Status:
RECRUITING
Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework for Post-Hepatectomy Liver Failure Prediction
Lead Sponsor:
Maastricht University
Collaborating Sponsors:
First Affiliated Hospital, Sun Yat-Sen University
Conditions:
Post-hepatectomy Liver Failure
Hepatocellular Carcinoma
Eligibility:
All Genders
Brief Summary
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 layerwi...
Detailed Description
Post-hepatectomy liver failure (PHLF) is a severe complication after liver resection. It is important to develop an interpretable model for predicting PHLF in order to facilitate effective collaborati...
Eligibility Criteria
Inclusion
- patients with treatment-naive and resectable HCC;
- performance status Eastern Cooperative Oncology Group (PS) score 0-1.
Exclusion
- liver resection was not performed;
- pathological diagnosis of non-HCC;
- failure in liver stiffness measurement defined as the elastography color map was less than 75% filled or interquartile range (IQR)/median \> 30%;
- immune-active chronic hepatitis indicated by an elevation of alanine aminotransferase (ALT) levels ≥ 2×upper limit of normal (ULN);
- obstructive jaundice or dilated intrahepatic bile ducts with a diameter of \>3 mm;
- hypoalbuminemia, hyperbilirubinemia, or coagulopathy not related to the liver.
Key Trial Info
Start Date :
December 10 2023
Trial Type :
OBSERVATIONAL
Allocation :
ESTIMATED
End Date :
March 15 2024
Estimated Enrollment :
80 Patients enrolled
Trial Details
Trial ID
NCT06031818
Start Date
December 10 2023
End Date
March 15 2024
Last Update
February 6 2024
Active Locations (1)
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1
The First Affiliated Hospital of Sun Yat-Sen University
Guangzhou, Guangdong, China, 510000