Machine Learning Assisted Decision Support Platform for Radiation Treatment Assessment

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The delivery of radiation for the treatment of cancer is a complicated process that requires both clinical and technical expertise. Although radiation treatments are safe and effective, there is a potential for errors in how treatments are designed and delivered that could lead to adverse patient outcomes. The current quality assurance process in radiation oncology requires substantial resources that leverages the collective experience of the radiation medicine team to ensure quality and safely, while minimizing the likelihood of errors. My lab has previously developed a novel proof-of-concept machine learning (ML) framework for automating the treatment quality review process that codifies the shared knowledge of the expert radiation medicine team to prioritize complex cases based on imaging, anatomical, technical, and dosimetric features. ML provides the ability to augment human tasks as well as integrate clinical decision making into the clinical process which forms the basis of the proposed research program. The ability to use ML to predict the quality of radiation treatments and understand radiation treatments which have potentially anomalous features can be used to 1) expedite the peer review process, 2) ensure higher quality treatments by flagging radiation treatments that are more likely to require attention or be erroneous, and 3) inform clinical management for complex processes that require real-time clinical decisions. Objectives: The overarching hypothesis of the proposed research program is that ML-assisted insights of treatment quality will be essential components of the radiation oncology process to ensure patients receive high quality and timely radiation treatments. The proposed research program will develop a quality radiation oncology platform built on novel ML methods with the following objectives: i) Build and deploy a dedicated ML-based quality platform to prioritize radiation treatments for review by the expert radiation medicine team based on treatment complexity and highlight treatments with potential errors requiring attention prior to treatment approvals. ii) Develop a real-time quality platform for adaptive radiation treatments that incorporates time-series imaging data and anatomical information over the course of treatment to ensure updates to treatments are appropriate based on the patients' changing anatomy. Outcomes and Significance: The novel technical developments from the proposed research program include: i) generating new outlier detection models with human understandable output to enable ML-assisted decision support for treatment review in peer review rounds and incorporating time-series data to provide real-time decision support for adaptive radiation treatments. The proposed research has direct applicability and significance for improving the workflow of quality processes in radiation oncology and enabling improved cancer care in addition to enabling patients more timely access to complex treatments.