Multi-modality ultrasound model for renal fibrosis assessment in chronic kidney disease : integrating grayscale and color doppler ultrasound radiomics with shear wave elastography

Abstract

Objective: Accurate assessment of renal fibrosis is critical for managing chronic kidney disease (CKD). This study aimed to develop a multi-modality ultrasound-based model that integrates radiomics signatures derived from grayscale ultrasound and color Doppler ultrasound, along with shear wave elastography (SWE) measurements, to assess the severity of renal fibrosis in CKD patients. Methods: A total of 125 CKD patients were enrolled and classified into mild and moderate-to-severe fibrosis groups based on renal biopsy. Radiomics features were extracted from grayscale and color Doppler ultrasound images, and key features were identified using machine learning algorithms to construct radiomics signatures for each modality. SWE measurements were used to assess renal stiffness. A multi-modality ultrasound model was constructed using logistic regression, combining the dual-modality radiomics signatures with SWE data. Model performance was evaluated using receiver operating characteristic (ROC) curves, five-fold cross-validation, calibration, and decision curve analysis (DCA). Results: Individual modalities for SWE, grayscale ultrasound radiomics, and color Doppler ultrasound radiomics showed area under the ROC curves (AUCs) of 0.74 (95% CI: 0.65– 0.82), 0.79 (95% CI: 0.71– 0.87), and 0.70 (95% CI: 0.60– 0.79), respectively. The multi-modality model, integrating all three modalities, achieved an AUC of 0.88 (95% CI: 0.82– 0.94), sensitivity of 0.82 (95% CI: 0.70– 0.91), specificity of 0.84 (95% CI: 0.73– 0.92), and accuracy of 0.83 (95% CI: 0.75– 0.89). In cross-validation, the model showed robust generalizability (AUC: 0.89, 95% CI: 0.74– 1.00). The calibration curve showed excellent agreement between predicted and observed outcomes, and the DCA curve confirmed the clinical utility of the model. A nomogram based on the multi-modality model was developed for individualized risk assessment of moderate-to-severe renal fibrosis. Conclusion: The multi-modality ultrasound model enhances non-invasive renal fibrosis assessment in CKD patients. By combining dual-modality radiomics with SWE measurements, this model offers a promising tool for personalized clinical decision-making and better management of CKD progression.