Multiparametric MRI-based whole-liver radiomics for predicting early-stage liver fibrosis in rabbits

• Published in: British journal of radiology 2024-05, Vol.97 (1157), p.964-970
• Main Authors: Mai, Xiao-Fei, Zhang, Hao, Wang, Yang, Zhong, Wen-Xin, Zou, Li-Qiu
• Format: Article
• Language: English
• Subjects: Animals; Contrast Media; Disease Models, Animal; Elasticity Imaging Techniques - methods; Liver - diagnostic imaging; Liver - pathology; Liver Cirrhosis - diagnostic imaging; Male; Multiparametric Magnetic Resonance Imaging - methods; Predictive Value of Tests; Rabbits; Radiomics
• ISSN: 0007-1285; 1748-880X; 1748-880X
• DOI: 10.1093/bjr/tqae063

To develop and validate a whole-liver radiomic model using multiparametric MRI for predicting early-stage liver fibrosis (LF) in rabbits. 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). 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. Our study highlights the remarkable performance of a multiparametric MRI-based radiomic model for the individualized diagnosis of early-stage LF. This is the first study to develop a combined model by integrating multiparametric radiomic features to improve the accuracy of LF staging.