Artificial Intelligence-Powered Radiotherapy for Resource-Limited Settings: Advancing Cervical and Prostate Cancer Treatment Planning With the Radiation Planning Assistant.

Authors

Tucker J Netherton, Ajay Aggarwal, Qusai Alakayleh, Beth M Beadle, Chloe Brooks, Henriette Burger, Carlos E Cardenas, Adrian Celaya, Sarah Chacko, Christine V Chung, Raphael Douglas, Daniel El Basha, Steven Frank, David Fuentes, Comron Hassanzadeh, Jonathan Helbrow, Peter Hoskin, Meena S Khan, Mariana Kroiss, Alexandra Leone, Lilie L Lin, Raymond P Mumme, Elizabeth Miles, Callistus Nguyen, Quyen Nguyen, Adenike Olanrewaju, Jaganathan Parameshwaran, Julianne Pollard-Larkin, Falk Poenisch, Shalin Shah, Alan Jerel Sosa, Chad Tang, Zhiqian Henry Yu, Lifei Zhang, Laurence E Court

Published in

JCO global oncology. Volume 12. Issue 7. Pages e2500702. Epub Jul 09, 2026.

Abstract

Radiotherapy treatment planning is a resource-intensive process characterized by multiple manual steps that can contribute to treatment delays and interobserver 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, artificial intelligence-driven workflows for prostate and cervical cancers, designed to improve efficiency, consistency, and accessibility in low- and middle-income countries.
We developed deep learning-based auto-contouring models using nnU-Net and integrated them with knowledge-based planning models trained on curated data sets 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.
In all, 50 test patients (40 prostate, 10 cervical) were evaluated end-to-end. 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.
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.

PMID:
42424567
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.

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