# Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics

arXiv: https://arxiv.org/abs/1912.06075 · HTML: https://mlsanity.com/paper/1912.06075/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries](https://mlsanity.com/paper/2107.03035/) (2021)
- [CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling](https://mlsanity.com/paper/2202.03671/) (2022)
- [Cardiac-CLIP: A Vision-Language Foundation Model for 3D Cardiac CT Images](https://mlsanity.com/paper/2507.22024/) (2025)
- [Improving segmentation of calcified and non-calcified plaques on CCTA-CPR scans via masking of the artery wall](https://mlsanity.com/paper/2201.10424/) (2022)
- [O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis](https://mlsanity.com/paper/2303.06358/) (2023)
- [CORA: Generalizable coronary artery disease assessment and risk stratification from coronary CT angiography using pathology-centric representation learning](https://mlsanity.com/paper/2603.24847/) (2026)
- [Clinical Risk-Aware Multi-Level Grading for Coronary Artery Stenosis through Curved Feature Reconstruction](https://mlsanity.com/paper/2606.30082/) (2026)

## Abstract (arXiv)

Assessing coronary artery plaque segments in coronary CT angiography scans is an important task to improve patient management and clinical outcomes, as it can help to decide whether invasive investigation and treatment are necessary. In this work, we present three machine learning approaches capable of performing this task. The first approach is based on radiomics, where a plaque segmentation is used to calculate various shape-, intensity- and texture-based features under different image transformations. A second approach is based on deep learning and relies on centerline extraction as sole prerequisite. In the third approach, we fuse the deep learning approach with radiomic features. On our data the methods reached similar scores as simulated fractional flow reserve (FFR) measurements, which - in contrast to our methods - requires an exact segmentation of the whole coronary tree and often time-consuming manual interaction. In literature, the performance of simulated FFR reaches an AUC between 0.79-0.93 predicting an abnormal invasive FFR that demands revascularization. The radiomics approach achieves an AUC of 0.86, the deep learning approach 0.84 and the combined method 0.88 for predicting the revascularization decision directly. While all three proposed methods can be determined within seconds, the FFR simulation typically takes several minutes. Provided representative training data in sufficient quantities, we believe that the presented methods can be used to create systems for fully automatic non-invasive risk assessment for a variety of adverse cardiac events.