Coronary artery calcification assessment in National Lung Screening Trial CT images (DeepCAC2)

Abstract

Coronary artery calcification (CAC) is a strong predictor of cardiovascular risk but remains underutilized in clinical routine thoracic imaging due to the need for dedicated imaging protocols and manual annotation. We present DeepCAC2, a publicly available dataset containing automated CAC segmentations, coronary artery calcium scores, and derived risk categories generated from low-dose chest CT scans of the National Lung Screening Trial (NLST). Using a fully automated deep learning pipeline trained on expert-annotated cardiac CT data, we processed 127,776 CT scans from 26,228 individuals and generated standardized CAC segmentations and risk estimates for each acquisition. We already provide a public dashboard as a simple tool to visually inspect a random subset of 200 NLST patients of the dataset. The dataset will be released with DICOM-compatible segmentation objects and structured metadata to support reproducible downstream analysis. The deep learning pipeline will be made publicly available as a DICOM-compatible MHub.ai container. DeepCAC2 provides a transparent, large-scale, public, fully reproducible resource for research in cardiovascular risk assessment, opportunistic screening, and imaging biomarker development.