Abstract P1-10-12: AI-based cardiac sub-structures segmentation for safer radiotherapy planning

Abstract

Abstract Purpose: Whilst radiotherapy increases cure rates in breast cancer, lung cancer, among others, it may also involve some cardiac exposure, which in turn may increase the risk of different heart diseases. The heart is a complex anatomical organ that involves many different structures making it difficult to contour cardiac sub-structures reproducibly. Contouring, especially for these cases, suffers from inter- and intra-expert variability while being time consuming. Cardiac atlases have been developed to aid in the delineation of cardiac substructures. However, these methods have many shortcomings, including the inability to overcome variations in patient anatomy. In this study, a deep learning based commercial solution for automatic OAR delineation was trained following international guidelines for heart substructures delineation and tested on an unseen cohort of lung and breast patients to evaluate its clinical acceptability. Methods: ART-Net, a CE-marked, FDA-cleared anatomically preserving deep-learning ensemble architecture for automatic annotation of OAR was evaluated using data of 20 breast/lung patients from 2 centers. Automatic annotation of 27 different structures (Ventricles (left and right), atria (left and right), left ventricle (anterior, apical, inferior, lateral, septal), LAD coronary (mid, proximal, distal, total), circumflex coronary (distal, proximal, total), RCA (distal, mid, proximal, total), coronary sinus, left main coronary artery, ascending aorta, pulmonary arteries, vena cava inferior, vena cava superior and the heart) was performed and submitted to 2 experts across 2 centers for qualitative evaluation. Contours were scored as A/acceptable, B/acceptable after minor corrections, and C/not acceptable for clinical use. To avoid any bias, experts were blind to whether the contour were manually, or AI delineated. The DSC between automatic and manual (ground truth) contours of the heart sub-structures were evaluated and compared with interobserver variability from the literature [1,2] using average and min DSC scores. Results: Automatic contours were generated in a mean time of 0.5s per scan slice. Out of the 27 structures, 20 were considered clinically acceptable in the qualitative study. In the inter-expert variability study, 12 structures passed the test successfully using initial acceptance criterion over an acceptable sample size and 9 other structures demonstrated performances above the minimal threshold of inter-expert variability, sometimes on smaller datasets due to lack of manual data. Overall, 16 structures were included in the final model. 13 structures were considered clinically acceptable in 100% of the cases with AI contours rated at the same level as manual contours. For the other 3 structures (coronary sinus, left main coronary artery and vena cava inferior), the performance of the AI contours was slightly below that of the manual contours (within 3.4% difference), with the least performing structure being the coronary sinus (84% for AI vs 87% manual). Conclusion: We show first results for the evaluation of AI-based auto-contouring tool for annotation of the substructures of the heart. The results show very good clinical acceptance, highlighting the high usability of the commercial tool for cardiac cases and its clinical implementation feasibility. The use of this AI tool can facilitate and accelerate future research studies investigating relationships between substructure doses and cardiac outcomes. Future work will include improvement of the sub-structures (mid, proximal, distal) and a retrospective meta-analysis to assess heart sub-structures degree of importance in terms of toxicity.