Details

Autor(en) / Beteiligte

• Chen, Ziman
• Ying, Michael Tin Cheung
• Wang, Yingli
• Chen, Jiaxin
• Wu, Chaoqun
• Han, Xinyang
• Su, Zhongzhen

Titel

Ultrasound-based radiomics analysis in the assessment of renal fibrosis in patients with chronic kidney disease

Ist Teil von

• Abdominal radiology (New York), 2023-08, Vol.48 (8), p.2649-2657

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2023

Quelle

SpringerNature Journals

Beschreibungen/Notizen

• Purpose Assessment of renal fibrosis non-invasively in chronic kidney disease (CKD) patients is still a clinical challenge. In this study, we aimed to establish a radiomics model integrating radiomics features derived from ultrasound (US) images with clinical characteristics for the assessment of renal fibrosis severity in CKD patients. Methods A total of 160 patients with CKD who underwent kidney biopsy and renal US examination were prospectively enrolled. Patients were classified into the mild or moderate-severe fibrosis group based on pathology results. Radiomics features were extracted from the US images, and a radiomics signature was constructed using the maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) regression algorithms. Multivariable logistic regression was employed to construct the radiomics model, which incorporated the radiomics signature and the selected clinical variables. The established model was evaluated for discrimination, calibration, and clinical utility in the derivation cohort and internal cross-validation (CV) analysis, respectively. Results The radiomics signature, consisting of nine identified fibrosis-related features, achieved moderate discriminatory ability with an area under the receiver operating characteristic curve (AUC) of 0.72 (95% confidence interval (CI) 0.64–0.79). By combining the radiomics signature with significant clinical risk factors, the radiomics model showed satisfactory discrimination performance, yielding an AUC of 0.85 (95% CI 0.79–0.91) in the derivation cohort and a mean AUC of 0.84 (95% CI 0.77–0.92) in the internal CV analysis. It also demonstrated fine accuracy via the calibration curve. Furthermore, the decision curve analysis indicated that the model was clinically useful. Conclusion The proposed radiomics model showed favorable performance in determining the individualized risk of moderate-severe renal fibrosis in patients with CKD, which may facilitate more effective clinical decision-making.