Clinical Feasibility of Artificial Intelligence-Based Autosegmentation of the Left Anterior Descending Artery in Radiotherapy for Breast Cancer - Mohan, Aditi; Talapatra, Kaustav; Sankhe, Manoj; Kiron, G.; Maniar, Priyal; Kolekar, Akshat; Kushe, Saakshi; Kharote, Prashant; Singh, Anuradha; Patel, Keval; Gupta, Aayush; Patel, Devarsh; Bardeskar, Nikhil; Patkar, Deepak (Indian Journal of Medical and Paediatric Oncology, 2024)
Introduction Breast cancer is a prevalent global disease, and radiotherapy plays a crucial role in its treatment. However, radiotherapy may lead to cardiac complications, particularly in patients receiving left-sided radiotherapy who may experience increased risks due to toxicity in the left anterior descending (LAD) artery. The manual contouring of the LAD artery is time-consuming and subject to variability. This study aimed to provide an overview of artificial intelligence (AI) based LAD artery contouring, assess its feasibility, and identify its limitations.Objectives The primary objectives were to evaluate the feasibility of AI-based LAD artery contouring, compare different approaches, and quantify properties impacting accuracy. The secondary objective was to recommend algorithms with greater accuracy.Materials and Methods A (noncontrast) computed tomography dataset of nine patients with breast cancer was used to analyze the features and behavior of the LAD artery. The functioning of different AI models used for autosegmentation was studied, and the LAD artery imaging features were identified and quantified using the widely used AI models. Additionally, an algorithm to reliably compute interpatient variability in the LAD artery contours was proposed.Results A lack of distinctive features, diminutive contour size (∼5 pixels on average), and inconsistent position of the LAD artery were observed. The interpatient variability in the LAD artery contours was five to seven times the average size of the contours. The dataset also had a high standard deviation of 28.9 and skewed data distribution.Conclusions The results indicated that the variable path of the LAD artery and high interpatient variability were the primary reasons for the inability of AI algorithms to have a concordance. Further, the small contour size amplified model inaccuracy. For higher autosegmentation accuracy, an anatomical landmark–based approach is necessary to capture surrounding structures that affect the path of the LAD artery.
Keywords: breast cancer radiotherapy; autosegmentation; left anterior descending artery contouring; left anterior descending artery sparing; clinical feasibility; artificial intelligence–based contouring