A Cascaded Deep Learning-Based Cardiac Substructures Segmentation Frame and on Non-Gated Non-Enhanced Planning CT Scans in Breast Cancer Patients

Abstract

To develop a deep learning-based segmentation frame for cardiac substructures especially coronary arteries (CAs) on non-gated non-enhanced planning computed tomography (CT) scans in breast cancer (BC) patients. Non-gated non-enhanced CT scans of 39 BC patients receiving adjuvant radiotherapy (RT) were collected. Cardiac substructures were manually labelled, including four chambers, left main (LM), left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA). The training, validation, and test sample is 28, 7 and 4, respectively. A cascaded network, using nnUNet as the backbone, is proposed to use chambers as prior information to constrain the segmentation of CAs. The mean Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95) and average symmetrical surface distance (ASSD) were used as geometric metrics. Dosimetric parameters of cardiac substructures was calculated based on the segmentation frame and manually labeled contouring, respectively. The data of cardiac examination including ultrasonography, electrocardiogram before and during the follow-up after RT were retrospectively collected. The cardiac event was any symptomatic heart disease or new-onset abnormality in the cardiac examination after RT. The mean DSC of heart, atriums and ventricles of the proposed frame was 0.93, 0.90, and 0.93, respectively. As shown in Table 1, compared with direct segmentation (as baseline), the proposed frame had a better performance in terms of HD95, ASSD, and the mean dose (Dmean) absolute error for all CAs. Compared to the dosimetric parameters of the heart collected based on the manual labelled contours, the relative errors of D5, D95, and V15Gy for LAD was 4.3±7.8%, 11.7±5.9%, and 14.6±13.0% collected based on the direct segmentation contours and 2.4±4.4%, 3.9±3.1%, 8.5±6.9% collected based on the auto-segmented contours, respectively. Multivariate analysis showed that increased V15Gy of LAD was an independent cardiac toxicity risk factor ([HR] = 1.07, 95% CI 1-1.15, p = 0.0387). We developed a cascaded network for cardiac substructures segmentation with dosimetric validation on non-enhanced CT scans in breast cancer radiotherapy. This is the first attempt to use chambers as prior information for CAs' segmentation and had a superior stable performance. Accurate segmentation will help radiation oncologists to better evaluate DVHs based on substructures and thus to estimate cardiovascular risk. An optimized cardiac substructure-based dosimetric constrain may be proposed accordingly.