Data Sheet 1_Noninvasive prediction of microvascular invasion in hepatocellular carcinoma using a fusion model of 5-mm optimal peritumoral radiomics and imaging features based on multiphasic gadoxetic acid-enhanced MRI.doc

Multivariable analysis was performed to identify significant imaging and clinical factors. Seven different models were developed. By applying receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA), we evaluated the model’s performance, with internal validation conducted through bootstrapping to compensate for the limitations of the small sample size.</p>Results<p>The radiomics and imaging feature fusion model showed good discrimination ability and achieved an area under the curve (AUC) of 0.957 (95% CI: 0.906-1) in the training cohort and 0.937 (95% CI: 0.863-1) in the validation cohort.

Bootstrapping validated the stability of the fusion model with an AUC value of 0.951 (95% CI: 0.909-0.982). The 5-mm peritumoral radiomics model had excellent predictive power for MVI with higher accuracy compared to the 10-mm peritumoral radiomic model (81.4% vs. 69.8%, P = 0.008). The accuracy of peritumoral radiomics was superior to that of intratumoral radiomics (AUC: 0.865 vs. 0.797, P = 0.002).</p>Conclusion<p>The construction of a radiomic signature based on a 5-mm peritumoral region on MRI is optimal to preoperatively predict MVI in HCC.

Our outcomes demonstrate that an integrated model incorporating radiomic features associated with the tumor microenvironment at this new biological scale can be a valuable tool for preoperative risk assessment.</p>