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Abstract #0240

An Interpretable and Generalizable Deep Learning Model for Non-Invasive Assessment of MVI in HCC Based on Preoperative MRI: A Multi-Center Study

Xue Dong1, Jingxuan Zhang1, Caili Ma2, Huai Yang1, Wei Zhang3, Xibin Jia3, Dawei Yang1, and Zhenghan Yang1
1Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China, Beijing, China, 2Department of Radiology, Beijing Long Fu Hospital, Meishuguan Road 18,East District, Beijing, 100010, China, Beijing, China, 3Beijing University of Technology, 100 Pingleyuan, Chao-Yang District, Beijing 100124, China., Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence

Motivation: MRI-based deep learning has potential for non-invasive preoperative identification of MVI in HCC. However, current models lack generalizability and interpretability.

Goal(s): Develop an interpretable, domain-generalizable deep learning (DL) model for MVI assessment in HCC.

Approach: A DL model using adversarial networks (AD) and different sequence combinations was validated across multiple centers. MVI-related genes were identified in a subset of HCC patients.

Results: The AD-DL model showed optimal performance with an AUC of 0.793 (internal test set), 0.801 (external test set 1), and 0.773 (external test set 2). 198 MVI-related genes were identified, involving Wnt, neuroactive ligand-receptor interaction, and Hippo pathways.

Impact: Incorporating adversarial networks into the model development process may enhance the generalizability of the MVI prediction model, facilitating its practical implementation.

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Keywords