Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI

Huazheng Shi; Ying Duan; Jie Shi; Wenrui Zhang; Weiran Liu; Bixia Shen; Fufu Liu; Xin Mei; Xiaoxiao Li; Zheng Yuan

doi:10.3389/fphys.2022.928969

Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI

Front Physiol. 2022 Aug 12:13:928969. doi: 10.3389/fphys.2022.928969. eCollection 2022.

Authors

Huazheng Shi¹, Ying Duan¹, Jie Shi², Wenrui Zhang¹, Weiran Liu¹, Bixia Shen¹, Fufu Liu¹, Xin Mei¹, Xiaoxiao Li¹, Zheng Yuan³

Affiliations

¹ Shanghai Universal Cloud Medical Imaging Diagnostic Center, Shanghai, China.
² Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, The Second Military Medical University, Shanghai, China.
³ Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Objective: To investigate the role of prediction microvascular invasion (mVI) in hepatocellular carcinoma (HCC) by ¹⁸F-FDG PET image texture analysis and hybrid criteria combining PET/CT and multi-parameter MRI. Materials and methods: Ninety-seven patients with HCC who received the examinations of MRI and ¹⁸F-FDG PET/CT were retrospectively included in this study and were randomized into training and testing cohorts. The lesion image texture features of ¹⁸F-FDG PET were extracted using MaZda software. The optimal predictive texture features of mVI were selected, and the classification procedure was conducted. The predictive performance of mVI by radiomics classier in training and testing cohorts was respectively recorded. Next, the hybrid model was developed by integrating the ¹⁸F-FDG PET image texture, metabolic parameters, and MRI parameters to predict mVI through logistic regression. Furthermore, the diagnostic performance of each time was recorded. Results: The ¹⁸F-FDG PET image radiomics classier showed good predicted performance in both training and testing cohorts to discriminate HCC with/without mVI, with an AUC of 0.917 (95% CI: 0.824-0.970) and 0.771 (95% CI: 0.578, 0.905). The hybrid model, which combines radiomics classier, SUVmax, ADC, hypovascular arterial phase enhancement pattern on contrast-enhanced MRI, and non-smooth tumor margin, also yielded better predictive performance with an AUC of 0.996 (95% CI: 0.939, 1.000) and 0.953 (95% CI: 0.883, 1.000). The differences in AUCs between radiomics classier and hybrid classier were significant in both training and testing cohorts (DeLong test, both p < 0.05). Conclusion: The radiomics classier based on ¹⁸F-FDG PET image texture and the hybrid classier incorporating ¹⁸F-FDG PET/CT and MRI yielded good predictive performance, which might provide a precise prediction of HCC mVI preoperatively.

Keywords: fluorine-18 fluorodeoxyglucose-positron emission tomography (18F-FDG-PET); hepatocellular carcinoma; magnetic resonance imaging; microvascular invasion; texture feature.