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Artificial Intelligence in Adaptive Radiation Therapy
Medical images, which include various modalities such as MRI, CT, and PET,
play crucial roles in clinical decision-making throughout the diagnosis and treatment
process. Foundation models hold immense potential to transform the utilization of
medical images [115]. For instance, it is poised to revolutionize the field by
introducing a new generation of versatile digital radiology assistants [125]. These
assistants can support radiologists throughout their workflow, significantly reducing
their workloads. Additionally, foundation models can integrate imaging with other
multi-modal sources such as text, providing both model auditing and annotated
images [126]. Text-to-image generative models, such as DALL-E [127], demonstrate
promising performance in medical image generation and augmentation [128].
Biological data—including DNA and RNA sequences, genetic variations such
as DNA copy number variations and DNA methylation patterns, and gene
expression profiles from miRNA and single-cell RNA —hold significant potential
for guiding decision-making in precision oncology. Public databases such as
TCGA, Gene Expression Omnibus [129], and Protein Data Bank [130] serve as
invaluable resources for researchers, enriching our understanding of cancer
biology and guiding personalized treatment strategies to improve patient outcomes. Ongoing efforts to develop foundation models for biological sequences
[131] facilitate various tasks, including multi-omic integration [132], RNA and
protein function prediction [133], and genetic variant effect prediction [134].
Additionally, various foundation models have been utilized to assist in determining treatment options based on molecular profiles and genetic alterations for
cancer patients [135]. Looking ahead, foundation models hold tremendous
potential for integrating multi-modal data, such as spatial transcriptome, DNA
and RNA raw sequencing data, and accompanying EHRs and medical imaging, to
transform traditional analytical approaches in cancer prognosis.
In summary, the integration of foundation models into clinical decision support
systems holds great promise for advancing medical decision-making processes,
although challenges remain in ensuring accuracy, privacy, and safety. Ongoing
research and development efforts are critical in addressing these challenges and
realizing the full potential of AI in healthcare.
5.5 Challenges and future perspectives
While the potential of big data in radiation oncology is enormous, its application is
not without challenges. With data coming from various sources and in different
formats, one of the foremost challenges is to ensure data quality and standardization. Robust model development and implementation require standardizing data
analysis frameworks.
Most studies trained their AI models using a single-institutional dataset, which
may lead to poor model generalizability in other institutions. This happens due to
domain shifts arising from variations in patient populations, clinical protocols,
imaging equipment, etc. Transfer learning [136] could help address domain shift
issues and improve model robustness in real-word applications. Multi-institutional
collaboration is another approach to reduce data biases, comply with regulatory
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Artificial Intelligence in Adaptive Radiation Therapy
guidelines that emphasize inclusivity and fairness, and enhance model performance.
However, sharing data across institutions could potentially pose risks to data
privacy and security. Given the sensitive nature of patient data, it is critical to
ensure data confidentiality and protect against data breaches, which requires robust
cybersecurity measures and adherence to regulations such as HIPAA. Federated
learning is another essential technique to address the risks of data sharing across
different institutions.
Translating the values from big data into clinical practice presents further
challenges. For example, expensive prospective clinical trials may be required to
test the safety and efficacy of the models for regulatory approval. Once approved,
physicians must understand and trust AI models to be on board with clinical
implementation. The decision-making process in many AI models’ learning,
particularly deep learning, is frequently perceived as a ‘black box’ due to their
intricate and opaque decision-making processes. Explainable AI (XAI) [137] plays a
crucial role in bringing transparency and building trust in the high-stakes clinical
decision-making process that directly impacts patient lives. For clinicians, understanding and justifying AI-driven predictions is essential for patient safety and
ethical care. By providing clear explanations, XAI can help prevent mistakes,
support adherence to regulatory standards, and encourage responsible AI use in
healthcare. Additionally, uncertainty quantification [138] could be applied to
generate model predictions along with uncertainty estimation, which is critical for
clinicians to evaluate the reliability of the predictions and identify challenging cases
that need to be examined carefully. Moreover, there is an increasing demand for
advanced computational infrastructures and skilled personnel to manage and
analyse big data in radiation oncology. More efforts should be made to address
these challenges.
Looking ahead, the evolving landscape of big data in radiation oncology is
poised to revolutionize the field with more personalized, efficient, and effective
treatment strategies. The emerging multi-omics data analysis is expected to
provide deeper insights into the complexities of cancer biology and the varying
responses of different cancers to treatments. By analyzing patterns and correlations using big data, researchers could identify new biomarkers and understand the
molecular and genetic underpinnings of individual tumors. This could lead to a
new era of radiation therapy, characterized by highly adaptive and personalized
treatment regimens.
The integration of AI and big data is anticipated to enhance healthcare operational efficiency, facilitate more effective and cost-saving clinical workflows, streamline administrative processes, and optimize resource allocation. Big data
applications also have the potential for remote patient monitoring, ensuring timely
intervention during post-treatment follow-ups. Additionally, big data may empower
patients with easy access to their health data, providing customized visualizations
and enhancing patient engagement in treatment. This, in turn, could potentially
improve patients’ adherence to clinicians’ instructions and ultimately enhance
treatment outcomes.
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Artificial Intelligence in Adaptive Radiation Therapy
5.6 Summary
In this chapter, we reviewed the transformative role of big data in advancing AI in
radiation oncology. Diverse and large-scale datasets—spanning imaging, EMRs,
biological sequences, and multi-omics—fuel increasingly powerful AI models that
drive precision, efficiency, and personalization in cancer care. We outlined a typical
big data lifecycle, emphasizing data aggregation, storage, sharing, visualization, and
knowledge implementation. We then discussed AI techniques for big data processing, integration, and modeling, ranging from classical machine learning and deep
learning to reinforcement learning and emerging foundation models. These
approaches enable the applications in medical image segmentation, treatment
planning, response prediction, quality assurance, and clinical decision support.
Lastly, we identified the challenges including data heterogeneity, limited generalizability of institution-specific models, privacy concerns, and the opacity of AI
models. Solutions such as transfer learning, multi-institutional collaboration,
explainable AI, and uncertainty quantification are essential to ensure safe and
trustworthy clinical integration.
The future of precision radiation oncology lies in analyzing big data using AI
models. By harnessing these advances, the field is poised to deliver data-driven and
personalized radiation therapy that could potentially improve outcomes and
redefine standards of cancer care.
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