{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:27:04Z","timestamp":1783628824268,"version":"3.55.0"},"reference-count":43,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"7","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Glob Oncol"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>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\u2013driven workflows for prostate and cervical cancers, designed to improve efficiency, consistency, and accessibility in low- and middle-income countries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>MATERIALS AND METHODS<\/jats:title>\n                    <jats:p>We developed deep learning\u2013based 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/go-25-00702","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T19:59:11Z","timestamp":1783627151000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence\u2013Powered Radiotherapy for Resource-Limited Settings: Advancing Cervical and Prostate Cancer Treatment Planning With the Radiation Planning 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