Predictive machine learning model for microvascular invasion identification in hepatocellular carcinoma based on the LI-RADS system

Xue Yang; Guoqing Shao; Jiaojiao Liu; Bin Liu; Chao Cai; Daobing Zeng; Hongjun Li

doi:10.3389/fonc.2022.1021570

Predictive machine learning model for microvascular invasion identification in hepatocellular carcinoma based on the LI-RADS system

Front Oncol. 2022 Nov 8:12:1021570. doi: 10.3389/fonc.2022.1021570. eCollection 2022.

Authors

Xue Yang¹, Guoqing Shao², Jiaojiao Liu¹, Bin Liu³, Chao Cai¹, Daobing Zeng^{4

5}, Hongjun Li¹

Affiliations

¹ Department of Radiology, Beijing You'an Hospital, Capital Medical University, Beijing, China.
² Department of Radiology, Xuzhou Central Hospital, Xuzhou, China.
³ Department of Radiology, Civil Aviation General Hospital, Beijing, China.
⁴ General Surgery Department, Beijing Youan Hospital, Capital Medical University, Beijing, China.
⁵ Clinical Center for Liver Cancer, Capital Medical University, Beijing, China.

Abstract

Purposes: This study aimed to establish a predictive model of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) by contrast-enhanced computed tomography (CT), which relied on a combination of machine learning approach and imaging features covering Liver Imaging and Reporting and Data System (LI-RADS) features.

Methods: The retrospective study included 279 patients with surgery who underwent preoperative enhanced CT. They were randomly allocated to training set, validation set, and test set (167 patients vs. 56 patients vs. 56 patients, respectively). Significant imaging findings for predicting MVI were identified through the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression method. Predictive models were performed by machine learning algorithm, support vector machine (SVM), in the training set and validation set, and evaluated in the test set. Further, a combined model adding clinical findings to the radiologic model was developed. Based on the LI-RADS category, subgroup analyses were conducted.

Results: We included 116 patients with MVI which were diagnosed through pathological confirmation. Six imaging features were selected about MVI prediction: four LI-RADS features (corona enhancement, enhancing capsule, non-rim aterial phase hyperehancement, tumor size) and two non-LI-RADS features (internal arteries, non-smooth tumor margin). The radiological feature with the best accuracy was corona enhancement followed by internal arteries and tumor size. The accuracies of the radiological model and combined model were 0.725-0.714 and 0.802-0.732 in the training set, validation set, and test set, respectively. In the LR-4/5 subgroup, a sensitivity of 100% and an NPV of 100% were obtained by the high-sensitivity threshold. A specificity of 100% and a PPV of 100% were acquired through the high specificity threshold in the LR-M subgroup.

Conclusion: A combination of LI-RADS features and non-LI-RADS features and serum alpha-fetoprotein value could be applied as a preoperative biomarker for predicting MVI by the machine learning approach. Furthermore, its good performance in the subgroup by LI-RADS category may help optimize the management of HCC patients.

Keywords: contrast enhanced computed tomography; hepatocellular carcinoma; liver imaging reporting and data system; machine learning; microvascular invasion.