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• Clinical Article •
Clinical application value of predicting microvascular invasion in hepatocellular carcinoma using intratumoral and peritumoral radiomics models: A multicenter study
ZHU Zhu  YU Yixing  LU Jian  XU Dabo  ZHANG Tao  FANG Wei  LU Xinyu  GU Wenhao 

Cite this article as: ZHU Z, YU Y X, LU J, et al. Clinical application value of predicting microvascular invasion in hepatocellular carcinoma using intratumoral and peritumoral radiomics models: A multicenter study[J]. Chin J Magn Reson Imaging, 2024, 15(8): 132-138. DOI:10.12015/issn.1674-8034.2024.08.020.


[Abstract] Objective The aim of this study was to evaluate the predictive value of intratumoral and peritumoral radiomics models for microvascular invasion (MVI) in hepatocellular carcinoma (HCC).Materials and Methods Gadoxetic acid disodium (Gd-EOB-DTPA) enhanced MRI images of patients with surgically pathologically confirmed HCC at three hospitals between 2016 and 2023 were retrospectively analyzed, as well as seven clinical information, including gender, age, maximum tumor diameter, alpha-fetoprotein (AFP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and the presence or absence of hepatitis B. Intratumoral regions and 5 mm and 10 mm peritumoral regions of interest (ROI) were outlined in arterial phase images, portal venous phase images, and hepatobiliary phase images, from which radiomics features were extracted; in the training cohort, multifactorial logistic regression analysis was applied to screen independent clinical predictors of MVI; support vector machine (SVM) was applied to establish a total of 10 models including intratumoral models, peritumoral models, intratumoral combined peritumoral models, clinical model, and clinical-radiomics combined model. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the models and DeLong test was employed to compare the difference of area under the curve (AUC).Results Maximum tumor diameter [dominance ratio (OR): 1.449, 95% confidence interval (CI): 1.212-1.733] and AFP (OR: 1.645, 95% CI: 0.665-4.071) were independent clinical predictors of MVI based on the training cohort. In the validation cohort, the AUCs of the clinical model, intratumoral models, peritumoral models, intratumoral plus peritumoral models, and clinical-radiomics combined model for predicting MVI of HCC were 0.728, 0.764-0.820, 0.791-0.795, 0.804-0.828, and 0.747, respectively, and those of the intratumoral plus 5 mm peritumoral model, intratumoral plus 10 mm peritumoral model were 0.828 (95% CI: 0.728-0.929), 0.804 (95% CI: 0.696-0.913). Among the models, the AUC of the intratumoral plus 5 mm peritumoral model was statistically different from that of the clinical model and the clinical-radiomics combined model (P=0.039, 0.028), and the differences in the AUCs among the rest of the models were not statistically significant (P>0.05).Conclusions The Gd-EOB-DTPA-based enhanced MRI radiomics models can be used for preoperative prediction of HCC MVI, in which the intratumoral plus 5 mm peritumoral model has a higher predictive ability for HCC MVI. This model helps in the development of individualized treatment.
[Keywords] radiomics；magnetic resonance imaging；Gd-EOB-DTPA；microvascular invasion；hepatocarcinoma

ZHU Zhu1   YU Yixing2   LU Jian3   XU Dabo1   ZHANG Tao3   FANG Wei1   LU Xinyu1   GU Wenhao1*  

1 Department of Radiology, The First People's Hospital of Taicang, Affiliated Hospital of Soochow University, Suzhou 215400, China

2 Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China

3 Department of Radiology, The Third Affiliated Hospital of Nantong University, The Third People's Hospital of Nantong, Nantong 226000, China

Corresponding author: GU W H, E-mail: tyyyxkgu@163.com

Conflicts of interest   None.

Received  2024-03-03
Accepted  2024-07-12
DOI: 10.12015/issn.1674-8034.2024.08.020
Cite this article as: ZHU Z, YU Y X, LU J, et al. Clinical application value of predicting microvascular invasion in hepatocellular carcinoma using intratumoral and peritumoral radiomics models: A multicenter study[J]. Chin J Magn Reson Imaging, 2024, 15(8): 132-138. DOI:10.12015/issn.1674-8034.2024.08.020.

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