Abstract
Purpose
Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies and the assessment of patient’s prognosis. A deep learning (DL) model was developed to predict the MVI status and grade in HCC patients based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical parameters.
Methods
HCC patients with pathologically confirmed MVI status from January to December 2016 were enrolled and preoperative DCE-MRI of these patients were collected in this study. Then they were randomly divided into the training and testing cohorts. A DL model with eight conventional neural network (CNN) branches for eight MRI sequences was built to predict the presence of MVI, and further combined with clinical parameters for better prediction.
Results
Among 601 HCC patients, 376 patients were pathologically MVI absent, and 225 patients were MVI present. To predict the presence of MVI, the DL model based only on images achieved an area under curve (AUC) of 0.915 in the testing cohort as compared to the radiomics model with an AUC of 0.731. The DL combined with clinical parameters (DLC) model yielded the best predictive performance with an AUC of 0.931. For the MVI-grade stratification, the DLC models achieved an overall accuracy of 0.793. Survival analysis demonstrated that the patients with DLC-predicted MVI status were associated with the poor overall survival (OS) and recurrence-free survival (RFS). Further investigation showed that hepatectomy with the wide resection margin contributes to better OS and RFS in the DLC-predicted MVI present patients.
Conclusion
The proposed DLC model can provide a non-invasive approach to evaluate MVI before surgery, which can help surgeons make decisions of surgical strategies and assess patient’s prognosis.
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Data availability
Data are available in the Article and Supplementary Information. All other data can be provided upon reasonable request to the corresponding authors.
Code availability
The code of the proposed method can be provided upon reasonable request to the corresponding authors.
Abbreviations
- MVI:
-
Microvascular invasion
- HCC:
-
Hepatocellular carcinoma
- DL:
-
Deep learning
- DCE-MRI:
-
Dynamic contrast-enhanced magnetic resonance imaging
- CNN:
-
Conventional neural network
- AUC:
-
Area under curve
- DLC:
-
Deep learning combined with clinical parameters
- OS:
-
Overall survival
- RFS:
-
Recurrence-free survival
- RFA:
-
Radiofrequency ablation
- AFP:
-
Alpha fetoprotein
- PIVKA-II:
-
Prothrombin induced by vitamin K absence-II
- CT:
-
Computed tomography
- MRI:
-
Magnetic resonance imaging
- PLR:
-
Platelet-lymphocyte ratio
- NLR:
-
Neutrophil-lymphocyte ratio
- LMR:
-
Lymphocyte-to-monocyte ratio
- PNI:
-
Prognostic nutritional index
- APRI:
-
Aspartate aminotransferase-to-platelet ratio index
- ANRI:
-
Aspartate aminotransferase-to-neutrophil ratio index
- ALR:
-
Aspartate aminotransferase-lymphocyte ratio
- T2WI:
-
Turbo spin-echo T2-weighted
- DWI:
-
Diffusion-weighted imaging
- ADC:
-
Apparent diffusion coefficient
- 3D:
-
Three-dimensional
- VOI:
-
Volume of interest
- FC:
-
Fully connected
- AIC:
-
Akaike information criterion
- ROC:
-
Receiver operating characteristic
- INR:
-
International normalized ratio
- SD:
-
Standard deviation
- IQR:
-
Interquartile range
- CI:
-
Confidence interval
- NCCN:
-
National Comprehensive Cancer Network
- AASLD:
-
American Association for the Study of Liver Diseases
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Acknowledgement
We acknowledged the Department of Pathology of Zhongshan Hospital of Fudan University for assistance with pathologic diagnosis.
Funding
This work was supported by National Key Research and Development Program of China (Grant 82090054) and National Natural Science Foundation of China (Grant 81572367 and 81772556) to Xiaoying Wang, Shanghai Science and Technology Innovation Action Plan (Grant 19511121302) to Manning Wang, National Key Research and Development Program of China (Grant 2017YFC0108804) to Shengxiang Rao and Shanghai Sailing Program (Grant 19YF1408100) to Wentao Wang.
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WXY, WMN, RSX: Guarantors of integrity of entire study; WXY: Concepts and design; All authors: Administrative support, Data acquisition (including radiological and clinical data); SDJ, WWT, RSX, WXY: Radiological images delineation; WYY, WMN: Development of methodology; SDJ, WYY, LMZ: Statistical analysis; SDJ, WYY: Manuscript drafting or revision.
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Song, D., Wang, Y., Wang, W. et al. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters. J Cancer Res Clin Oncol 147, 3757–3767 (2021). https://doi.org/10.1007/s00432-021-03617-3
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DOI: https://doi.org/10.1007/s00432-021-03617-3