ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2021
Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
64 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- Potential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Study.JCO global oncology · 2024Trial
- Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.Radiology. Artificial intelligence · 2024Trial
- Clinical edge-case stress testing of two Asia-developed AI auto-contouring systems using prostate radiotherapy planning CT images.Radiological physics and technology · 2026Article
- Quantifying manual editing time in autocontouring via audit log analysis.Physics and imaging in radiation oncology · 2026Article
- Artificial intelligence (AI)-based multi-organ contour quality assurance with uncertainty estimation for online adaptive radiotherapy (oART).Machine Learning. Health · 2026Article
- Review
- Evaluation of a quasi-automatic treatment planning workflow for head-and-neck cancer in radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Prospective blinded clinical evaluation of a fully automated workflow for prostate radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Comparison between artificial intelligence-based and manual organ delineations in pretreatment computed tomography scans of prostate cancer patients: a visual grading study.Radiation protection dosimetry · 2026Article
- PURE-MRI: An international study assessing physician accuracy in delineating the prostate and urethra on prostate MRI.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- Leveraging large language models for heuristic usability assessment of medical software: Insights with the Radiation Planning Assistant.Journal of applied clinical medical physics · 2026Article
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
- A combined automatic organ at risk contouring and synthetic Computed Tomography solution for pelvic Magnetic Resonance-only radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Real-world performance evaluation of commercial autocontouring software for head and neck cancer radiotherapy.The British journal of radiology · 2025Article
- Article
- A comprehensive multifaceted technical evaluation framework for implementation of auto-segmentation models in radiotherapy.Communications medicine · 2025Article
- Risk analysis of the Unity 1.5T MR-Linac adapt-to-shape workflow.Journal of applied clinical medical physics · 2025Article
- Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures.Physics and imaging in radiation oncology · 2025Article
- Auto-Segmentation and Auto-Planning in Automated Radiotherapy for Prostate Cancer.Bioengineering (Basel, Switzerland) · 2025Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
background and purposeArtificial intelligence advances have stimulated a new generation of autosegmentation, however clinical evaluations of these algorithms are lacking. This study assesses the clinical utility of deep learning-based autosegmentation for MR-based prostate radiotherapy planning. MATERIALS AND
methodsData was collected prospectively for patients undergoing prostate-only radiation at our institution from June to December 2019. Geometric indices (volumetric Dice-Sørensen Coefficient, VDSC; surface Dice-Sørensen Coefficient, SDSC; added path length, APL) compared automated to final contours. Physicians reported contouring time and rated autocontours on 3-point protocol deviation scales. Descriptive statistics and univariable analyses evaluated relationships between the aforementioned metrics.
resultsAmong 173 patients, 85% received SBRT. The CTV was available for 167 (97%) with median VDSC, SDSC, and APL for CTV (prostate and SV) 0.89 (IQR 0.83-0.95), 0.91 (IQR 0.75-0.96), and 1801 mm (IQR 1140-2703), respectively. Physicians completed surveys for 43/55 patients (RR 78%). 33% of autocontours (14/43) required major "clinically significant" edits. Physicians spent a median of 28 min contouring (IQR 20-30), representing a 12-minute (30%) time savings compared to historic controls (median 40, IQR 25-68, n = 21, p < 0.01). Geometric indices correlated weakly with contouring time, and had no relationship with quality scores.
conclusionDeep learning-based autosegmentation was implemented successfully and improved efficiency. Major "clinically significant" edits are uncommon and do not correlate with geometric indices. APL was supported as a clinically meaningful quantitative metric. Efforts are needed to educate and generate consensus among physicians, and develop mechanisms to flag cases for quality assurance.
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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.