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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...

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Updated: Oct 5, 2025

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

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Published on: August 28, 2018

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning

Katelyn M Atkins^1,2, Jakob Weiss^2,3,4, Roman Zeleznik^2,3

¹Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA.

JCO Clinical Cancer Informatics

|January 27, 2022

Summary

Coronary artery calcium (CAC) can be measured from lung cancer CT scans using deep learning. Elevated CAC predicts higher mortality risk, suggesting potential for cardiac screening before cancer treatment.

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Area of Science:

Cardiology
Radiology
Oncology

Background:

Coronary artery calcium (CAC) on CT scans predicts atherosclerotic coronary disease.
The utility of CAC quantification from lung cancer radiotherapy planning CT scans is not established.

Purpose of the Study:

To assess the feasibility and relevance of quantifying CAC from lung cancer radiotherapy planning CT scans.
To determine if deep learning (DL)-quantified CAC predicts all-cause mortality and major adverse cardiac events (MACEs).

Main Methods:

Retrospective analysis of 428 lung cancer radiotherapy planning CT scans.
Utilized a validated deep learning (DL) model to quantify CAC (DL-CAC score).
Assessed associations between DL-CAC score and mortality/MACEs using Cox and Fine and Gray regressions.

Main Results:

A majority of patients (61.4%) had an elevated DL-CAC score (≥ 1).
Elevated DL-CAC (≥ 1) was associated with increased all-cause mortality (aHR, 1.51; P = .04).
A trend towards increased MACE risk was observed with elevated DL-CAC (HR, 1.80; P = .11).

Conclusions:

Deep learning effectively quantifies CAC from radiotherapy planning CT scans.
Elevated CAC, identified via DL, is linked to increased mortality risk in lung cancer patients.
Automated CAC screening from radiotherapy planning CT scans may benefit pre-cancer therapy risk stratification.