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

TL;DR

DeepCAC2 introduces a large-scale, automated deep learning framework and dataset for coronary artery calcification assessment from low-dose chest CT scans, facilitating cardiovascular risk prediction and research.

Contribution

This work provides a publicly available dataset, automated CAC segmentation pipeline, and risk scoring tools derived from NLST CT scans, advancing opportunistic screening.

Findings

Processed 127,776 CT scans with automated CAC scoring

Generated standardized calcium scores and risk categories

Released a public dataset and tools for cardiovascular research

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.