ArticleJCO global oncology2024
Artificial Intelligence-Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care.
Article in JCO global oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- Journey through technological advancements in radiation therapy.World journal of radiology · 2026Review
- Review
- Leveraging large language models for heuristic usability assessment of medical software: Insights with the Radiation Planning Assistant.Journal of applied clinical medical physics · 2026Article
- Artificial intelligence-assisted radiotherapy for pelvic and abdominal malignancies: assessing feasibility in the context of Africa-specific risks.Frontiers in oncology · 2026Review
- Impact of health system interventions to improve access to external beam radiotherapy: a scoping review.Frontiers in oncology · 2026Review
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- First Decade of the National Cancer Institute's Affordable Cancer Technologies Program: Accelerating Translational Technology Research and Development for Cancer Globally.JCO global oncology · 2025Review
- Article
- External validation of an algorithm to detect vertebral level mislabeling and autocontouring errors.Physics and imaging in radiation oncology · 2025Article
- Feasibility and impact of knowledge-based automated radiotherapy treatment planning in low- and middle-income countries.Ecancermedicalscience · 2025Article
- Advancing the Collaboration Between Imaging and Radiation Oncology.Seminars in radiation oncology · 2024Review
- Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning.Journal of applied clinical medical physics · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.