A Radiomics-Based Machine Learning Approach for Coronary Stenosis Assessment from Coronary Computed Tomography Angiography

Coronary Artery Disease (CAD) is a leading cause of mortality worldwide. This study aims to improve CAD diagnosis and facilitate tailored treatment strategies by implementing a radiomics-based machine learning algorithm for the automated evaluation of stenosis from coronary computed tomography angiography (CCTA). The study population included 220 patients undergoing CCTA for suspected CAD. Multiplanar Reconstruction images at 45° of rotation were obtained for each of the three primary coronary arteries, resulting in a total of 343 coronary artery segments. Each patient was assigned to: non-obstructive (0% stenosis), sub-obstructive (stenosis<50%) or obstructive (stenosis>50%) class. After radiomic features extraction and selection, a gradient boosting model was trained using a cascade approach, which subdivided the CAD-RADS scoring task into two easier sub-tasks: (A) no CAD vs. CAD and (B) nonobstructive vs. obstructive CAD. The dataset was divided into training and test sets (80-20%) and 5-fold cross-validation was applied to the training set to determine optimal hyperparameters. A balanced accuracy of 80% and AUC-ROC of 84% were achieved on the test set. These preliminary results are promising for automatic stenosis assessment, potentially useful in optimizing CAD management.