Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks
Background and PurposeIt 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 demonstra...
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Frontiers Media S.A.
2021-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.588010/full |
| _version_ | 1818743310612692992 |
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| author | Wu Zhou Wanwei Jian Xiaoping Cen Lijuan Zhang Hui Guo Zaiyi Liu Changhong Liang Guangyi Wang |
| author_facet | Wu Zhou Wanwei Jian Xiaoping Cen Lijuan Zhang Hui Guo Zaiyi Liu Changhong Liang Guangyi Wang |
| author_sort | Wu Zhou |
| collection | DOAJ |
| description | Background and PurposeIt 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 MethodsThis 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.ResultsThe 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).ConclusionA deep learning framework based on 3D CNN and deeply supervised net with contrast-enhanced MR could be effective for MVI prediction. |
| first_indexed | 2024-12-18T02:26:23Z |
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| language | English |
| last_indexed | 2024-12-18T02:26:23Z |
| publishDate | 2021-03-01 |
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| series | Frontiers in Oncology |
| spelling | doaj.art-5ced5ace3643470fa0244ee9295d23d72022-12-21T21:24:02ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-03-011110.3389/fonc.2021.588010588010Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural NetworksWu Zhou0Wanwei Jian1Xiaoping Cen2Lijuan Zhang3Hui Guo4Zaiyi Liu5Changhong Liang6Guangyi Wang7School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, ChinaSchool of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, ChinaSchool of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Optometry, Guangzhou Aier Eye Hospital, Jinan University, Guangzhou, ChinaDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaBackground and PurposeIt 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 MethodsThis 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.ResultsThe 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).ConclusionA deep learning framework based on 3D CNN and deeply supervised net with contrast-enhanced MR could be effective for MVI prediction.https://www.frontiersin.org/articles/10.3389/fonc.2021.588010/fullhepatocellular carcinomamicrovascular invasionconvolutional neural networkdeeply supervised networkcontrast-enhanced MR |
| spellingShingle | Wu Zhou Wanwei Jian Xiaoping Cen Lijuan Zhang Hui Guo Zaiyi Liu Changhong Liang Guangyi Wang Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks Frontiers in Oncology hepatocellular carcinoma microvascular invasion convolutional neural network deeply supervised network contrast-enhanced MR |
| title | Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks |
| title_full | Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks |
| title_fullStr | Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks |
| title_full_unstemmed | Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks |
| title_short | Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks |
| title_sort | prediction of microvascular invasion of hepatocellular carcinoma based on contrast enhanced mr and 3d convolutional neural networks |
| topic | hepatocellular carcinoma microvascular invasion convolutional neural network deeply supervised network contrast-enhanced MR |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.588010/full |
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