# Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography

arXiv: https://arxiv.org/abs/2008.06997 · HTML: https://mlsanity.com/paper/2008.06997/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [Cardiovascular Disease Detection from Multi-View Chest X-rays with BI-Mamba](https://mlsanity.com/paper/2405.18533/) (2024)
- [Human Body Digital Twin: A Master Plan](https://mlsanity.com/paper/2307.09225/) (2023)
- [A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study](https://mlsanity.com/paper/2303.10789/) (2023)
- [Xray2Xray: World Model from Chest X-rays with Volumetric Context](https://mlsanity.com/paper/2506.19055/) (2025)
- [Specialty-Oriented Generalist Medical AI for Chest CT Screening](https://mlsanity.com/paper/2304.02649/) (2023)
- [Cardiac-CLIP: A Vision-Language Foundation Model for 3D Cardiac CT Images](https://mlsanity.com/paper/2507.22024/) (2025)
- [Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from Low-Dose Computed Tomography](https://mlsanity.com/paper/2511.06625/) (2025)
- [Foundation Models for Medical Imaging: Status, Challenges, and Directions](https://mlsanity.com/paper/2602.15913/) (2026)

## Abstract (arXiv)

Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model, trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieved an area under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identified patients with high CVD mortality risks (AUC of 0.768). We validated our model against ECG-gated cardiac CT based markers, including coronary artery calcification (CAC) score, CAD-RADS score, and MESA 10-year risk score from an independent dataset of 335 subjects. Our work shows that, in high-risk patients, deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.