Multicenter Evaluation of a Deep Learning Model for Automatic Contouring of Cardiac Substructures in Adults.

Purpose

Efficient delineation of cardiac substructures is essential for assessing radiation-induced cardiac toxicity in thoracic radiotherapy. We clinically evaluated a deep learning model designed for automated segmentation of these structures.

Methods

AND MATERIALS The model built in collaboration with XXX (XXX) generated 21 cardiac substructures (whole heart, chambers, coronary arteries, valves, and great vessels). Five radiation oncologists rated raw contours on thirty contrast-enhanced CTs from two institutions using a 4-point Likert scale (3 = no correction, 2 = minor/moderate correction, 1 = major correction, 0 = not performed) and applied corrections when needed. Dice Similarity Coefficients (DSC) and 95th-percentile Hausdorff distances (HD95) were computed to compare model versus reference contours, clinician-corrected versus reference contours, and model versus STAPLE contours.

Results

Of 630 structures, 93.8% were scored either 3 or ≥2, and 6.2% received 1 by at least one clinician. For the coronary arteries (210 structures), 88.5% were scored either 3 or ≥ 2. Contour generation took approximately 1 min, with a median review and correction time of 7.0 min per CT (IQR, 5.8-9.8). The median DSC between the model and reference contours was 1.0 across all structures, while it ranged from 0.92 to 1.0 for clinician-corrected versus reference contours. Overall, HD95 was below 5 mm, for both clinicians versus reference and model versus STAPLE.

Conclusions

The deep learning model provided satisfactory segmentation of cardiac substructures on contrast-enhanced CT, including coronary arteries. Its integration into a widely adopted treatment planning system lays the foundation for broad clinical implementation.