ArticlePhysics and imaging in radiation oncology2023
Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy.
Article in Physics and imaging in radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 37 citations in OpenAlex.
- AI-Assisted Brain Tumor MRI Reporting and Treatment-Planning Segmentation: A Retrospective Paired Workflow Evaluation.Biomedicines · 2026Article
- Development of an MR-only radiotherapy treatment planning workflow using a commercial synthetic CT generator for brain and head & neck tumor patients.Zeitschrift fur medizinische Physik · 2026Article
- Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.BMJ oncology · 2026Review
- Maintaining quality of scientific peer-review and publishing inPhysics and imaging in radiation oncology · 2026Article
- Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy.Journal of biomedical physics & engineering · 2025Article
- Evaluation of CNN-based deep learning models for auto-contouring in glioblastoma radiotherapy: a review.Radiation oncology (London, England) · 2025Review
- The impact of neck tilt on the accuracy of deep learning generated contours for CT images of the head and neck.Journal of applied clinical medical physics · 2025Article
- Enhancing clinical safety in radiation oncology: A data-driven approach to risk management.Zeitschrift fur medizinische Physik · 2025Article
- Establishing prospective performance monitoring for real-world implementation of deep learning-based auto-segmentation in prostate cancer radiotherapy.Physics and imaging in radiation oncology · 2025Article
- A dual-layer quality assurance approach leveraging dose prediction for efficient review of automated contours of organs at risk in the brain in radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Geometric, dosimetric and psychometric evaluation of three commercial AI software solutions for OAR auto-segmentation in head and neck radiotherapy.Scientific reports · 2025Article
- Can Advances in Artificial Intelligence Strengthen the Role of Intraoperative Radiotherapy in the Treatment of Cancer?Cancers · 2025Review
- A comprehensive multifaceted technical evaluation framework for implementation of auto-segmentation models in radiotherapy.Communications medicine · 2025Article
- Geometric and dosimetric evaluation of a commercial AI auto-contouring tool on multiple anatomical sites in CT scans.Journal of applied clinical medical physics · 2025Article
- Trade-off of different deep learning-based auto-segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI.Medical physics · 2025Article
- Evaluation and failure analysis of four commercial deep learning-based autosegmentation software for abdominal organs at risk.Journal of applied clinical medical physics · 2025Article
- Artificial intelligence for treatment delivery: image-guided radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- MR-linac: role of artificial intelligence and automation.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Clinical commissioning and introduction of an in-house artificial intelligence (AI) platform for automated head and neck intensity modulated radiation therapy (IMRT) treatment planning.Journal of applied clinical medical physics · 2025Article
- Current status and upcoming developments for online adaptive proton therapy enabling a closed feedback loop for near real-time adaptation.Frontiers in oncology · 2025Review
Corrections and comments
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Authors and funding
16 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: Tools for auto-segmentation in radiotherapy are widely available, but guidelines for clinical implementation are missing. The goal was to develop a workflow for performance evaluation of three commercial auto-segmentation tools to select one candidate for clinical implementation. Materials and Methods: One hundred patients with six treatment sites (brain, head-and-neck, thorax, abdomen, and pelvis) were included. Three sets of AI-based contours for organs-at-risk (OAR) generated by three software tools and manually drawn expert contours were blindly rated for contouring accuracy. The dice similarity coefficient (DSC), the Hausdorff distance, and a dose/volume evaluation based on the recalculation of the original treatment plan were assessed. Statistically significant differences were tested using the Kruskal-Wallis test and the post-hoc Dunn Test with Bonferroni correction. Results: The mean DSC scores compared to expert contours for all OARs combined were 0.80 ± 0.10, 0.75 ± 0.10, and 0.74 ± 0.11 for the three software tools. Physicians' rating identified equivalent or superior performance of some AI-based contours in head (eye, lens, optic nerve, brain, chiasm), thorax (e.g., heart and lungs), and pelvis and abdomen (e.g., kidney, femoral head) compared to manual contours. For some OARs, the AI models provided results requiring only minor corrections. Bowel-bag and stomach were not fit for direct use. During the interdisciplinary discussion, the physicians' rating was considered the most relevant. Conclusion: A comprehensive method for evaluation and clinical implementation of commercially available auto-segmentation software was developed. The in-depth analysis yielded clear instructions for clinical use within the radiotherapy department.
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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.