A Deep Learning Radiomics Model Based on Superb Microvascular Imaging for Non-Invasive Prediction of the Degree of Arteriolosclerosis in Patients With Chronic Kidney Disease.
Authors
Affiliations (3)
Affiliations (3)
- Department of Ultrasound, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, China.
- Department of Ultrasound, The First Afffliated Hospital of Anhui Medical University, Hefei, Anhui, China.
- Department of Ultrasound, Chengdu Second People's Hospital, Chengdu, Sichuan, China.
Abstract
This study aimed to develop and validate a deep learning radiomics (DLR) model based on superb microvascular imaging (SMI) for the noninvasive assessment of the severity of arteriolosclerosis in patients with chronic kidney disease (CKD). From June 2022 to December 2024, we prospectively recruited 326 CKD patients who underwent kidney biopsy across two medical centers. The enrolled patients were randomly allocated to the training or testing set in a 7:3 ratio. Deep learning (DL) features and radiomics features from SMI images were extracted, and after dimensionality reduction, they were used to establish deep learning radiomics (DLR) models. The performance of the proposed models was assessed through receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Among the 326 CKD patients, 165 were positive for arteriolosclerosis and 161 were negative. In the training group, the area under the curve (AUC) values for the CDUS model,clinical model, radiomics model, DL model, and DLR model were 0.621 (0.547-0.695), 0.68 (0.611-0.749), 0.763 (0.703-0.823), 0.820 (0.767-0.874), and 0.840 (0.790-0.890), respectively. In the testing group, the AUCs were 0.677 (0.571-0.783), 0.776 (0.684-0.869), 0.727 (0.626-0.829), 0.779 (0.687-0.872), and 0.819 (0.735-0.903), respectively. The DLR model outperformed standalone radiomics, DL models, and the CDUS-based clinical model. The DCA validated the clinical utility of the DLR model. The DLR model utilizing SMI imaging can precisely and non-invasively assess the severity of arteriolosclerosis in CKD patients, which can assist physicians in formulating more favorable treatment plans for patients.