Combining Super-Resolution Imaging and Shear Wave Elastography for Enhanced Risk Assessment of Moderate-to-Severe Renal Fibrosis in Chronic Kidney Disease Patients

Abstract

Xingyue Huang,&ast; Yao Zhang,&ast; Yugang Hu, Juhong Pan, Xin Huang, Jun Zhang, Huan Pu, Yueying Chen, Qing Deng, Qing Zhou Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, 430061, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qing Zhou, Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, No. 99 of Zhangzhidong Road, Wuhan, 430061, People’s Republic of China, Tel [phone], Email [email protected] Qing Deng, Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, No. 99 of Zhangzhidong Road, Wuhan, 430061, People’s Republic of China, Tel [phone], Email [email protected]: This study aims to evaluate the diagnostic efficacy of shear wave elastography (SWE) and super-resolution imaging (SRI) in detecting moderate-to-severe renal fibrosis (MSRF) among patients with chronic kidney disease (CKD).Methods: In this prospective study, 202 CKD patients who underwent SWE and SRI prior to renal biopsy were enrolled. Based on pathological findings, patients were categorized into a mild renal fibrosis group (n=107) and an MSRF group (n=95). LASSO logistic regression was employed to identify independent risk factors for MSRF. Four diagnostic models—isolated, series, parallel, and integrated—were developed by combining elasticity values from SWE and vascular density values from SRI. Additionally, a nomogram incorporating clinical parameters and ultrasound composite parameters was constructed to assess MSRF in CKD patients.Results: LASSO and subsequent logistic regression analysis revealed that age, diabetes history, estimated glomerular filtration rate (eGFR), elasticity, and vascular density were independently associated with MSRF. The integrated model, utilizing a logistic algorithm, demonstrated superior diagnostic performance with an area under the curve (AUC) of 0.83 (P < 0.001), sensitivity of 80.4%, and specificity of 75.8%, outperforming all other models. Furthermore, the nomogram, which integrated clinical factors and ultrasound composite parameters, exhibited excellent predictive performance (AUC = 0.878, 95% CI 0.782– 0.974). Calibration and decision curve analyses confirmed the model’s robust calibration and clinical utility.Conclusion: The integration of SWE-derived elasticity and SRI-derived vascular density significantly enhances the diagnostic accuracy for MSRF in CKD patients. This comprehensive approach offers a promising non-invasive strategy for assessing renal fibrosis severity.Keywords: chronic kidney disease, renal fibrosis, shear wave elastography, super-resolution imaging

Keywords

• Chronic kidney disease
• renal fibrosis
• shear wave elastography
• super-resolution imaging.