Radiomics-Based prediction of renal prognosis in ANCA-associated vasculitis
Abstract
To develop and validate a non-invasive prediction model combining renal CT radiomics features and clinical markers for assessing long-term renal prognosis at 1, 3, and 5 years in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). A retrospective cohort of 262 AAV patients from Peking Union Medical College Hospital (2015–2020) was analyzed. Radiomics features (morphological, first-order, and texture) were extracted from segmented renal parenchyma on abdominopelvic CT. Clinical markers included demographics, serum creatinine, complement component 3 (C3), and other laboratory indicators. Renal volume, serum creatinine, and complement component 3 (C3) were pre-specified as predictors based on a priori biological rationale, and XGBoost was employed for model construction. Performance was evaluated via ROC-AUC (receiver operating characteristic/area under the curve), F1 score, decision curve analysis, and calibration curves. SHAP (SHapley Additive exPlanations) analysis assessed feature contributions, and Kaplan–Meier curves with Bonferroni-corrected log-rank tests were used for survival stratification. The mean follow-up was 40.8 months; 59 patients (23%) developed ESRD (56 within 3 years) and 29 (11%) died. The combined model (renal volume + sCr + C3) achieved an AUC of 0.93 for 3-year ESRD prediction (clinical-only model: 0.91), with renal volume independently associated with ESRD (Wald p = 0.004). For the 3-year composite outcome, the combined model achieved an AUC of 0.84 (clinical-only: 0.81) with significantly improved reclassification (IDI p = 0.018; category-free NRI p = 0.024). In pre-specified median-based Kaplan–Meier analyses, reduced renal volume (≤ 130.6 mL), elevated serum creatinine (> 177.5 μmol/L) and lower C3 (≤ 0.97 g/L) were associated with poorer 3-year ESRD-free survival (log-rank p ≤ 0.002). The integrated radiomics-clinical model provides accurate, non-invasive risk stratification for long-term renal outcomes in AAV, facilitating personalized treatment.