Prediction of microvascular invasion in hepatocellular carcinoma with magnetic resonance imaging using models combining deep attention mechanism with clinical features.
10.12122/j.issn.1673-4254.2023.05.21
- Author:
Gao GONG
1
;
Shi CAO
1
;
Hui XIAO
1
;
Weiyang FANG
1
;
Yuqing QUE
2
;
Ziwei LIU
3
;
Chaomin CHEN
1
Author Information
1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
2. First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
3. Shunde Hospital Affiliated to Southern Medical University, Foshan 528308, China.
- Publication Type:Journal Article
- Keywords:
attention mechanism;
clinical features;
hepatocellular carcinoma;
magnetic resonance imaging;
microvascular invasion
- MeSH:
Humans;
Carcinoma, Hepatocellular;
Retrospective Studies;
Liver Neoplasms;
Magnetic Resonance Imaging;
Algorithms
- From:
Journal of Southern Medical University
2023;43(5):839-851
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To investigate the consistency and diagnostic performance of magnetic resonance imaging (MRI) for detecting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and the validity of deep learning attention mechanisms and clinical features for MVI grade prediction.
METHODS:This retrospective study was conducted among 158 patients with HCC treated in Shunde Hospital Affiliated to Southern Medical University between January, 2017 and February, 2020. The imaging data and clinical data of the patients were collected to establish single sequence deep learning models and fusion models based on the EfficientNetB0 and attention modules. The imaging data included conventional MRI sequences (T1WI, T2WI, and DWI), enhanced MRI sequences (AP, PP, EP, and HBP) and synthesized MRI sequences (T1mapping-pre and T1mapping-20 min), and the high-risk areas of MVI were visualized using deep learning visualization techniques.
RESULTS:The fusion model based on T1mapping-20min sequence and clinical features outperformed other fusion models with an accuracy of 0.8376, a sensitivity of 0.8378, a specificity of 0.8702, and an AUC of 0.8501 for detecting MVI. The deep fusion models were also capable of displaying the high-risk areas of MVI.
CONCLUSION:The fusion models based on multiple MRI sequences can effectively detect MVI in patients with HCC, demonstrating the validity of deep learning algorithm that combines attention mechanism and clinical features for MVI grade prediction.
