AI-Powered Radiotherapy for Resource-Limited Settings: Advancing Cervical and Prostate Cancer Treatment Planning with the Radiation Planning Assistant (RPA)

Abstract

1. ABSTRACT Purpose Radiotherapy treatment planning is a resource-intensive process characterized by multiple manual steps and clinical hand-offs that contribute to treatment delays and inter-observer variability. The Radiation Planning Assistant (RPA) is a web-based platform designed to deliver automated contouring and planning approaches tailored to low-resource settings. This work expands the RPA to develop and clinically validate end-to-end, AI-driven workflows for prostate and cervical cancers, designed to improve efficiency, consistency, and accessibility in low- and middle-income countries (LMICs). Methods We developed deep learning-based auto-contouring models using nnU-Net and integrated them with knowledge-based planning (KBP) models trained on curated datasets from over 1,000 prostate and 110 cervical cancer treatment plans. For prostate cancer, models were developed to accommodate prostate directed, prostate bed, and nodal treatment scenarios. Cervical cancer planning followed EMBRACE II guidelines and included pelvic and para-aortic nodal volumes. These tools were integrated into the RPA. Clinical acceptability of the auto-contours and plans was assessed retrospectively by radiation oncologists using a five-point Likert scale. Results In total, 50 test patients (40 prostate, 10 cervical) were evaluated. For prostate cancer, 70% of target auto-contours and 73% of treatment plans were clinically acceptable without edits; for cervical cancer, these rates were 80% and 80%, respectively. For prostate cancer planning, 77% of target and 98% of organ-at-risk structures met all per-protocol compliance criteria. For cervical cancer planning, all EMBRACE II protocol hard constraint criteria were met. Bowel and vaginal contours demonstrated lower performance, but these did not compromise plan quality. Conclusion We present validated, end-to-end radiotherapy planning workflows for prostate and cervical cancers that leverage the RPA’s infrastructure to streamline treatment planning in a globally accessible platform and demonstrate high clinical acceptability. By reducing reliance on specialist input, this work addresses key barriers to equitable radiotherapy access in resource-limited settings and responds to global calls from the IAEA and WHO to expand radiotherapy capacity. Funding National Institute of Health, National Science Foundation, Rising Tide Foundation, University of Texas MD Anderson Cancer Center

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