institute
https://www.cancer.gov/research/infrastructure/clinical-trials/nctn/nctn-clinical-trials-network.)
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The NCTN is also supported by the Lead Academic Participating Sites (LAPS),
the NCI Community Oncology Research Program (NCORP), the Clinical Trials
Support Unit (CTSU), and various biorepositories. These resources collectively
enhance the network’s research capabilities, extending the reach of clinical trials and
ensuring the management of biological specimens.
The NCTN’s objectives include conducting essential phase II and III clinical
trials, integrating cancer biology studies within these trials, and advancing personalized medicine through biomarker research. The network’s impact on cancer
research is significant, leading to new treatment strategies, the development of
innovative drugs, and a deeper understanding of cancer biology. For professionals in
radiation oncology and clinical trials, such as those at Penn Medicine, the NCTN’s
focus on quality assurance in imaging and radiation therapy, bolstered by IROC and
CIRO, is of paramount importance.
The NCTN represents a comprehensive and forward-thinking approach to cancer
research. Its collaborative model, enhanced by the meticulous standards upheld by
IROC in imaging and radiation oncology and the innovative contributions of
CIRO, exemplifies a commitment to excellence in cancer treatment and research.
DTs have the potential to be incorporated into the infrastructure of clinical trials
by acting as virtual representations of patients. They can integrate various types of
data, including clinical, molecular, and therapeutic parameters, as well as sensor
data and living conditions. These virtual models are created using artificial
intelligence and real-world data, allowing for a comparison between the patient in
the clinical trial and their DT. Researchers can use DTs to analyse the outcomes of
patients receiving experimental treatments and compare them to their DTs, which
could potentially be significant for drug approval trials. Furthermore, the inclusion
of DTs in clinical trials can help generate real-world evidence and enhance
participant safety through efficient data integration and knowledge management.
There is also a proposal for the development and validation of a Virtual Human
Twin infrastructure to support the implementation of new DTs in healthcare
solutions [36, 45].
19.2.2 Quality assurance, compliance, and standardization
For nearly half a century, the NCI’s NCTN has financed practice-altering
randomized clinical trials. The stringent requirement for quality assurance, particularly in the fields of radiotherapy and imaging, plays a vital role in this network of
clinical trials. Failure to adhere to the prescribed parameters for radiotherapy
protocols has been linked to suboptimal clinical outcomes, including an elevated
incidence of toxicity, treatment failure, and overall mortality in clinical studies that
involve multiple institutions. Upon conducting a comprehensive assessment, discrepancies from the established radiotherapy standards were identified, such as
inadequate identification and treatment of target areas, excessive radiation doses
administered to normal structures, and prolonged radiotherapy treatments that
surpass the recommended durations. In cases where the anticipated distinction
between the experimental and conventional groups is minimal, reducing the
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uncertainty in radiotherapy dosage can lead to a significant decrease in the number
of patients required for a randomized clinical study. The accuracy of measurements
and the responsiveness of imaging metrics to genuine changes have an impact on the
sample size in studies that evaluate the effectiveness of therapy. When the precision
of positron emission tomography declines from 10% to 40% during a complete
measurement, the sample size can increase by a factor of 15 [43, 46–48]. The
establishment of the IROC as part of the NCTN was intended to ensure the quality
of imaging and radiotherapy. Likewise, the NRG Oncology CIRO was created as a
crucial component dedicated to radiotherapy advancements. IROC and CIRO
collaborate in the development of methodologies and exploration of research topics.
The functions of IROC and CIRO complement one another by guaranteeing the
quality of radiotherapy and the accompanying imaging, as CIRO provides stringent
guidelines that are strictly enforced through IROC’s core functions. The standardization of procedures, which is an essential aspect of IROC/CIRO’s quality
assurance program, also contributes to the reduction of variations in the collection
of radiation and imaging data, thereby allowing for the broader adoption and
application of artificial intelligence tools that have been developed using datasets
from a limited number of institutions [43, 44].
