基于对比增强磁共振成像和三维卷积神经网络的肝细胞癌微血管侵犯预测
Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks.
作者信息
Zhou Wu, Jian Wanwei, Cen Xiaoping, Zhang Lijuan, Guo Hui, Liu Zaiyi, Liang Changhong, Wang Guangyi
机构信息
School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
出版信息
Front Oncol. 2021 Mar 4;11:588010. doi: 10.3389/fonc.2021.588010. eCollection 2021.
BACKGROUND AND PURPOSE
It is extremely important to predict the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) before surgery, which is a key predictor of recurrence and helps determine the treatment strategy before liver resection or liver transplantation. In this study, we demonstrate that a deep learning approach based on contrast-enhanced MR and 3D convolutional neural networks (CNN) can be applied to better predict MVI in HCC patients.
MATERIALS AND METHODS
This retrospective study included 114 consecutive patients who were surgically resected from October 2012 to October 2018 with 117 histologically confirmed HCC. MR sequences including 3.0T/LAVA (liver acquisition with volume acceleration) and 3.0T/e-THRIVE (enhanced T1 high resolution isotropic volume excitation) were used in image acquisition of each patient. First, numerous 3D patches were separately extracted from the region of each lesion for data augmentation. Then, 3D CNN was utilized to extract the discriminant deep features of HCC from contrast-enhanced MR separately. Furthermore, loss function for deep supervision was designed to integrate deep features from multiple phases of contrast-enhanced MR. The dataset was divided into two parts, in which 77 HCCs were used as the training set, while the remaining 40 HCCs were used for independent testing. Receiver operating characteristic curve (ROC) analysis was adopted to assess the performance of MVI prediction. The output probability of the model was assessed by the independent student's t-test or Mann-Whitney U test.
RESULTS
The mean AUC values of MVI prediction of HCC were 0.793 (p=0.001) in the pre-contrast phase, 0.855 (p=0.000) in arterial phase, and 0.817 (p=0.000) in the portal vein phase. Simple concatenation of deep features using 3D CNN derived from all the three phases improved the performance with the AUC value of 0.906 (p=0.000). By comparison, the proposed deep learning model with deep supervision loss function produced the best results with the AUC value of 0.926 (p=0.000).
CONCLUSION
A deep learning framework based on 3D CNN and deeply supervised net with contrast-enhanced MR could be effective for MVI prediction.
背景与目的
术前预测肝细胞癌（HCC）的微血管侵犯（MVI）极其重要，MVI是复发的关键预测指标，有助于确定肝切除或肝移植前的治疗策略。在本研究中，我们证明基于对比增强磁共振成像（MR）和三维卷积神经网络（3D CNN）的深度学习方法可用于更好地预测HCC患者的MVI。
材料与方法
本回顾性研究纳入了2012年10月至2018年10月期间连续114例行手术切除的患者，共117个经组织学证实的HCC。每位患者的图像采集采用包括3.0T/LAVA（肝脏容积加速采集）和3.0T/e-THRIVE（增强T1高分辨率各向同性容积激发）在内的MR序列。首先，从每个病变区域分别提取大量三维图像块进行数据增强。然后，利用3D CNN从对比增强MR中分别提取HCC的判别性深度特征。此外，设计深度监督损失函数以整合对比增强MR多期的深度特征。数据集分为两部分，其中77个HCC用作训练集，其余40个HCC用于独立测试。采用受试者操作特征曲线（ROC）分析评估MVI预测的性能。通过独立样本t检验或曼-惠特尼U检验评估模型的输出概率。
结果
HCC的MVI预测在平扫期的平均AUC值为0.793（p = 0.001），动脉期为0.855（p = 0.000），门静脉期为0.817（p = 0.000）。使用来自所有三个期的3D CNN对深度特征进行简单拼接可提高性能，AUC值为0.906（p = 0.000）。相比之下，提出的具有深度监督损失函数的深度学习模型产生了最佳结果，AUC值为0.926（p = 0.000）。
结论
基于3D CNN和对比增强MR的深度监督网络的深度学习框架对MVI预测可能有效。
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