20.500.12556/RUL-178671 Dosimetric assessment of deep learning based organ-at-risk segmentation insights from the HaN-Seg challenge Background and purpose To extend the previously reported geometric analysis of HaN-Seg: The Head and Neck Organ-at-Risk CT and MR Segmentation Challenge by integrating a dosimetric evaluation, thereby offering a comprehensive assessment of challenge results with practical insights into their clinical applicability. Materials and methods Participating teams of the HaN-Seg challenge were tasked to auto-segment 30 organs-at-risk (OARs) in the head and neck region using paired contrast-enhanced computed tomography and T1-weighted magnetic resonance images. The teams were ranked according to their geometric performance, measured by the Dice similarity coefficient (DSC) and 95th-percentile Hausdorff distance (HD95). Here, we extend this evaluation with a forward dosimetric analysis, also known as dosimetric impact approximation, including the verification of OAR dosimetric restriction compliance, assessment of OAR priority ratings, evaluation of segmentation performance relative to tumor proximity, and correlation analysis between geometric and dosimetric metrics. Results All six teams from the previous geometric analysis were assessed for dosimetric performance on the original 14 test cases. Dosimetric analysis revealed minor performance differences among teams, with the best- and worst-performing teams achieving dosimetric compliance in 70.7% and 67.7% of OAR auto-segmentations, respectively. Most teams successfully met priority 1 dosimetric restrictions including the spinal cord, brainstem, optic chiasm, and optic nerves in 11 out of 14 test cases. The lowest compliance rates were observed for the oral cavity and submandibular glands. Correlation analysis revealed no clear relationship between geometric and dosimetric metrics. Conclusion The high dosimetric compliance highlights the practical utility of deep learning OAR auto-segmentation methods. Lower compliance for the oral cavity and submandibular glands most probably stems from their proximity to tumors and the corresponding steep dose gradients, where certain dosimetric constraints are inherently challenging to meet in clinical practice, or from the limitations of the forward dosimetric analysis. These findings underpin the critical need for both geometric and dosimetric evaluations of OAR auto-segmentation tools to ensure robust validation. Such a comprehensive assessment will be essential as commercial deep learning tools become increasingly integrated into the radiotherapy planning workflow. computational challenge segmentation deep learning organs-at-risk computed tomography magnetic resonance radiotherapy head and neck cancer dosimetric evaluation dosimetric restrictions računski izziv segmentacija globoko učenje kritični organi računalniška tomografija magnetna resonanca radioterapija rak glave in vratu dozimetrično vrednotenje dozimetrične omejitve true false true Angleški jezik Slovenski jezik Članek v reviji 2026-01-29 10:20:44 2026-01-29 10:20:50 2026-01-30 04:50:01 0000-00-00 00:00:00 2026 0 0 8 str. art. 111387 Vol. 271 Apr. 2026 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004.93 1879-0887 10.1016/j.radonc.2026.111387 266164995 1-s2.0-S0167814026000253-main.pdf 1-s2.0-S0167814026000253-main.pdf 1 F79C4DFE50ED1CC12AE9BD8824FBD60D a8e19d1bb7601f739d7a15043ff7cb7e08a5342113744bc9f25155f94d786040 fa154f2f-fcf3-11f0-a1ba-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=226585 https://www.sciencedirect.com/science/article/pii/S0167814026000253 1 c67c8f4b-fcf3-11f0-a1ba-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=226584 Fakulteta za elektrotehniko 0 0 0