biostudies-literatureMai XFNational Natural Science Foundation of China964-970https://www.ebi.ac.uk/biostudies/studies/S-EPMC11075985biostudies-literatureUnknown97(1157)<h4>Objectives</h4>To develop and validate a whole-liver radiomic model using multiparametric MRI for predicting early-stage liver fibrosis (LF) in rabbits.<h4>Methods</h4>A total of 134 rabbits (early-stage LF, n = 91; advanced-stage LF, n = 43) who underwent liver magnetic resonance elastography (MRE), hepatobiliary phase, dynamic contrast enhanced (DCE), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging, and T2* scanning were enrolled and randomly allocated to either the training or validation cohort. Whole-liver radiomic features were extracted and selected to develop a radiomic model and generate quantitative Rad-scores. Then, multivariable logistic regression was utilized to determine the Rad-scores associated with early-stage LF, and effective features were integrated to establish a combined model. The predictive performance was assessed by the area under the curve (AUC).<h4>Results</h4>The MRE model achieved superior AUCs of 0.95 in the training cohort and 0.86 in the validation cohort, followed by the DCE-MRI model (0.93 and 0.82), while the IVIM model had lower AUC values of 0.91 and 0.82, respectively. The Rad-scores of MRE, DCE-MRI and IVIM were identified as independent predictors associated with early-stage LF. The combined model demonstrated AUC values of 0.96 and 0.88 for predicting early-stage LF in the training and validation cohorts, respectively.<h4>Conclusions</h4>Our study highlights the remarkable performance of a multiparametric MRI-based radiomic model for the individualized diagnosis of early-stage LF.<h4>Advances in knowledge</h4>This is the first study to develop a combined model by integrating multiparametric radiomic features to improve the accuracy of LF staging.The British journal of radiologyMultiparametric MRI-based whole-liver radiomics for predicting early-stage liver fibrosis in rabbits.PMC1107598581771805Zou LQMai XFZhang HWang YZhong WXfalseMultiparametric MRI-based whole-liver radiomics for predicting early-stage liver fibrosis in rabbits.<h4>Objectives</h4>To develop and validate a whole-liver radiomic model using multiparametric MRI for predicting early-stage liver fibrosis (LF) in rabbits.<h4>Methods</h4>A total of 134 rabbits (early-stage LF, n = 91; advanced-stage LF, n = 43) who underwent liver magnetic resonance elastography (MRE), hepatobiliary phase, dynamic contrast enhanced (DCE), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging, and T2* scanning were enrolled and randomly allocated to either the training or validation cohort. Whole-liver radiomic features were extracted and selected to develop a radiomic model and generate quantitative Rad-scores. Then, multivariable logistic regression was utilized to determine the Rad-scores associated with early-stage LF, and effective features were integrated to establish a combined model. The predictive performance was assessed by the area under the curve (AUC).<h4>Results</h4>The MRE model achieved superior AUCs of 0.95 in the training cohort and 0.86 in the validation cohort, followed by the DCE-MRI model (0.93 and 0.82), while the IVIM model had lower AUC values of 0.91 and 0.82, respectively. The Rad-scores of MRE, DCE-MRI and IVIM were identified as independent predictors associated with early-stage LF. The combined model demonstrated AUC values of 0.96 and 0.88 for predicting early-stage LF in the training and validation cohorts, respectively.<h4>Conclusions</h4>Our study highlights the remarkable performance of a multiparametric MRI-based radiomic model for the individualized diagnosis of early-stage LF.<h4>Advances in knowledge</h4>This is the first study to develop a combined model by integrating multiparametric radiomic features to improve the accuracy of LF staging.2024-01-01T00:00:00Z2024 May2025-07-13T03:04:08.695Z2025-07-13T03:04:08.695ZS-EPMC110759853855232110.1093/bjr/tqae063