19.3 Case studies of AI in adaptive radiotherapy trials
19.3.1 Overview of guidance for advanced radiotherapy in clinical trials
Adaptive radiotherapy (ART) offers the capability to account for anatomical and
biological changes during radiation therapy. ART approaches include offline
adaptations between fractions, online adaptations prior to delivery, and real-time
adaptations during delivery [49]. These target systematic changes, daily variations,
and intrafraction changes, respectively. ART may also be anatomically or biologically guided based on imaging findings.
Several key technological components underlie ART implementation. Imaging
considerations include contrast, resolution, artifacts, field-of-view, and other properties that differ across modalities such as CT, CBCT, MVCT, MRI, and PET [49].
Deformable image registration enables structure and dose mapping between image
sets but requires extensive validation [49, 50]. Dose needs to be accumulated across
multiple image sets, which relies on accurate deformation vector fields [49]. For
online ART, rapid re-planning necessitates efficient recontouring, fast plan optimization, and real-time quality assurance [49].
Comprehensive quality assurance guidelines for ART are provided across NRG
Oncology CIRO publications. Critical areas requiring credentialing include deformable image registration, dose accumulation, end-to-end workflow testing, and
adaptive treatment plan quality assurance [49, 51]. Multi-institutional clinical trials,
such as those investigating FLASH radiotherapy delivered at ultrahigh dose rates,
have specific QA needs for consistent and safe implementation [51].
For clinically implementing ART, clear physician directives should determine
adaptations based on metrics such as target coverage violations or organs-at-risk
overdosing relative to protocol-defined constraints. Online ART requires substantial
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physician involvement, including target and organ delineation, plan approval, and
QA. Practical considerations such as offline versus online optimization and plan
adaptation frequency balance adaptiveness with efficiency. Across disease sites, the
greatest ART benefits occur when substantial daily anatomical variations happen
adjacent to steep dose gradients [49].
The integration of ART as primary or secondary objectives in clinical trials
should be clearly defined. To ensure protocol compliance, central review processes
are recommended for physician recontouring and plan quality assurance. ART
credentialing should review hardware, software, workflows, and decision-making
capacity at each institution. For example, credentialing templates and adaptive
radiotherapy physics language for protocols are provided for consistent trial
implementation [49].
19.3.2 AI in the radiotherapy clinical trial quality assurance processes
One of the AI applications to radiotherapy is to guide treatment planning with a
knowledge-engineering-based plan prediction approach. Knowledge-based radiotherapy planning employs machine learning algorithms to analyse a large dataset of
previous radiation therapy treatments [52]. This method leverages patterns found in
past successful treatments to guide the dose distribution for new patients, thereby
optimizing treatment effectiveness while minimizing exposure to healthy tissues. The
process involves training a model, often a neural network or a decision tree, on
historical treatment data, including patient anatomy, disease characteristics, and
successful dose distributions. This model then predicts an ideal treatment plan for
new patients based on their unique clinical features. One of the implementations of
knowledge-based planning, the RapidPlan (Varian Inc.), was utilized for three main
radiotherapy treatment planning activities related to several clinical trials.
The first activity focused on feasibility studies to establish dose constraints and
evaluate treatment planning solutions against protocol criteria. Studies were
performed for the NRG-GY006 (cervical cancer), RTOG1308 (lung cancer), and
NRG-HN002 (head and neck cancer) trials [53–55]. For NRG-GY006, an atlasbased active bone marrow-sparing model was built to ensure quality assurance for
intensity-modulated radiation therapy (IMRT) planning as part of the pre-treatment
review process. The model demonstrated the ability to generate plans meeting trial
objectives and consistency across institutions. For RTOG1308, a RapidPlan model
assessment showed that stringent dose constraints were achievable across patient
datasets from two institutions. Recommendations were made to optimize the NRGHN002 trial launch based on a feasibility study across multiple planning systems
and delivery techniques using benchmark patient cases. With minor modifications to
spinal cord maximum dose criteria, compliance was demonstrated across
institutions.
