Radiomics Analysis Based on Multiparametric MRI for Predicting Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy.BACKGROUND
 To investigate the role of radiomics analysis based on multiparametric MRI (mpMRI) for predicting ER in HCC after partial hepatectomy.


 1.5T or 3.0T, gradient-recalled-echo in-phase T1 -weighted imaging (I-T1 WI) and opposed-phase T1 WI (O-T1 WI), fast spin-echo T2 -weighted imaging (T2 WI), spin-echo planar diffusion-weighted imaging (DWI), and gradient-recalled-echo contrast-enhanced MRI (CE-MRI).


 The radiomics model based on I-T1 WI, O-T1 WI, T2 WI, and CE-MRI sequences presented the best performance among all radiomics models with an area under the ROC curve (AUC) of 0.771 (95% confidence interval (CI): 0.598-0.894) in the validation cohort.  The mpMRI-based radiomics analysis has potential to predict ER of HCC patients after hepatectomy, which could enhance risk stratification and provide support for individualized treatment planning.



MRI
In all, 1146 radiomics features were extracted from each image sequence, and radiomics models based on each sequence and their combination were established via multivariate logistic regression analysis. 
multivariate logistic regression analysis
Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative performance of each model. 
receiver operating characteristic (ROC) curve analysis
The potential clinical usefulness was evaluated by decision curve analysis (DCA).



decision curve analysis (DCA)
A nomogram based on the combined model was established.


 The combined nomogram (AUC: 0.873; 95% CI: 0.756-0.989) outperformed the radiomics model and the CPR model (AUC: 0.742; 95% CI: 0.577-0.907).  DCA demonstrated that the combined nomogram was clinically useful.



nomogram