A MRI-based radiomics nomogram for evaluation of renal function in ADPKD

摘要

Abstract Objectives This study is aimed to establish a fusion model of radiomics-based nomogram to predict the renal function of autosomal dominant polycystic kidney disease (ADPKD).Methods One hundred patients with ADPKD were randomly divided into training group (n?=?69) and test group (n?=?31). The radiomics features were extracted from T1-weighted fat suppression images (FS-T1WI) and T2-weighted fat suppression images (FS-T2WI). Decision tree algorithm was employed to build radiomics model to get radiomics signature. Then multivariate logistic regression analysis was used to establish the radiomics nomogram based on independent clinical factors, conventional MR imaging variables and radiomics signature. The receiver operating characteristic (ROC) analysis and Delong test were used to compare the performance of radiomics model and radiomics nomogram model, and the decision curve to evaluate the clinical application value of radiomics nomogram model in the evaluation of renal function in patients with ADPKD.Results Fourteen radiomics features were selected to establish radiomics model. Based on FS-T1WI and FS-T2WI sequences, the radiomics model showed good discrimination ability in training group and test group training group: (AUC)?=?0.7542, test group (AUC)?=?0.7417. The performance of radiomics nomogram model was significantly better than that of radiomics model in all data sets radiomics model (AUC)?=?0.7505, radiomics nomogram model (AUC)?=?0.8435, p value?=?0.005. The analysis of calibration curve and decision curve showed that radiomics nomogram model had more clinical application value.Conclusion radiomics analysis of MRI can be used for the preliminary evaluation and prediction of renal function in patients with ADPKD. The radiomics nomogram model shows better prediction effect in renal function evaluation, and can be used as a non-invasive renal function prediction tool to assist clinical decision-making.Trial registration ChiCTR, ChiCTR2100046739. Registered 27 May 2021—retrospectively registered, http://www.ChiCTR.org.cn/showproj.aspx?proj=125955Graphical abstract