Journal of Shandong University (Health Sciences) ›› 2026, Vol. 64 ›› Issue (6): 60-67.doi: 10.6040/j.issn.1671-7554.0.2025.0946

• Clinical Medicine • Previous Articles    

MRI based radiomics nomogram differentiate between hepatocellular carcinoma and intrahepatic mass-forming cholangiocarcinoma in HBV patients

WANG Linxiang1,2, CUI Jin2, WANG Lianbang2, QI Xu2, WANG Gongzheng2, WANG Ximing1,2   

  1. 1. Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Department of Radiology, Shandong Provincial Hospital, Jinan 250021, Shandong, China
  • Published:2026-06-29

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Abstract: Objective To evaluate the performance of differentiating hepatocellular carcinoma(HCC)and intrahepatic mass-forming cholangiocarcinoma(IMCC)based on multiparametric MRI radiomics nomogram. Methods The retrospective, multicenter study involved 206 patients with either HCC or IMCC, who were enrolled from three hospitals between August 2016 and September 2022. Patients were divided into a training cohort(126 cases)and a testing cohort(80 cases)based on different hospitals. And the radiomics features of three sequences(T1-FS, T2-FS, and DWI)were extracted. The four machine learning algorithms were used to construct radiomics models. The optimal model was selected for further analysis. Independent clinical features were identified through univariate and multivariate analysis, which were then used to construct the clinical model. Subsequently, the radiomics nomogram was established by integrating independent clinical features with the selected radiomics model. The validation of all models was conducted through 5-fold cross-validation for hyperparameter optimization in the training cohort and evaluated in the external testing cohort. The receiver operating characteristic(ROC)curve was used to evaluate the model performance. The Delong test was used to compare the model differences. Results ROC analysis of the testing cohort demonstrated that the optimal radiomics model(T1+T2+DWI)by employing linear support vector machine achieved an area under curve(AUC)of 0.929(95%CI: 0.872-0.986), with sensitivity of 0.879, specificity of 0.894, and accuracy of 0.888. In conjunction with alpha fetoprotein, carbohydrate antigen 199 and sex, the clinical-radiomics nomogram exhibited superior performance, achieving an AUC of 0.951(95%CI: 0.910-1.000), with sensitivity of 0.909, specificity of 0.936, and accuracy of 0.925. These results were statistically superior to both the clinical model(AUC=0.822, 95%CI: 0.730-0.910; P=0.008)and the radiomics model(P=0.038), indicating significant added value from the integration of clinical and radiomic features. Conclusion The radiomics nomogram can distinguish between IMCC and HCC in patients with hepatitis B virus, thus facilitating more precise treatment plan, particularly for patients who are not able to tolerate enhanced scanning procedures.

Key words: Radiomics, Primary liver cancer, Hepatitis B virus, Magnetic resonance imaging

CLC Number: 

  • R445
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