Clinical prediction of microvascular invasion in hepatocellular carcinoma using an MRI-Based graph convolutional network model integrated with nomogram.

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Abstract

Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram.We retrospectively collected 182 HCC patients confirmed histopathologically, all of them performed enhanced MRI before surgery. The patients were randomly divided into training and validation groups. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), respectively. After removing redundant features, the graph structure by constructing the distance matrix with the feature matrix was built. Screening the superior phases and acquired GCN Score (GS). Finally, combining clinical, radiological and GS established the predicting nomogram.27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoral artery (p = 0.007) was an independent predictor of MVI. GCN model with GLCM-GLRLM features exhibited AUCs of the training group was 0.532, 0.690, and 0.885 and the validation group was 0.583, 0.580, and 0.854 for AP, PVP, and DP, respectively. DP was selected to develop final model and got GS. Combining GS with diameter, corona enhancement, mosaic architecture, and intratumoral artery constructed a nomogram which showed a C-index of 0.884 (95% CI: 0.829-0.927).The GCN model based on DP has a high predictive ability. A nomogram combining GS, clinical and radiological characteristics can be a simple and effective guiding tool for selecting HCC treatment options.GCN based on MRI could predict MVI on HCC; GCN combining with nomogram analysis to diagnose MVI preoperatively may influence the clinical decision.© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: [email protected].