Abstract
BACKGROUND: Embolic stroke of undetermined source (ESUS) may be associated with carotid artery plaques with <50% stenosis. Plaque vulnerability is multifactorial, possibly related to intraplaque hemorrhage (IPH), lipid-rich-necrotic-core (LRNC), perivascular adipose tissue (PVAT), and calcification morphology. Machine-learning (ML) approaches in plaque classification are increasingly popular but often limited in clinical interpretability by black-box nature. We apply an explainable ML approach, using noncalcified plaque components and calcification features with SHapley Additive exPlanations (SHAP) framework to classify calcified carotid plaques as culprit/non-culprit. METHODS: In this retrospective cross-sectional study, patients with unilateral anterior circulation ESUS who underwent neck CT angiography and had calcific carotid plaque were analyzed. Calcification-level features were derived from manual segmentations. Plaque-level features were assessed by a neuroradiologist blinded to stroke-side and by semi-automated software. Calcifications/plaques were classified as culprit if ipsilateral to stroke-side. Eight baseline ML models were compared. Three CatBoost models were trained: Plaque-level, Calcification-level, and Combined. SHAP was incorporated to explain model decisions. RESULTS: 70 patients yielded 116 calcific carotid plaques (60 ipsilateral to stroke; 270 calcifications (146 ipsilateral)). 17 plaque-level and 15 calcification-level features were extracted. Baseline CatBoost model outperformed other models. Combined model achieved test AUC 0.77 (95% CI: 0.59-0.92), accuracy 0.82 (95% CI: 0.71 - 0.91), mean cross-validation AUC 0.78. Plaque-level and calcification-level models performed lower (AUC 0.41 95% CI: 0.15-0.68, 0.60 95% CI 0.44-0.76). Combined model utilized five features: plaque thickness, IPH/LRNC volume ratio, PVAT volume, calcification minimum density, and total calcification volume over mean density ratio. Plaque thickness was most important feature based on SHAP values, with potential threshold at >2.6 mm. CONCLUSIONS: ML model trained with noncalcified plaque and calcification features can classify culprit calcific carotid plaque with greater accuracy than models trained using only plaque-level or calcification-level features. Model using clinically interpretable features with SHAP framework provides explanations for its decisions and allows identification of potential thresholds for high-risk features.