S1557
Physics - Autosegmentation
ESTRO 2026
Results: Stage 1: Median vDSC 0.78 (IQR: 0.68-0.88). Sensitivity per voxel was 0.77 but notably lower per node (0.66), reflecting the model’s propensity to overlook smaller positive nodes. A higher precision on a voxel basis (0.86 vs 0.76) highlights the model’s ability to delineate the boundaries of detected nodes well but a susceptibility to segment additional nodes. Stage 2: Inclusion of node-negative patients did not significantly change test vDSC or sensitivity or precision per node vs Stage 1 (paired Wilcoxon p>0.05).Stage 3: Post-processing increased per-node precision from median 0.83 to 1.0 (W=0.0, p<0.001), whilst sensitivity remained unchanged (median 1.0; W=3.0, p=1.0) reflecting that post-processing mostly removed small false positives.
world Head and Neck Cancer Dataset for Research,” Clin Oncol, vol. 47, p. 103935, Nov. 20254X. Chen et al., “Deep learning–based automatic segmentation of cardiac substructures for lung cancers,” Radiotherapy
and Oncology, vol. 191, p. 110061, Feb. 2024 Keywords: Artificial Intelligence, Bias, multi- disciplinary
Digital Poster Highlight 1454
Domain-specific fine-tuning of a 3D foundation model for automated head and neck organ at risk segmentation Yuqing Xia 1 , Samer Jabor 2 , Arkajyoti Roy 3 , Neil Kirby 1 , Nikos Papanikolaou 1 1 Radiation Oncology, University of Texas Health Science Center at San Antonio, San Antonio, USA. 2 Computer Science, St Mary's University, San Antonio, USA. 3 Operations & Analytics, University of Texas at San Antonio, San Antonio, USA Purpose/Objective: Accurate segmentation of organs at risk (OARs) is essential for high-precision head-and-neck (H&N) radiotherapy planning. Conventional convolutional neural network (CNN) architectures, such as nnU-Net, have demonstrated strong performance but require large, homogeneous datasets and substantial training time. Foundation models like the Segment Anything Model (SAM) provide flexible, prompt-based segmentation but have limited ability to capture the complex three-dimensional spatial context characteristic of medical imaging. This study fine-tunes a 3D SAM-Med model (SAM-FT) for automated segmentation of H&N OARs and targets through domain-specific adaptation, interactive prompting, and composite loss optimization. The performance of SAM-FT was compared with SAM-Med3D and nnU-Net in terms of segmentation accuracy, computational efficiency, and clinical applicability for radiotherapy contouring. Material/Methods: A total of 211 anonymized H&N CT datasets containing 20 delineated OARs from our cancer center were preprocessed and standardized. Eighty percent of the data were used for training and twenty percent for testing. SAM-FT was optimized using variable-length interactive prompting (1–20 simulated clicks) and a composite Dice–IoU–Cross-Entropy loss to enhance spatial and boundary accuracy. nnU-Net was trained using its automated configuration pipeline. Model performance was assessed through five-fold cross- validation using Dice similarity coefficient, Intersection-over-Union (IoU), precision and recall.
Qualitative scoring (n=30) for Stage 3 showed median Likert=3 (mean>3 for both clinicians). In cases without false positives/negatives, median=4 (range 3-5). vDSC showed weak correlation with Likert (ρ=0.19, p=0.32), while SDC correlated significantly (ρ=0.51, p=0.004), aligning better with clinician judgement. Conclusion: This model demonstrates strong performance on larger nodes and substantial precision gains after simple post-processing. Leveraging a federated data lake with rich demographic and clinical metadata enables future examination of bias and supports equitable model deployment. Multidisciplinary buy-in from radiologists and oncologists will be key to setting acceptance thresholds and defining use as a timesaving, editable baseline with acceptable clinical risk1. References: 1Royal College of Radiologists, “Autocontouring in Radiotherapy: Guidance for Clinicians,” 2024. Accessed: Apr. 25, 2025. https://www.rcr.ac.uk/media/rqjlnlny/rcr-auto- contouring-in-radiotherapy-2024.pdf2V. Butterworth et al., “Data-centric artificial intelligence and cancer research: construction of a real-world head and neck treatment data repository,” ESMO Real World Data and Digital Oncology, vol. 9, p. 100162, Sep. 20253T. Young et al., “RT-HaND_C: A Multi-Source, Validated Real-
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