Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework for Post-Hepatectomy Liver Failure Prediction

The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). The main questions it aims to answer are: * To investigate the usability of the VAE-MLP framework for explanation of the deep learning model. * To investigate the clinical effectiveness of VAE-MLP framework for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma. In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation (LRP) plots to evaluate the usability of the framework. In the clinical trial the clinicians and radiologists will make the prediction under two different conditions: with model explanation and without model explanation with a washout period of at least 14 days to evaluate the clinical effectiveness of the explanation framework.

Sex

All

Inclusion Criteria:

1. patients with treatment-naive and resectable HCC;
2. performance status Eastern Cooperative Oncology Group (PS) score 0-1.

Exclusion Criteria:

1. liver resection was not performed;
2. pathological diagnosis of non-HCC;
3. failure in liver stiffness measurement defined as the elastography color map was less than 75% filled or interquartile range (IQR)/median \> 30%;
4. immune-active chronic hepatitis indicated by an elevation of alanine aminotransferase (ALT) levels ≥ 2×upper limit of normal (ULN);
5. obstructive jaundice or dilated intrahepatic bile ducts with a diameter of \>3 mm;
6. hypoalbuminemia, hyperbilirubinemia, or coagulopathy not related to the liver.