Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care
In: JCO Global Oncology, Jg. 10 (2024-04-01), Heft 10
Online
academicJournal
Zugriff:
PURPOSEIncreased automation has been identified as one approach to improving global cancer care. The Radiation Planning Assistant (RPA) is a web-based tool offering automated radiotherapy (RT) contouring and planning to low-resource clinics. In this study, the RPA workflow and clinical acceptability were assessed by physicians around the world.METHODSThe RPA output for 75 cases was reviewed by at least three physicians; 31 radiation oncologists at 16 institutions in six countries on five continents reviewed RPA contours and plans for clinical acceptability using a 5-point Likert scale.RESULTSFor cervical cancer, RPA plans using bony landmarks were scored as usable as-is in 81% (with minor edits 93%); using soft tissue contours, plans were scored as usable as-is in 79% (with minor edits 96%). For postmastectomy breast cancer, RPA plans were scored as usable as-is in 44% (with minor edits 91%). For whole-brain treatment, RPA plans were scored as usable as-is in 67% (with minor edits 99%). For head/neck cancer, the normal tissue autocontours were acceptable as-is in 89% (with minor edits 97%). The clinical target volumes (CTVs) were acceptable as-is in 40% (with minor edits 93%). The volumetric-modulated arc therapy (VMAT) plans were acceptable as-is in 87% (with minor edits 96%). For cervical cancer, the normal tissue autocontours were acceptable as-is in 92% (with minor edits 99%). The CTVs for cervical cancer were scored as acceptable as-is in 83% (with minor edits 92%). The VMAT plans for cervical cancer were acceptable as-is in 99% (with minor edits 100%).CONCLUSIONThe RPA, a web-based tool designed to improve access to high-quality RT in low-resource settings, has high rates of clinical acceptability by practicing clinicians around the world. It has significant potential for successful implementation in low-resource clinics.
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Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care
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Autor/in / Beteiligte Person: | Court, Laurence E. ; Aggarwal, Ajay ; Jhingran, Anuja ; Naidoo, Komeela ; Netherton, Tucker ; Olanrewaju, Adenike ; Peterson, Christine ; Parkes, Jeannette ; Simonds, Hannah ; Trauernicht, Christoph ; Zhang, Lifei ; Beadle, Beth M. |
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Zeitschrift: | JCO Global Oncology, Jg. 10 (2024-04-01), Heft 10 |
Veröffentlichung: | American Society of Clinical Oncology, 2024 |
Medientyp: | academicJournal |
ISSN: | 2687-8941 (print) |
DOI: | 10.1200/GO.23.00376 |
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