The second major activity was the use of RapidPlan models to enable online and
offline quality reviews of treatment plans submitted to trials [56–59]. Models were
trained on high-quality historical plans and used to evaluate and re-optimize new
patient plans. This improved protocol compliance, target coverage consistency, and
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organ-at-risk sparing compared to the originally submitted plans across trials,
including spine stereotactic radiosurgery (SRS) (RTOG0631) and lung cancer
(RTOG1308, photons, and protons). For head and neck cancer patients on the
NRG-HN001 trial, re-optimization with a multi-institutional model showed
improved organ-at-risk sparing in 33 out of 50 cases. Similar quality improvements
were demonstrated for proton plans relative to institution-submitted plans.
Finally, comparisons were made between RapidPlan and alternative knowledgebased planning solutions. Plan quality metrics and dose–volume histogram predictions generated by RapidPlan models versus PlanIQ models were equivalent for
RTOG0631 and RTOG0522 trials. For NRG-HN002 across planning systems,
protocol dosimetric compliance was achieved, confirming consistency [60]. In the
case of RTOG1308 lung cancer treatment, mean dose deviations up to 14 Gy were
observed between model predictions, suggesting superior performance of modelbased planning for challenging geometries.
The studies demonstrate multi-pronged utilities of RapidPlan models for radiotherapy trial quality assurance across disease sites, treatment modalities, and phases
of trial execution. The knowledge-based planning approach enables assessments of
planning consistency and protocol deviations with automated re-planning
capabilities.
High-quality data in radiotherapy clinical trials are crucial, requiring protocolcompliant contours. Traditional manual contour reviews are time-intensive and
subjective. The AI-based algorithm was implemented to enhance the quality
assurance workflow, offering a more objective and efficient process. Utilizing deep
active learning, the developed system employs convolutional neural network models
trained on high-quality contours for automated evaluation, employing metrics such
as the Dice score and Hausdorff distance for decision-making. Results showed high
consistency, accuracy, and sensitivity across multiple organs. This automated system
is implemented across various disease sites through collaborations with AI segmentation commercial solutions [44, 61].
AI is applied to obtain outcome-driven quality assurance criteria using
interpretable machine learning strategies, such as the explainable boosting machine
(EBM). EBM is a transparent, tree-based model that simplifies understanding
individual feature impacts on predictions. EBM was adapted for survival analysis
in radiotherapy, modeling survival as a classification or regression task to identify
critical dose–volume constraints for cardiopulmonary structures affecting survival
outcomes in advanced non-small cell lung cancer cases [44, 62].
19.4 Ethical and regulatory considerations
19.4.1 Patient consent and data privacy
Obtaining patient consent and safeguarding privacy in clinical trials involves
navigating ethical, legal, and practical terrain. At the core lies respect for patient
autonomy through informed consent, upholding dignity as active participants rather
than passive subjects. Meticulously designed consent processes convey comprehensive yet comprehensible information on study purpose, methods, risks, and benefits.
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The documented agreement serves as a testament to patients’ understanding and
willingness.
Consent and confidentiality are legal imperatives, too, with regulations such as
HIPAA enforcing privacy standards. Non-compliance risks significant consequences
for individuals and institutions. This legal framework actualizes ethical ideals into
enforceable duties. Breaches erode patient trust and transparency, foundational to
accurate data collection. Rigorous protocols anonymize data and restrict access to
maintain integrity.
Navigating valid consent poses challenges, including disabilities impeding communication, fluctuating capacity, and emergencies precluding engagement [63].
