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

Abstract

Objectives: Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram.

Methods: 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.

Results: 27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoural artery (P = 0.007) was an independent predictor of MVI. GCN model with grey-level cooccurrence matrix-grey-level run length matrix features exhibited area under the curves 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 intratumoural artery constructed a nomogram which showed a C-index of 0.884 (95% CI: 0.829-0.927).

Conclusions: 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.

Advances in knowledge: GCN based on MRI could predict MVI on HCC.

Keywords: deep learning; graph convolutional network; hepatocellular carcinoma; microvascular invasion; radiomics.

Figure 1.

The patient recruitment pathway and the inclusion and exclusion criteria. Note: HCC = hepatocellular carcinoma; ICC = intrahepatic cholangiocarcinoma; MVI = microvascular invasion.

Figure 2.

(A) The HCC lesion (blue arrow) in the right lobe of the liver is shown on contrast-enhanced MR image, presenting a “mosaic architecture” on T2WI. (B) HCC lesion (blue arrow) in the left lobe of the liver on contrast-enhanced MR image and “intratumoral artery” inside the lesion (red arrow) on AP image. (C) HCC lesion (blue arrow) in the right lobe of the liver is shown on contrast-enhanced MR image. The lesion appears as nonrim AP hyperenhancement on the AP image, and “corona enhancement” is shown around the lesion (white arrow). (D) HCC lesion (blue arrow) in the right lobe of the liver is demonstrated on contrast-enhanced MR image, the regression of lesion enhancement on PVP image is shown as “nonperipheral washout,” and the edge of the visible lesion shows an “enhancing capsule.”

Figure 3.

Flowchart of the model development process in this study.

Figure 4.

The AUC of each GCN combination model is based on the AP, PVP, and DP images of the training and validation groups, respectively.

Figure 5.

(A) Nomogram predicting the probability of MVI in HCC patients. Total points were calculated by adding up the points of each variable on the point scale and indicating the probability of MVI presence according to the bottom scales. GS, GCN-Score. (B) DCA is for the combined nomogram. The grey or black line hypothesizes that all patients were MVI positive or negative, respectively. The red line represents the net benefit of the nomogram at different threshold probabilities. (C) The calibration curve for predicting the presence of MVI. The combined nomogram-predicted MVI presence is plotted on the X-axis, and the actual MVI presence is plotted on the Y-axis. A plot along the 45 ° line would indicate a perfect calibration model.