ArticleRadiation oncology (London, England)2021
The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer.
Article in Radiation oncology (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed, 45 citations in OpenAlex.
- 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
- Clinical edge-case stress testing of two Asia-developed AI auto-contouring systems using prostate radiotherapy planning CT images.Radiological physics and technology · 2026Article
- AI-Based Dose Compliance of Secondary Organs at Risk in Head and Neck Cancer Radiotherapy.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluation of a quasi-automatic treatment planning workflow for head-and-neck cancer in radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol.Systematic reviews · 2026Article
- Clinically validated deep learning auto-contouring for glioblastoma volumetric modulated arc therapy planning: a dosimetric equivalence study.Radiation oncology journal · 2026Article
- 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
- Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment.Frontiers in oncology · 2026Review
- Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy.Journal of biomedical physics & engineering · 2025Article
- Geometric, dosimetric and psychometric evaluation of three commercial AI software solutions for OAR auto-segmentation in head and neck radiotherapy.Scientific reports · 2025Article
- Time efficiency, geometric accuracy, and clinical impact of AI-assisted contouring of organs at risk in head and neck cancer radiotherapy.Acta oncologica (Stockholm, Sweden) · 2025Article
- Deep learning-based delineation of whole-body organs at risk empowering adaptive radiotherapy.BMC medical informatics and decision making · 2025Article
- Domain-Adaptive and Per-Fraction Guided Deep Learning Framework for Magnetic Resonance Imaging-Based Segmentation of Organs at Risk in Gynecologic Cancers.Advances in radiation oncology · 2025Article
- [A multi-scale supervision and residual feedback optimization algorithm for improving optic chiasm and optic nerve segmentation accuracy in nasopharyngeal carcinoma CT images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 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
- Automated daily dose accumulation workflow for treatment quality assurance during online adaptive radiotherapy with a 0.35T MR-linac.Journal of applied clinical medical physics · 2025Article
- Interobserver variability in organ delineation on radiotherapy treatment planning for nasopharyngeal carcinoma: A dosimetric and prognostic analysis.Frontiers in oncology · 2025Article
- Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures.Journal of applied clinical medical physics · 2024Article
- Clinical evaluation of a deep learning CBCT auto-segmentation software for prostate adaptive radiation therapy.Clinical and translational radiation oncology · 2024Article
- A deep learning-based 3D Prompt-nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma.Journal of applied clinical medical physics · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer. METHODS AND MATERIALS: Twenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy in our department were enrolled in this study. Two deep learning-based auto-segmentation systems, including an in-house developed system (FD) and a commercial product (UIH), were used to generate two auto-segmented OARs sets (OAR_FD and OAR_UIH). Treatment plans based on auto-segmented OARs and following our clinical requirements were generated for each patient on each OARs sets (Plan_FD and Plan_UIH). Geometric metrics (Hausdorff distance (HD), mean distance to agreement (MDA), the Dice similarity coefficient (DICE) and the Jaccard index) were calculated for geometric evaluation. The dosimetric impact was evaluated by comparing Plan_FD and Plan_UIH to original clinically approved plans (Plan_Manual) with dose-volume metrics and 3D gamma analysis. Spearman's correlation analysis was performed to investigate the correlation between dosimetric difference and geometric metrics.
resultsFD and UIH could provide similar geometric performance in parotids, temporal lobes, lens, and eyes (DICE, p > 0.05). OAR_FD had better geometric performance in the optic nerves, oral cavity, larynx, and femoral heads (DICE, p < 0.05). OAR_UIH had better geometric performance in the bladder (DICE, p < 0.05). In dosimetric analysis, both Plan_FD and Plan_UIH had nonsignificant dosimetric differences compared to Plan_Manual for most PTV and OARs dose-volume metrics. The only significant dosimetric difference was the max dose of the left temporal lobe for Plan_FD vs. Plan_Manual (p = 0.05). Only one significant correlation was found between the mean dose of the femoral head and its HD index (R = 0.4, p = 0.01), there is no OARs showed strong correlation between its dosimetric difference and all of four geometric metrics.
conclusionsDeep learning-based OARs auto-segmentation for NPC and rectal cancer has a nonsignificant impact on most PTV and OARs dose-volume metrics. Correlations between the auto-segmentation geometric metric and dosimetric difference were not observed for most OARs.
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