ArticleJournal of applied clinical medical physics2025
Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- Evaluation of the image quality index with MRI motion artifacts on tumor segmentation using deep learning.Radiological physics and technology · 2026Article
- Real-world clinical impact of implementing and updating a deep learning-based automatic contouring system in rectal cancer radiotherapy.Journal of applied clinical medical physics · 2026Article
- A systematic review of artificial intelligence in radiotherapy associated cardiovascular toxicity.Cardio-oncology (London, England) · 2026Review
- Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol.Systematic reviews · 2026Article
- Feasibility of automated AI-based contouring and stable radiomic feature assessment by HyperSight-CBCT Imaging for adaptive high-precision radiotherapy of prostate cancer.Scientific reports · 2026Article
- Automated deep learning segmentation and planning for left-sided breast radiotherapy with minimised adaptations based on dose, TCP and NTCP criteria.Physics and imaging in radiation oncology · 2026Article
- Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.BMJ oncology · 2026Review
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
- Clinical validation of AI-assisted contouring in prostate radiation therapy treatment planning: Highlighting automation bias and the need for standardized quality assurance.Journal of applied clinical medical physics · 2026Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Statistical process control for performance monitoring and continuous quality assurance of deep learning segmentations in radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Article
- A fully automated machine-learning-based workflow for radiation treatment planning in prostate cancer.Clinical and translational radiation oncology · 2025Article
- Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.Journal of applied clinical medical physics · 2025Article
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Authors and funding
9 authors.
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Abstract
purposeThis study aimed to investigate the dosimetric impact of deep-learning-based auto-contouring for clinical target volume (CTV) and organs at risk (OARs) delineation in prostate cancer radiotherapy planning. Additionally, we compared the geometric accuracy of auto-contouring system to the variability observed between human experts.
methodsWe evaluated 28 planning CT volumes, each with three contour sets: reference original contours (OC), auto-segmented contours (AC), and expert-defined manual contours (EC). We generated 3D-CRT and intensity-modulated radiation therapy (IMRT) plans for each contour set and compared their dosimetric characteristics using dose-volume histograms (DVHs), homogeneity index (HI), conformity index (CI), and gamma pass rate (3%/3 mm).
resultsThe geometric differences between automated contours and both their original manual reference contours and a second set of manually generated contours are smaller than the differences between two manually contoured sets for bladder, right femoral head (RFH), and left femoral head (LFH) structures. Furthermore, dose distribution accuracy using planning target volumes (PTVs) derived from automatically contoured CTVs and auto-contoured OARs demonstrated consistency with plans based on reference contours across all evaluated cases for both 3D-CRT and IMRT plans. For example, in IMRT plans, the average D
conclusionOur automated contouring system can reduce inter-expert variability and achieve dosimetric accuracy comparable to gold standard reference contours, highlighting its potential for streamlining clinical workflows. The quantitative analysis revealed no consistent trend of increasing or decreasing PTVs derived from automatically contoured CTVs and OAR doses due to automated contours, indicating minimal impact on treatment outcomes. These findings support the clinical feasibility of utilizing our deep-learning-based auto-contouring model for prostate cancer radiotherapy planning.
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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.