Deep Learning-Based Pelvic Vessel Auto-Segmentation for Standardized Lymph Node Delineation in Prostate Cancer Radiotherapy
In brief
AI mapped pelvic vessels with 0.92 agreement in 20 prostate cancer scans
In an internal test of 20 patients, deep-learning software segmented pelvic vessels on CT with a mean Dice score of 0.92 against physician-drawn contours and a median surface distance of 0.70 mm. The results support testing vessel maps as an anatomic guide for lymph node radiotherapy planning, but the model still needs external validation and evaluation of clinical impact.
- Journal
- Practical radiation oncology (Q1)
- Published
- 29 September 2026
- Study design
- Prospective / inception cohort
- Evidence level
- Level 2, Moderate (CEBM 2b)
- Authors
- Yuan Gao, Daxell Wells, Sambit Bhattacharya, Michael Repka, Liza Lavrova, Brian Anderson, et al.
- PMID
- 42810617
- DOI
- 10.1016/j.prro.2026.09.009
Why clinicians should know about it
- Picked for Anatomy (paper of the day, 1 October 2026): Pelvic vessel auto‑segmentation for lymph node delineation
Abstract
Background Accurate delineation of lymph node clinical target volumes (CTVs) is essential for effective and safe prostate cancer radiotherapy, yet inter-physician variability in pelvic nodal contouring remains high. Because pelvic lymph nodes follow predictable distributions around major pelvic vessels, accurate pelvic vessel segmentation could serve as an anatomic guide to standardize nodal CTV definition. This study aimed to develop an automated pelvic vessel segmentation framework on CT imaging to serve as an anatomical scaffold for lymph node delineation in prostate radiotherapy planning. Methods This single-institution study included non-contrast pelvic CT simulation images from 50 patients with prostate cancer who underwent pelvic nodal irradiation. Major pelvic vessels, including the common, external and internal iliac arteries and veins (including branches of the internal iliac vessels up until they exit the pelvis via the greater sciatic foramen), were manually annotated and checked by expert radiation oncologists and used as reference contours. The dataset was randomly divided into a training cohort (n = 30) and a withheld internal test cohort (n = 20). Automated segmentations were generated using a deep-learning framework trained on physician-annotated CT images. Performance was assessed using the Dice Similarity Coefficient, 95th-percentile Hausdorff Distance and Median Surface Distance, with additional expert review of the automated contours by an experienced radiation oncologist. Results On the withheld internal test cohort, the automated segmentation performance achieved a mean (95% CI) Dice Similarity Coefficient of 0.92 (0.91-0.92), Median Surface Distance of 0.70 mm (0.55-0.86) and 95th-percentile Hausdorff Distance of 9.88 mm (8.43-11.33). Conclusions and Relevance Automated pelvic vessel segmentation on non-contrast CT demonstrates quantitative agreement with physician contours. By providing a patient-specific vascular anatomy reference, this approach may provide a foundation for future anatomy-guided nodal CTV delineation workflows, pending external validation and prospective evaluation of clinical impact.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.