S1554
Physics - Autosegmentation
ESTRO 2026
Thomas’ NHS Foundation Trust, London, United Kingdom. 4 AI & Data Analytics, FH Technikum Wien, Vienna, Austria Purpose/Objective: In radiotherapy (RT), accurate segmentation of organs at risk on planning CT scans is essential for optimal treatment planning. Deep-learning (DL) pelvic auto- segmentation models developed at Guy’s and St. Thomas’ NHS Foundation Trust [1][2] have reduced manual segmentation workload [2], with an additional aim to improve consistency. Prior to clinical implementation, qualitative scoring and timing studies were carried out as part of validation of clinical utility [2]. This study utilises efficient scripting to extract quantitative metrics for segmentation accuracy and investigates whether they offer an objective alternative to resource-intensive traditional clinical utility measures. Material/Methods: DL models auto-segmented bladder, bowel, rectum, sigmoid, femoral heads and penile bulb. Clinicians assigned quality scores (QS) using the MD Anderson Likert scale (1 to 5) [3] and recorded adjustment times (AT) for contour corrections [2]. Quantitative evaluation included volumetric Dice Similarity Coefficient (vDSC), the surface DSC (sDSC), Hausdorff distance (HD), and Added Path Length (APL). Pearson correlation coefficients (PCC) assessed the relationship between quantitative metrics, QS, and AT. Correlation strength was categorised as insignificant (absolute PCC (PCCabs) < 0.20), weak (0.20 ≤ PCCabs < 0.40), moderate (0.40 ≤ PCCabs < 0.75) or strong (PCCabs ≥ 0.75). Results: Quantitative metrics showed moderate to strong correlations with clinician-assigned QS for all structures except the bowel, and with adjustment times for all structures except the bowel and rectum. vDSC and sDSC metrics demonstrated stronger correlations across all structures compared to HD and APL, with mean PCC values of 0.70±0.30 (vDSC-QS), 0.71±0.25 (sDSC-QS), -0.55 ± 0.28 (vDSC-AT) and -0.61 ± 0.24 (sDSC-AT). Notably, sigmoid segmentation exhibited high variability in vDSC (range: 0.44-1.00) and sDSC (range: 0.47-1.00), yet maintained strong correlations with QS (PCC = 0.81, 0.82 respectively).
Conclusion: Automated MSP detection enables objective quantification of tumor extension across the midline and demonstrates a correlation between extension depth and contralateral lymphatic involvement. Continuous quantification of distance to MSP may allow for more accurate prediction of contralateral involvement patterns compared to binary distinction between tumors crossing the MSP or not. References: Ludwig, R., Pérez Haas, Y., Benavente, S., Balermpas, P., & Unkelbach, J. (2025). A probabilistic model of bilateral lymphatic spread in head and neck cancer. Scientific Reports,15(1). https://doi.org/10.1038/s41598-025-99978-7 Keywords: mid-sagittal plane, oropharynx-cancer, lymph nodes Evaluating the Clinical Utility of AI-Generated Auto-Segmentation: Correlation Between Quantitative Metrics and Clinician Assessment in Radiotherapy Evi Markou 1 , Victoria Butterworth 1 , Maram Alqarni 2 , Luis Ribeiro 3 , Ajay Aggarwal 3 , Gurdip Azad 3 , Victoria Harris 3 , Simon Hughes 3 , Stephen Morris 3 , Kirsty Morrison 3 , Vinod Mullassery 3 , Lydia Pascal 3 , Priyankah Patel 3 , Benjamin Taylor 3 , Sindu Vivekanandan 3 , Ingrid White 3 , Anna Winship 3 , Andrew King 2 , Isabel Dregely 4 , Sally Barrington 2 , Teresa GuerreroUrbano 3 , Christopher Thomas 1 1 Medical Physics & Clinical engineering, Guy’s & St. Thomas’ NHS Foundation Trust, London, United Kingdom. 2 School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom. 3 Clinical Oncology, Guy’s & St. Digital Poster 1143
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