The Association Between Tumor Radiomic Analysis and Peritumor Habitat‐Derived Radiomic Analysis on Gadoxetate Disodium‐Enhanced MRI With Microvascular Invasion in Hepatocellular Carcinoma

Abstract

BackgroundHepatocellular carcinoma (HCC) has a poor prognosis, often characterized by microvascular invasion (MVI). Radiomics and habitat imaging offer potential for preoperative MVI assessment.PurposeTo identify MVI in HCC by habitat imaging, tumor radiomic analysis, and peritumor habitat‐derived radiomic analysis.Study TypeRetrospective.SubjectsThree hundred eighteen patients (53 ± 11.42 years old; male = 276) with pathologically confirmed HCC (training:testing = 224:94).Field Strength/Sequence1.5 T, T2WI (spin echo), and precontrast and dynamic T1WI using three‐dimensional gradient echo sequence.AssessmentClinical model, habitat model, single sequence radiomic models, the peritumor habitat‐derived radiomic model, and the combined models were constructed for evaluating MVI. Follow‐up clinical data were obtained by a review of medical records or telephone interviews.Statistical TestsUnivariable and multivariable logistic regression, receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, K‐M curves, log rank test. A P‐value less than 0.05 (two sides) was considered to indicate statistical significance.ResultsHabitat imaging revealed a positive correlation between the number of subregions and MVI probability. The Radiomic‐Pre model demonstrated AUCs of 0.815 (95% CI: 0.752–0.878) and 0.708 (95% CI: 0.599–0.817) for detecting MVI in the training and testing cohorts, respectively. Similarly, the AUCs for MVI detection using Radiomic‐HBP were 0.790 (95% CI: 0.724–0.855) for the training cohort and 0.712 (95% CI: 0.604–0.820) for the test cohort. Combination models exhibited improved performance, with the Radiomics + Habitat + Dilation + Habitat 2 + Clinical Model (Model 7) achieving the higher AUC than Model 1–4 and 6 (0.825 vs. 0.688, 0.726, 0.785, 0.757, 0.804, P = 0.013, 0.048, 0.035, 0.041, 0.039, respectively) in the testing cohort. High‐risk patients (cutoff value >0.11) identified by this model showed shorter recurrence‐free survival.Data ConclusionThe combined model including tumor size, habitat imaging, radiomic analysis exhibited the best performance in predicting MVI, while also assessing prognostic risk.Evidence Level3Technical EfficacyStage 2