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Artificial Intelligence in Adaptive Radiation Therapy
Figure 13.10. Example checklist for AI-based outcome model development.
13.5 Summary
The progress and development in medical imaging, biotechnology, and treatment
modalities is an unstoppable process, introducing new data resources for outcome
modeling and treatment adaptation. Due to the amount and complexity of the
available data, AI technologies are often the only suitable tool for its integration.
However, it leverages multiple challenges, primarily associated with data aggregation/sharing/quality, AI architecture, and AI explainability. Poor or missing AI
explainability can quickly transform any model from a promising technology to an
untrustworthy and unreliable black box. Therefore, all AI-empowered outcome
models must be comprehensively validated and concept-proofed according to
standardized methodologies to become a viable and reliable decision-support tool
in the clinic. As such, it can represent a promising improvement for precision
medicine, where the key to success lies in response-based treatment adaptation and
personalization. For several decades, it has been the clinical team’s role to provide
patient response estimation (often utilizing poor population-based models) and
recommend treatment adjustments. With the expansion of AI technologies, this task
is nowadays transferable to automated systems, which can integrate larger and more
complex patient-specific data. This is where the concept of knowledge-based
13-26
Artificial Intelligence in Adaptive Radiation Therapy
response-adapted radiotherapy reaches its full potential. It integrates comprehensive
information, identifies the most important links to patient-specific treatment
response, and tailors the optimal adjustments to maximize the benefits.
Acknowledgments
This work was supported by National Institute of Health (NIH) grant R01CA233487 and Department of Defense (DoD) Congressional Directed Med Res
Prog (CDMRP) W81XWH-22–1–0277.
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