Patient selection strategies for deep inspiration breath-hold in left-sided breast cancer: a review of current evidence and future directions

With accumulating evidence confirming that postoperative adjuvant radiotherapy improves survival outcomes in patients with left-sided breast cancer, the long-term cardiotoxicity risks associated with radiotherapy have attracted widespread attention. The deep inspiration breath hold (DIBH) technique increases the distance between the heart and the breast target, effectively reducing radiation dose to the heart and coronary arteries. However, the resource-intensive nature of DIBH and substantial heterogeneity in therapeutic benefits among patients pose a major clinical challenge: how to accurately and efficiently identify patients suitable for DIBH. This narrative review summarizes current evidence regarding DIBH patient selection, and integrates available strategies with clinical workflows to construct a three-tiered decision-making framework encompassing feasibility assessment, indication evaluation, and clinical benefit assessment. Given the existing gaps in high-level prospective clinical evidence, three primary research directions are proposed: (1) conducting large-scale multi-center prospective cohort studies to validate the generalizability and clinical practicality of selection strategies; (2) developing an individualized normal tissue complication probability (NTCP) model integrating multi-dimensional risk factors to guide precise decision-making; and (3) implementing prospective clinical trials of artificial intelligence (AI)-assisted selection platforms to accelerate the clinical translation of intelligent selection tools. This review synthesizes current evidence on DIBH patient selection, constructs a hierarchical decision framework to provide a reference for clinical practice, and delineates directions for future research.