Clinical-radiological machine learning model for non-invasive diagnosis and stratification of peripheral artery disease: a multicenter study.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, Hubei Province, China. Electronic address: [email protected].
- Department of MR Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. Electronic address: [email protected].
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, Hubei Province, China. Electronic address: [email protected].
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, Hubei Province, China. Electronic address: [email protected].
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, Hubei Province, China. Electronic address: [email protected].
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, Hubei Province, China. Electronic address: [email protected].
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, Hubei Province, China. Electronic address: [email protected].
Abstract
Peripheral artery disease (PAD) is an atherosclerotic disorder prevalent in the elderly that leads to peripheral function decline and body composition changes. Current diagnostic approaches lack sensitivity for early PAD detection and staging. This study aimed to develop and validate machine learning (ML) models of clinical and CT-based radiological features to improve PAD diagnosis and severity stratification. A retrospective multicenter study was conducted using data from two institutions. Clinical and radiological features (including volumetric body composition and muscle texture parameters extracted from calf and thigh segments) were analyzed. Participants were randomly divided into training (70%) and test (30%) sets, stratified by PAD status. Models with different ML algorithms were developed and compared. Model interpretability was assessed with Shapley additive explanation (SHAP) analysis, and performance was evaluated through receiver operating characteristic analysis, Hosmer-Lemeshow testing, Brier score and calibration curves. This study comprised 342 participants, divided into training (n = 176), test set (n = 76) from Institute 1, external validation (n = 90) from Institute 2. Three models were developed: clinical model (CM), radiological model (RM), and combined clinical-radiological model (CRM). The calf-based CRM using random forest algorithm achieved area under the curves of 0.871 (training), 0.870 (test), and 0.828 (validation), demonstrating good calibration (p ≥ 0.05) and the low Brier score. SHAP analysis visually interpreted feature contributions toward PAD diagnosis and staging. The CRM model effectively integrated calf-derived radiological and clinical features into a noninvasive, interpretable tool for PAD diagnosis and severity stratification, demonstrating strong clinical applicability.