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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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Automated CAD-RADS scoring from multiplanar CCTA images using radiomics-driven machine learning.

Anna Corti1, Francesca Ronchetti2, Francesca Lo Iacono1

  • 1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.

European Journal of Radiology
|July 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel radiomics-based machine learning approach for automating Coronary Artery Disease-Reporting and Data System (CAD-RADS) scoring from CCTA images. The radiomic model offers improved explainability and accuracy in coronary artery stenosis assessment.

Keywords:
AtherosclerosisCoronary artery disease (CAD)PlaqueRadiomicsStenosiscoronary computed tomography angiography (CCTA)

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Area of Science:

  • Medical Imaging Analysis
  • Machine Learning in Radiology
  • Cardiovascular Disease Assessment

Background:

  • Coronary Artery Disease-Reporting and Data System (CAD-RADS) scoring from CCTA is manual, time-consuming, and variable.
  • Deep learning automation exists, but radiomics-based approaches with better interpretability are needed.
  • This study addresses the need for explainable AI in CAD-RADS assessment.

Purpose of the Study:

  • To develop and validate a novel radiomics-based machine learning model for automated CAD-RADS scoring.
  • To compare the performance of radiomic, clinical, and combined models for CAD-RADS classification.
  • To evaluate the utility of radiomics in therapy-oriented classification of coronary artery stenosis.

Main Methods:

  • Retrospective study of 251 patients undergoing CCTA.
  • Automated image segmentation, radiomic feature extraction, and data preprocessing.
  • Development of a cascade pipeline for 6-class CAD-RADS and 4-class therapy-oriented classification using clinical, radiomic, and combined models with 5-fold cross-validation.

Main Results:

  • Radiomic and combined models significantly outperformed the clinical model for CAD-RADS scoring (AUC 0.88 and 0.90 vs. 0.66).
  • Radiomic and combined models showed superior performance in therapy-oriented classification (AUC 0.93 and 0.97 vs. 0.79).
  • The models demonstrated significant improvements in classifying specific stenosis severity levels.

Conclusions:

  • This study presents the first radiomics-based model for CAD-RADS classification.
  • The developed model offers enhanced explainability and accuracy in coronary artery stenosis assessment.
  • Radiomics provides a promising, interpretable AI tool to support radiologists in CCTA analysis.