S1549
Physics - Autosegmentation
ESTRO 2026
Villalonga Mur 1 , Magdalena Lafuente Alconchel 1 , Moisés Mira Flores 1 1 Radiation Oncology, H.U Arnau de Vilanova, Lleida, Spain. 2 GREISI, IRB, Lleida, Spain. 3 Radiation Physics Department, H.U Arnau de Vilanova, Lleida, Spain Purpose/Objective: Accurate delineation of cardiac substructures is crucial in breast cancer radiotherapy to minimize radiation exposure and late cardiac toxicity. The use of artificial intelligence (AI) in contouring has the potential to improve consistency and efficiency, but its accuracy in complex and variable structures, such as the heart chambers and coronary arteries, requires validation. This study aimed to evaluate the concordance between AI-based contouring using the MVision® Contour+ software and manual delineations performed by physicians with different levels of experience. Material/Methods: A total of 125 patients with breast cancer undergoing radiotherapy were included. Four radiation oncologists (two consultants and two residents) contoured the main cardiac substructures—left and right atria, left and right ventricles, and coronary arteries—on planning CT images. The Dice Similarity Coefficient (DSC) was used to quantify spatial overlap between contours, and interobserver variability (Δmean) was also calculated. Statistical comparison between AI and physicians was performed using paired tests, with p < 0.05 considered significant. Results: The mean DSC values (±SD) for MVision® vs. physicians were:Left atrium: 0.827 ± 0.004 vs. 0.813 ± 0.005 (p = 0.19)Right atrium: 0.778 ± 0.006 vs. 0.777 ± 0.006 (p = 0.93)Right ventricle: 0.797 ± 0.004 vs. 0.793 ± 0.005 (p = 0.72)Left ventricle: 0.870 ± 0.004 vs. 0.885 ± 0.004 (p = 0.82)Coronary arteries (A_LAD): 0.366 ± 0.010 vs. 0.378 ± 0.013 (p = 0.91)No statistically significant differences were found in any structure. Interobserver Δmean ranged from 0.003–0.008 for cardiac chambers, indicating high reproducibility, and 0.009–0.014 for coronary arteries, reflecting greater variability in smaller and less-defined structures.
contour, suggesting that simple thresholds for esophageal dimensions could be utilized to flag possible failures up-front.
Conclusion: While the CE-scan-based b-AI performed well for esophageal contouring in CE scans, performance in non-CE scans was worse, and less predictable, demonstrating clinical importance of data bias in application of AI-tools. Anatomical failure characteristics suggested that cases at high risk of failure could be identified up-front and that development of quantitative thresholds for flagging failures before undertaking corrections may be possible. Keywords: failure modes, monitoring
Digital Poster 732
Artificial Intelligence vs. Physicians in Cardiac Contouring for Breast Cancer Radiotherapy: A Comparative Analysis of 125 Patients Virginia García Reglero 1,2 , Sara Vázquez González 1 , Luis Ramos García 3 , Oscar Ripol Valentin 3 , Lucía Tueros Farfan 1 , José David González Gómez 1 , Priscila Bernard Contreras 1 , Manuela Bermúdez Zubiría 1 , Alejandro Rodríguez Gutierrez 1 , Elena García Alonso 1 , Amaya Gracia Sanjuan 1 , Daniel José Lueza Gistau 1 , Mireia
Made with FlippingBook - Share PDF online