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

Abstract

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 < 0.05 was considered statistically significant. Results: In the validation cohort, the combined radiomics model (area under the curve [AUC] = 0.908, 95% confidence interval [CI]: 0.831-0.985) showed the highest diagnostic performance. The AUCs of the PP (AUC = 0.879, 95% CI: 0.779-0.979) and combined radiomics models were significantly higher than that of clinical model (AUC = 0.685, 95% CI: 0.526-0.844). There were no significant differences in AUC between AP or PP radiomics model and combined radiomics model (P = 0.286, 0.180 and 0.543). Conclusion: MRI radiomics models may be useful for discriminating DPHCC from non-DPHCC before surgery.

RETRACTED: Dual-phenotype hepatocellular carcinoma: correlation of MRI features with other primary hepatocellular carcinoma and differential diagnosis

“…MRI provides extensive diagnostic information through multi-parameter and multi-sequence imaging. In recent years, with the rapid development of artificial intelligence, a few studies ( 15 , 16 ) had applied radiomics to preoperatively diagnose DPHCC. However, the limited sample size and single-center data in some study may hinder the generalization of the model.…”

Hepatocellular carcinoma and focal nodular hyperplasia showing iso- or hyperintensity in the hepatobiliary phase: differentiation using Gd-EOB-DTPA enhanced MRI radiomics and deep learning features

“…Radiomics has been widely used in the study of liver disease. For example, Yu et al 24 established intra-tumoral and peritumoral radiomics models to predict vessels encapsulating tumor clusters (VETC) in HCC, and Wu et al 25 found that MRI radiomics models may be useful for discriminating dual-phenotype hepatocellular carcinoma (DPHCC) from non-DPHCC before surgery. In addition, compared to conventional machine learning methods, deep learning 26,27 has emerged as a state-of-the-art machine learning method in many areas.…”

MRI‐Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges

“…46 The radiomics model derived from arterial and portal venous phases could predict nondual-phenotype HCC (both CK7 and CK19 negative) with an AUC of 0.91 in the validation set. 47 Other biomarkers associated with the HCC subtype or prognosis have also been explored using a radiomics approach, including VEGF expression and β-catenin mutation. 48,49 Furthermore, the tumor immune microenvironment may guide immunotherapy and reflect the prognosis in HCC patients.…”

“…Wang developed a model combining 17 radiomic features extracted from the arterial phase and HBP, achieving AUCs of 0.82 46 . The radiomics model derived from arterial and portal venous phases could predict non‐dual‐phenotype HCC (both CK7 and CK19 negative) with an AUC of 0.91 in the validation set 47 . Other biomarkers associated with the HCC subtype or prognosis have also been explored using a radiomics approach, including VEGF expression and β‐catenin mutation 48,49 .…”