Preoperative Diagnosis of Dual‐Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models

Background Dual‐phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC). Purpose To develop and validate clinical and radiomics models based on contrast‐enhanced MRI for the preoperative diagnosis of DPHCC. Study type Retrospective. Population A total of 87 patients with DPHCC and 92 patients with non‐DPHCC randomly divided into a training cohort (n = 125: 64 non‐DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non‐DPHCC; 26 DPHCC). Field Strength/Sequence A 3.0 T; dynamic contrast‐enhanced MRI with time‐resolved T1‐weighted imaging sequence. Assessment In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression‐least absolute shrinkage and selection operator [LR‐LASSO]) and their discriminatory efficacy assessed and compared. Statistical Tests The independent sample t test, Mann–Whitney U test, Chi‐square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value