Pragmatic trial consent waivers balance ethical rigor with feasibility and risk
mitigation across patients, clinicians, and systems [64]. Cross-disciplinary collaboration is imperative to update guidance and address research gaps. Human-centered
solutions such as consent mechanisms, privacy assistants, and dynamic consent
platforms further uphold ethical ideals.
Training requirements ensure all personnel adhere to exacting privacy and
confidentiality standards. Review boards scrutinize protocols, serving as an oversight layer reinforcing patient rights and autonomy. Complex, ethical, legal, and
practical vigilance is essential for consent and privacy to enable advancement
through research while minimizing patient risk and maximizing agency in
participation.
This framework of multifaceted standards aims to shift clinical trials from a
paradigm of patients as passive subjects to one of active collaboration built on trust,
transparency, and mutual understanding. Truly informed consent respects participant dignity while propelling scientifi c progress. Although navigating regulatory,
ethical, and communication terrain poses challenges, solutions grounded in human
values offer paths to uphold safety and autonomy at once. Overall, the clinical trials
ecosystem must reinforce patient centeredness—from reviewing protocol to analysing data to translating findings to practice. Through consent built on education
rather than obfuscation and rigorous privacy standards, advancement and ethics
intersect rather than conflict. The result, research with the participant rather than
research on the participant, offers a blueprint for progress reflecting core human
values.
19.4.2 Bias, fairness, and transparency
Bias, fairness, and transparency in clinical trials raise significant validity and ethical
concerns requiring comprehensive solutions.
Bias manifests in the selection of non-representative participants, systematically
skewed measurements, and selective reporting that misrepresents outcomes. Core
strategies to mitigate bias include randomization to minimize the confounding
influence variables, double-blinding studies so neither participants nor researchers
know the treatment versus control group, and pre-registering trial protocols to
prevent manipulating reporting [65].
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Fairness in clinical trials involves equitable selection and treatment of participants. Historically, certain groups such as women, minorities, and the elderly were
underrepresented, lacking data on how treatments affect them differently. Solutions
include proactive diverse recruitment, so results apply more broadly, equity-focused
protocols addressing inclusion, and community engagement to understand needs.
Transparency issues arise from insufficient disclosure of methodologies, changes,
conflicts of interest, and data management. This erodes public and scientific trust.
Solutions encompass open access to protocols and results for scrutiny, independent
review boards, and requiring conflict of interest disclosures.
In radiotherapy and imaging, ensuring the techniques evaluated are free from bias
and fair across patient demographics is critical. Maintaining transparency in testing
and reporting establishes research credibility and ethics. Solutions such as diverse
recruitment, rigorous protocols, and transparent reporting are instrumental.
Addressing multifaceted bias, fairness, and transparency issues requires a
comprehensive approach targeting research culture, reporting biases and methodological shortcomings [66, 67]. The EU Clinical Trials Regulation improved interventional drug trial result transparency, but a ‘two-class system’ emerged to distinguish
these from other studies [68]. Institutions, funders, and ethics committees should
improve transparency across all clinical studies. The EU Portal Clinical Trials
Information System also aims to make study documents more transparent for
independent analysis of consent and harm–benefit assessment [69]. Ultimately
transparency entails publicly sharing information on trial design, conduct, results,
and data.
Progress notwithstanding, continued vigilance is essential from multiple stakeholders, wielding an array of transparency tools to uphold ethical, unbiased clinical
research.
19.4.3 Regulatory guidelines and compliance
Major regulations are crucial to ensure clinical trials uphold safety, efficacy, and
ethical standards. Key guidelines include the International Conference on
Harmonization (ICH) Good Clinical Practice (GCP) standards, mandating credible,
accurate reporting with subject protections [70]. The Declaration of Helsinki details
ethical imperatives such as informed consent, letting participants withdraw, prioritizing welfare, and requiring independent committee reviews [71].
For US trials, Food and Drug Administration (FDA) regulations cover
Investigational New Drug applications, protecting subjects, Institutional Review
Board (IRB) rules, and reporting adverse events [72]. The European Medicines
Agency (EMA) oversees European Union trials, regulating authorization, conduct,
and reporting [73].
