Development and internal evaluation of an interpretable machine learning model based on clinical and radiomics features to differentiate lower extremity arterial embolism from atherothrombosis.
Authors
Affiliations (8)
Affiliations (8)
- Department of Interventional Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
- The First Hospital of Liangshan, Xichang, China.
- The Fourth People's Hospital of Sichuan Province, West China Chunxi Hospital of Sichuan University, Chengdu, China.
- Department of General Surgery (Vascular Surgery), The Affiliated Hospital of Southwest Medical University, Luzhou, China.
- Laboratory of Nucleic Acids in Medicine for National High-Level Talent, Nucleic Acid Medicine of Luzhou Key Laboratory, Southwest Medical University, Luzhou, China.
- Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, Collaborative Innovation Center for Prevention and Treatment of Cardiovascular Disease of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China.
- Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China.
Abstract
Accurate differentiation between lower extremity arterial embolism (AE) and atherothrombosis is essential. This study aimed to develop and validate an interpretable machine learning (ML) model combining clinical and radiomics features for preoperative non-invasive diagnosis. This retrospective study enrolled patients with lower extremity AE or atherothrombosis admitted to the Department of Vascular Surgery at our institution between January 2018 and January 2025. Clinical data were collected through the electronic medical record system, and radiomics features were extracted from lower extremity computed tomography angiography (CTA) images. Based on these features, three ML models were constructed, comprising a clinical model, a radiomics model, and a combined clinical-radiomics model. The diagnostic performance was evaluated using the area under the curve (AUC), sensitivity, specificity, F1-score, and accuracy. Furthermore, clinical utility was validated using calibration curves and decision curve analysis, while model interpretation was conducted via the SHAP method. Among the three predictive models, the fusion model demonstrated the optimal differential diagnostic performance. In the testing set, this model yielded an AUC of 0.821 (95% CI 0.718-0.924), with sensitivity, specificity, F1-score, and accuracy of 0.750, 0.714, 0.761, and 0.734, respectively; the positive likelihood ratio (LR+) and negative likelihood ratio (LR-) were 2.62 and 0.35. In the training set, its AUC reached 0.979 (95% CI 0.959-0.999), with sensitivity, specificity, F1-score, and accuracy of 0.940, 0.939, 0.946, and 0.940, respectively; the LR+ and LR- were 15.41 and 0.06. The clinical-radiomics fusion model demonstrates potential as a non-invasive auxiliary tool to assist in differentiating lower extremity atherothrombosis from AE in a single-center setting, warranting prospective multicenter validation.