Machine Learning Multiclass Models Based on Cardiac Segmentation Quantitative Parameters from Non-Contrast and Contrast-Enhanced Chest CT for the Differential Diagnosis of Acute Chest Pain
Year: 2026
Type: article
Abstract: Objectives: Acute coronary syndrome (ACS), pulmonary embolism (PE), and aortic dissection (AD) present with overlapping symptoms but require markedly different treatments. We investigated whether cardiac quantitative parameters obtained by automated segmentation of non-contrast and contrast-enhanced chest CT can support machine learning-based multiclass differentiation of these conditions. Materials and Methods: This single-center retrospective study included 628 patients who underwent triple rule-out CT angiography (147 negative controls, 193 ACS, 147 PE, and 141 AD). Twenty-six features were extracted separately from automated cardiac segmentation of non-contrast and contrast-enhanced images. Five classifiers—multivariable logistic regression, random forest, XGBoost, LightGBM, and support vector machine—were developed with a 7:3 stratified train–test split and five-fold cross-validation. Performance was evaluated by accuracy, macro-averaged F1 score, and macro-averaged area under the receiver operating characteristic curve (AUC), and feature importance was assessed with SHapley Additive exPlanations (SHAP). Results: Multivariable logistic regression showed the most balanced performance for both modalities. The non-contrast model achieved an accuracy of 0.635, a macro-averaged F1 score of 0.637, and a macro-averaged AUC of 0.840, with class-specific AUCs of 0.814 for ACS, 0.878 for PE, and 0.887 for AD. The contrast-enhanced model achieved 0.683, 0.673, and 0.861, with AUCs of 0.881, 0.876, and 0.919. Recall for ACS increased from 58.6% to 70.7% and for AD from 74.4% to 83.7%, while AD cases misclassified as negative decreased from 11.6% to 2.3%. Left ventricular mass index and right-heart parameters ranked among the most contributory features in both models. Conclusion: Machine learning models built on automatically segmented cardiac parameters showed moderate discriminatory potential for differentiating ACS, PE, and AD. The contrast-enhanced model outperformed the non-contrast model, whereas non-contrast CT still carried complementary structural information that may support risk stratification before contrast administration.
Source: Medicina
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,654
OpenAlex ID: W7225799611