Navigating complex, detailed regulations poses compliance challenges. With
multifaceted protocols, organizations may struggle to fully implement every requirement. Global trials must reconcile varied or conflicting country-level rules.
Managing voluminous quality data while upholding accuracy, security, and
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confidentiality strains resources. Obtaining informed consent and ethical approvals
grows more complex with diverse, vulnerable groups.
Solutions include comprehensive training to ensure staff understand relevant guidelines, standardized operating procedures aligning with regulations, independent
ethics committees providing guidance, quality assurance audits early addressing
non-compliance, and advanced data systems aiding management.
This intricate landscape demands vigilance from sponsors, investigators, and
reviewers so trials uphold the most rigorous scientific and ethical standards. With
human health at stake, even minor non-compliance could undermine safety or
efficacy findings. Yet complex regulations also safeguard against exploitation,
preserving rights and welfare consistent with research ethics principles. Although
advancing medical knowledge through trials may serve public health aims, the
imperative of monitoring standards helps ensure this progress also aligns with public
ethical priorities.
Through multifaceted checks-and-balances—extensive guidelines, intensive train-
ing, and oversight systems—the clinical trials ecosystem seeks to foster advancement
with accountability. By upholding consistency across geographies and populations,
regulators enable generalizing insights more responsibly. And by upholding
informed consent, subject welfare and data ethics, they reinforce research alignment
with participant-centered values. The intent is to catalyze progress and protection in
equal measure. While no framework fully eliminates ethical breaches, an infrastructure prioritizing safety and dignity from study design through result dissemination aims to advance science grounded in conscience.
19.5 Future directions and challenges
19.5.1 Emerging technologies and techniques
Emerging radiotherapy technologies such as radiopharmaceutical therapy, FLASH,
and MR-guided radiotherapy offer opportunities to advance cancer treatment
through enhanced tumor targeting and normal tissue sparing. However, optimizing
clinical integration requires rigorous trials evaluating dosimetry, fractionation,
disease site dependencies, and long-term impacts [49, 51, 74]. Spatially fractionated
regimens may augment immunogenic response when combined with immunotherapy, but consensus guidelines are lacking on appropriate applications [75].
Proton therapy trials should collect comprehensive data on dose, linear energy
transfer (LET), and outcomes to inform LET-based treatment planning and models
of relative biological effectiveness (RBE), which likely exceeds the standard value of
1.1 in some tissues. Re-irradiation trials warrant meticulous cumulative dose
assessment through prior record completeness, image registration for anatomical
changes, and biological correction models [76].
Quantum sensing and computing technologies promise future transformations in
imaging, treatment planning, and research [77]. Integrating artificial intelligence can
facilitate accuracy, efficiency, and quality assurance across radiotherapy trials but
requires addressing inherent biases and lack of transparency [78]. Opportunities
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include knowledge-based and biological image-guided treatment planning, automated segmentation, motion management, and predictive modeling [62, 79, 80].
Advancing radiotherapy hinges on carefully designed prospective trials to
optimize modality-specific protocols while collecting comprehensive data on treatment factors, response, and toxicity. Multidisciplinary collaboration and infrastructure modernization, paired with diligent quality assurance, are imperative to
firmly establish safety and maximize therapeutic potential. AI-driven tools may
accelerate this mission but require thoughtfully crafted validation studies to ensure
robust performance and clinician trust.
19.5.2 Alternative strategies
DTs represent an emerging technology with the potential to advance biomedical
research and personalized medicine. When paired with clinical trial data, DTs of
patients could help optimize and individualize therapies. However, several challenges must be addressed [81].
