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Artificial intelligence-assisted longitudinal assessment of coronary artery calcification in the Korean lung cancer screening CT program

  • 0Department of Radiology, Chonnam National University Medical School and Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea.
Clinical Imaging +

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July 23, 2025

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July 23, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Coronary artery calcification (CAC) growth, tracked with AI in lung cancer screening (LCS) patients, predicts adverse cardiovascular events (ACEs). This AI-assisted method aids in risk stratification for cardiovascular prevention.

Area Of Science

  • Cardiology
  • Radiology
  • Artificial Intelligence

Background

  • Coronary artery calcification (CAC) progression is a known cardiovascular risk factor.
  • The clinical significance of CAC growth in individuals undergoing lung cancer screening (LCS) is not well understood.
  • Artificial intelligence (AI) offers potential for automated CAC assessment.

Purpose Of The Study

  • To evaluate the association between CAC growth and adverse cardiovascular events (ACEs) in patients undergoing LCS.
  • To assess the utility of AI-assisted CAC analysis for risk stratification in this population.

Main Methods

  • Retrospective analysis of low-dose chest CT (LDCT) scans from LCS patients with available follow-up scans.
  • AI software used for quantifying CAC severity and tracking annual CAC progression.
  • CAC growth defined as incident CAC or >15% annual progression.
  • Cox regression models used to analyze associations between CAC growth and ACEs, adjusting for covariates.

Main Results

  • 193 male patients with a mean age of 61.6 years were analyzed.
  • Over a 4-year follow-up, 15.5% experienced ACEs.
  • Age, CAC growth (HR 2.40), and moderate-to-severe baseline CAC (HR 2.86) were significantly associated with increased ACEs.

Conclusions

  • AI-assisted tracking of CAC growth using serial LDCT scans holds prognostic value in LCS populations.
  • This approach can potentially guide risk-based cardiovascular follow-up and prevention strategies.
  • AI enhances the clinical utility of routine imaging for cardiovascular risk assessment.

Keywords:

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