Noninvasive prediction of T-score in IgA nephropathy using machine learning within the Oxford classification system.

Introduction: IgA nephropathy (IgAN) is the most common glomerulonephritis worldwide. However, studies utilizing computed tomography (CT) to evaluate the severity of renal interstitial fibrosis in IgAN remain scarce. Objective: To explore the feasibility and value of combining pretreatment abdominal CT radiomics features with clinical characteristics and machine learning algorithms to determine the Oxford classification T score(renal interstitial fibrosis) of patients with IgAN. Methods: This retrospective study included 343 patients with IgAN from the First Affiliated Hospital of Zhengzhou University, confirmed by renal biopsy, pretreatment abdominal CT, and clinical data. The patients were divided into training (n = 240) and testing (n = 103) cohorts in a 7:3 ratio. Two senior radiologists delineated the regions of interest, and radiomic features were extracted from the CT images. The extracted radiomic attributes were subjected to least absolute shrinkage and selection operator (LASSO) regression with ten-fold cross-validation, thereby identifying a parsimonious subset of high-weighted imaging biomarkers that confer maximal discriminative power for the prediction of renal interstitial fibrosis. Based on clinical features, radiomic features, or a combination of both, random forest algorithms were employed to construct three-class discrimination models for the Oxford classification T-score of patients with IgAN. The diagnostic performance of the models was evaluated using receiver operating characteristic curves. Results: After feature selection, 26 radiomics features demonstrated predictive efficacy in diagnosing the T-score and were used to establish the radiomics model. The clinical radiomic model exhibited the best diagnostic performance. To diagnose patients with IgAN of Oxford classification T0, the model achieved an area under the curve (AUC) of 0.94 in the training cohort and 0.94 in the testing cohort. For T1 classification, the AUC was 0.97 in the training and 0.96 in the testing cohorts. For T2 classification, the AUC was 0.94 and 0.95 in the training and testing cohorts, respectively. Conclusions: The classification diagnostic model based on CT radiomics and clinical features combined with machine learning can accurately predict the Oxford classification T-score in patients with IgAN.