A key challenge is model complexity—embracing complexity risks models
becoming too computationally intensive while oversimplifying risks and losing
critical details. Approaches must balance fidelity and feasibility. There were
successes in applying high-resolution gene-level models to animal systems and
patient cells to determine optimal drug therapies. However, validating predictions
remains difficult without human trials.
Capturing spatial and temporal considerations with imaging, molecular simulations, and mathematical models enables key insights, such as predicting chemotherapy delivery and treatment responses. Yet a mismatch persists between
measurable biological data and computational capability. Strategies are needed to
integrate or generate missing measurements across timescales.
The diversity of models and data is also an obstacle. While benchmark digital
patients and populations offer promise in evaluating medical devices or running in
silico trials, integrating mechanism-based physiological models with sparse, heterogeneous patient data is an open challenge. Techniques leveraging optimal experimental design could strengthen predictive performance from population to
individual.
Connecting data across biological scales to build robust multiscale DTs remains
an active research gap. Opportunities exist to interface models rather than data
directly but communicating across scales and ensuring model composability and
reproducibility is nontrivial. Iterative, modular approaches accounting for uncertainties may help bridge insights from molecular simulations toward eventual
clinical application.
Privacy and ethical concerns abound regarding access, control, and transparency
surrounding patient data used to develop, update, and enrich medical DTs.
Engaging participants in managing privacy risks and communication of uncertainties linked to model predictions is paramount, as is ensuring equitable access to
emerging DT technologies.
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While much potential exists for medical DTs to enhance decision-making,
prediction, and optimization of interventions across the clinical trial ecosystem,
solving complex data integration, modeling, validation, and ethical challenges
remains imperative to eventual real-world implementation. Focused efforts on
priority gaps could unlock this nascent technology’s full translational power [81].
19.6 Conclusion
In conclusion, AI promises to profoundly enhance clinical trials across myriad facets,
from protocol design to data analysis to safety monitoring. AI-based solutions
can optimize patient recruitment, reduce costs, accelerate timelines, and extract deeper
insights from multifaceted data. Technologies such as DTs and virtual modeling
further expand capabilities, enabling sophisticated simulation and prediction
unachievable through conventional methodologies.
However, thoughtfully crafted validation frameworks and ethical guidelines are
imperative to guide AI integration responsibly. Models must demonstrate reliable,
unbiased performance across diverse demographics before influencing high-stakes
medical decision-making. Patient privacy, transparency, and autonomy require
ongoing safeguarding as data sharing and analytics expand.
Nevertheless, the potential advantages of judiciously incorporating AI are
substantial. Personalized, predictive, and dose-optimized treatment plans can be
formulated through AI-assisted knowledge. Automated segmentation, registration,
and motion management streamline workflows. Risk models calibrated on population data may inform individual risk assessments with greater accuracy.
Ultimately, AI in clinical trials aims not to supplant physicians but to augment
human intelligence—equipping practitioners to base recommendations on comprehensive perspectives while retaining experience-driven nuances. This fusion of
computational power with clinical acumen may propel more precise, effective,
and democratized research, unlocking scientific insights at unprecedented scale and
speed. The path ahead undoubtedly entails obstacles, but the promise of ameliorating patient outcomes through data-enlightened understanding compels persistent,
collaborative progress. With ethical vigilance and visionary drive, AI-empowered
clinical trials can catalyze a new epoch of evidence-based care and scientific
discovery benefitting all.
19.7 Summary
AI is reshaping clinical trials by enhancing efficiency, precision, and personalization
in research processes. AI-driven tools streamline patient recruitment, optimize trial
protocols, and enable adaptive monitoring, resulting in faster timelines and
improved outcomes. Technologies such as DTs allow for the creation of virtual
patient models that simulate treatment responses, facilitating personalized therapy
optimization while minimizing risks. These advancements significantly contribute to
improving safety, efficacy, and cost-effectiveness in medical research. However, the
integration of AI into clinical trials presents challenges, including concerns around
data privacy, transparency, ethical use, and potential biases in decision-making.
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