[Hepatology International] A deep learning model with incorporation of microvascular invasion area as a factor in predicting prognosis of hepatocellular carcinoma after R0 hepatectomy

Introduction

This is the inference code and the saved model for "A deep learning model with incorporation of microvascular invasion area as a factor in predicting prognosis of hepatocellular carcinoma after R0 hepatectomy" published in Hepatology International.

The model takes in four input features and predict a MVI survival risk score for each patient. Please refer to the paper for more details.

Dependencies:

Python 3.6+;

pandas;

torch;

numpy;

Usage:

1. Prepare your data following the demo input file "./Input_Patient_Info.cvs".

2. Pay attention to the input file path and run:

python3 mvi_risk_predict.py

3. Obtain the prediction file "./Output_Patient_Info_with_Risk_Score".

Disclaimer:

This tool is for research purpose and not approved for clinical use.

This is not an official Tencent product.

Citation:

Please consider citing our paper in your publications if the project helps your research.

@article{wang2022a,

title={A deep learning model with incorporation of microvascular invasion area as a factor in predicting prognosis of hepatocellular carcinoma after R0 hepatectomy},

author={Kang, Wang and Yanjun, Xiang and Jiangpeng, Yan and Others},

journal={Hepatology International},

year={2022},

publisher={Springer} }