Deep Learning of Liver Contrast-Enhanced Ultrasound to Predict Microvascular Invasion and Prognosis in Hepatocellular Carcinoma

Yafang Zhang; Qingyue Wei; Yini Huang; Zhao Yao; Cuiju Yan; Xuebin Zou; Jing Han; Qing Li; Rushuang Mao; Ying Liao; Lan Cao; Min Lin; Xiaoshuang Zhou; Xiaofeng Tang; Yixin Hu; Lingling Li; Yuanyuan Wang; Jinhua Yu; Jianhua Zhou

doi:10.3389/fonc.2022.878061

Deep Learning of Liver Contrast-Enhanced Ultrasound to Predict Microvascular Invasion and Prognosis in Hepatocellular Carcinoma

Front Oncol. 2022 Jul 7:12:878061. doi: 10.3389/fonc.2022.878061. eCollection 2022.

Authors

Yafang Zhang¹, Qingyue Wei², Yini Huang¹, Zhao Yao², Cuiju Yan¹, Xuebin Zou¹, Jing Han¹, Qing Li¹, Rushuang Mao¹, Ying Liao¹, Lan Cao¹, Min Lin¹, Xiaoshuang Zhou¹, Xiaofeng Tang¹, Yixin Hu¹, Lingling Li¹, Yuanyuan Wang², Jinhua Yu², Jianhua Zhou¹

Affiliations

¹ Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
² School of Information Science and Technology, Fudan University, Shanghai, China.

Abstract

Background and aims: Microvascular invasion (MVI) is a well-known risk factor for poor prognosis in hepatocellular carcinoma (HCC). This study aimed to develop a deep convolutional neural network (DCNN) model based on contrast-enhanced ultrasound (CEUS) to predict MVI, and thus to predict prognosis in patients with HCC.

Methods: A total of 436 patients with surgically resected HCC who underwent preoperative CEUS were retrospectively enrolled. Patients were divided into training (n = 301), validation (n = 102), and test (n = 33) sets. A clinical model (Clinical model), a CEUS video-based DCNN model (CEUS-DCNN model), and a fusion model based on CEUS video and clinical variables (CECL-DCNN model) were built to predict MVI. Survival analysis was used to evaluate the clinical performance of the predicted MVI.

Results: Compared with the Clinical model, the CEUS-DCNN model exhibited similar sensitivity, but higher specificity (71.4% vs. 38.1%, p = 0.03) in the test group. The CECL-DCNN model showed significantly higher specificity (81.0% vs. 38.1%, p = 0.005) and accuracy (78.8% vs. 51.5%, p = 0.009) than the Clinical model, with an AUC of 0.865. The Clinical predicted MVI could not significantly distinguish OS or RFS (both p > 0.05), while the CEUS-DCNN predicted MVI could only predict the earlier recurrence (hazard ratio [HR] with 95% confidence interval [CI 2.92 [1.1-7.75], p = 0.024). However, the CECL-DCNN predicted MVI was a significant prognostic factor for both OS (HR with 95% CI: 6.03 [1.7-21.39], p = 0.009) and RFS (HR with 95% CI: 3.3 [1.23-8.91], p = 0.011) in the test group.

Conclusions: The proposed CECL-DCNN model based on preoperative CEUS video can serve as a noninvasive tool to predict MVI status in HCC, thereby predicting poor prognosis.

Keywords: contrast-enhanced ultrasound; deep learning; hepatocellular carcinoma; microvascular invasion; prognosis.