Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care

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...

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Main Authors: Laurence E. Court, Ajay Aggarwal, Anuja Jhingran, Komeela Naidoo, Tucker Netherton, Adenike Olanrewaju, Christine Peterson, Jeannette Parkes, Hannah Simonds, Christoph Trauernicht, Lifei Zhang, Beth M. Beadle
Format: Article
Language:English
Published: American Society of Clinical Oncology 2024-11-01
Series:JCO Global Oncology
Online Access:https://ascopubs.org/doi/10.1200/GO.23.00376
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author Laurence E. Court
Ajay Aggarwal
Anuja Jhingran
Komeela Naidoo
Tucker Netherton
Adenike Olanrewaju
Christine Peterson
Jeannette Parkes
Hannah Simonds
Christoph Trauernicht
Lifei Zhang
Beth M. Beadle
author_facet Laurence E. Court
Ajay Aggarwal
Anuja Jhingran
Komeela Naidoo
Tucker Netherton
Adenike Olanrewaju
Christine Peterson
Jeannette Parkes
Hannah Simonds
Christoph Trauernicht
Lifei Zhang
Beth M. Beadle
author_sort Laurence E. Court
collection DOAJ
description 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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spelling doaj-art-e3e146a544804eda818ef0bcc66dd0712025-08-20T01:57:35ZengAmerican Society of Clinical OncologyJCO Global Oncology2687-89412024-11-011010.1200/GO.23.00376Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer CareLaurence E. Court0Ajay Aggarwal1Anuja Jhingran2Komeela Naidoo3Tucker Netherton4Adenike Olanrewaju5Christine Peterson6Jeannette Parkes7Hannah Simonds8Christoph Trauernicht9Lifei Zhang10Beth M. Beadle11University of Texas MD Anderson Cancer Center, Houston, TXGuy's and St Thomas Hospitals, London, United KingdomUniversity of Texas MD Anderson Cancer Center, Houston, TXStellenbosch University, Stellenbosch, South AfricaUniversity of Texas MD Anderson Cancer Center, Houston, TXUniversity of Texas MD Anderson Cancer Center, Houston, TXUniversity of Texas MD Anderson Cancer Center, Houston, TXUniversity of Cape Town, Cape Town, South AfricaStellenbosch University, Stellenbosch, South AfricaStellenbosch University, Stellenbosch, South AfricaUniversity of Texas MD Anderson Cancer Center, Houston, TXStanford University, Stanford, CAPURPOSEIncreased 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.https://ascopubs.org/doi/10.1200/GO.23.00376
spellingShingle Laurence E. Court
Ajay Aggarwal
Anuja Jhingran
Komeela Naidoo
Tucker Netherton
Adenike Olanrewaju
Christine Peterson
Jeannette Parkes
Hannah Simonds
Christoph Trauernicht
Lifei Zhang
Beth M. Beadle
Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care
JCO Global Oncology
title Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care
title_full Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care
title_fullStr Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care
title_full_unstemmed Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care
title_short Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care
title_sort artificial intelligence based radiotherapy contouring and planning to improve global access to cancer care
url https://ascopubs.org/doi/10.1200/GO.23.00376
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