无线电技术
肝纤维化
阶段(地层学)
医学
多参数磁共振成像
纤维化
磁共振成像
放射科
病理
核医学
内科学
癌症
生物
古生物学
前列腺癌
作者
Xiao-Fei Mai,Hao Zhang,Yang Wang,Wei Zhong,Zou Liqiu
出处
期刊:British Journal of Radiology
[British Institute of Radiology]
日期:2024-03-29
摘要
To develop and validate a whole-liver radiomic model using multiparametric magnetic resonance imaging (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 (HBP), dynamic contrast enhanced (DCE), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) 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.
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