Automatic segmentation of cardiac structures can change the way we evaluate dose limits for radiotherapy in the left breast

Purpose: Radiotherapy is a crucial part of breast cancer treatment. Precision in dose assessment is essential to minimize side effects. Traditionally, anatomical structures are delineated manually, a time-consuming process subject to variability. automatic segmentation, including methods based on multiple atlases and deep learning, offers a promising alternative. For the radiotherapy treatment of the left breast, the RTOG 1005 protocol highlights the importance of cardiac delineation and the need to minimize cardiac exposure to radiation. Our study aims to evaluate dose distribution in auto-segmented substructures and establish models to correlate them with dose in the cardiac area.

Methods and materials: Anatomical structures were auto-segmented using TotalSegmentator and Limbus AI. The relationship between the volume of the cardiac area and of organs at risk was assessed using log-linear regressions.

Results: The mean dose distribution was considerable for LAD (left anterior descending coronary artery), heart, and left ventricle. The volumetric distribution of organs at risk is evaluated for specific RTOG 1005 isodoses. We highlight the greater variability in the absolute volumetric evaluation. Log-linear regression models are presented to estimate dose constraint parameters. We highlight a greater number of highly correlated comparisons for absolute dose-volume assessment.

Conclusions: Dose-volume assessment protocols in patients with left breast cancer often neglect cardiac substructures. However, automatic tools can overcome these technical difficulties. In this study, we correlated the dose in the cardiac area with the doses in specific substructures and suggested limits for planning evaluation. Our data also indicates that statistical models could be applied in the assessment of those substructures where an automatic segmentation tool is not available. Our data also shows a benefit in reporting absolute dose-volume thresholds for future cause-effect assessments.