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Patient-level CAD-RADS scoring from coronary radiomic features.

Anna Corti1, Francesca Lo Iacono2, Francesca Ronchetti3

  • 1Department of Electronics, Information and Bioengineering, Politecnico Di Milano, Via Ponzio 34/5, 20133, Milan, Italy. anna.corti@polimi.it.

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Summary

This study introduces novel radiomic feature summarization strategies to generate a single Coronary Artery Disease-Reporting and Data System (CAD-RADS) score from coronary CT angiography. Majority voting methods significantly improved CAD-RADS scoring accuracy, advancing automated analysis.

Keywords:
Atherosclerotic plaqueCAD-RADSCoronary artery disease (CAD)Coronary computed tomography angiography (CCTA)Machine learningRadiomicsStenosis

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Area of Science:

  • Cardiovascular Imaging
  • Radiomics
  • Machine Learning

Background:

  • Synthesizing patient-wise Coronary Artery Disease-Reporting and Data System (CAD-RADS) scores from coronary radiomic data is challenging.
  • Coronary computed tomography angiography (CCTA) generates extensive multiplanar reconstruction (MPR) images crucial for cardiac assessment.

Purpose of the Study:

  • To develop and evaluate four distinct strategies for summarizing coronary radiomic features to derive a single patient-level CAD-RADS score.
  • To compare the performance of statistical-based versus majority voting approaches for automated CAD-RADS classification.

Main Methods:

  • Extracted radiomic features from 2779 MPR images of 238 patients undergoing CCTA.
  • Developed a cascade pipeline with gradient boosting classifiers for CAD-RADS scoring.
  • Implemented two statistical (average, min, max, std dev) and two majority voting (MV_P, MV_C) methods for patient-level scoring.
  • Utilized an 80%-20% training/test split with five-fold cross-validation.

Main Results:

  • Majority voting approaches outperformed statistical-based methods in CAD-RADS scoring.
  • The MV_P approach achieved high AUCs across various CAD-RADS categories (e.g., 0.94 for CAD-RADS 0, 0.97 for CAD-RADS 2).
  • The MV_C approach also demonstrated strong performance, particularly for higher CAD-RADS grades (e.g., 0.96 for CAD-RADS 3, 0.98 for CAD-RADS 4).

Conclusions:

  • Majority voting strategies, particularly MV_P, offer a robust and reproducible method for automated patient-wise CAD-RADS scoring.
  • This work represents a significant step towards reliable coronary radiomics tools for clinical application.
  • The proposed methods enhance the potential for automated CAD-RADS assessment in clinical practice.