End-to-End CT Radiomics-Based Pipeline for Predicting Renal Interstitial Fibrosis Grade in CKD Patients.

Abstract

Rationale and objectives: Non-invasive assessment of renal fibrosis in patients with chronic kidney disease (CKD) remains a clinical challenge. This study aims to integrate radiomics and clinical factors to develop an end-to-end pipeline for predicting interstitial fibrosis (IF) in CKD patients.

Materials and methods: This retrospective study included 80 patients with CKD, with 53 patients in training set and 27 patients in test set. All patients underwent renal computed tomography (CT) scans and biopsy. Patients were classified into two groups based on their renal IF grade: mild-moderate and severe. Radiomics features were extracted from the automatically segmented right renal region on CT images, and univariate analysis along with multiple Least Absolute Shrinkage and Selection Operator (LASSO) was employed to construct the radiomics signature. Subsequently, logistic regression models were developed to create the radiomics model and the combined model. The predictive performance of both models was evaluated through discrimination, calibration, and decision curve analysis (DCA), and a nomogram was constructed for the model demonstrating superior performance.

Results: The combined model significantly outperformed the radiomics model, achieving a cross-validated AUC of 0.935±0.041 in the training set, compared to 0.804±0.024 for the radiomics model. In the test set, the combined model outperformed the radiomics model, with an AUC of 0.918 [95% CI 0.799-1] vs. 0.764 [95% CI 0.549-0.979], p=0.031 (DeLong test, Statistic: -2.152). Calibration curves and DCA indicated that the combined model demonstrated good calibration and better clinical net benefit.

Conclusion: This end-to-end workflow could serve as a potential non-invasive tool to predict renal IF grade (mild-moderate vs. severe) in CKD patients.