An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension

Abstract

The hepatic venous pressure gradient (HVPG) is the gold standard for cirrhotic portal hypertension (PHT), but it is invasive and specialized. Alternative non-invasive techniques are needed to assess the hepatic venous pressure gradient (HVPG). Here, we develop an auto-machine-learning CT radiomics HVPG quantitative model (aHVPG), and then we validate the model in internal and external test datasets by the area under the receiver operating characteristic curves (AUCs) for HVPG stages (≥10, ≥12, ≥16, and ≥20 mm Hg) and compare the model with imaging- and serum-based tools. The final aHVPG model achieves AUCs over 0.80 and outperforms other non-invasive tools for assessing HVPG. The model shows performance improvement in identifying the severity of PHT, which may help non-invasive HVPG primary prophylaxis when transjugular HVPG measurements are not available.

Keywords: CHESS; CT; HVPG; cirrhosis; deep learning; machine learning; noninvasive tool; portal hypertension.

Conflict of interest statement

The authors declare no competing interests.

© 2022 The Authors.

Figures

Graphical abstract

Figure 1

Flowchart of study enrollment (A) The segmentation task included 100 patients from center A to center F; 62 of 100 patients were enrolled in training the segmentation network, and 38 patients were used for the test. (B) The aHVPG (radiomics) task included 372 patients from center A to center F for model training and internal testing and 27 from center G for external testing. Segmentation and radiomics tasks are independent. Another 38 healthy participants were enrolled in the healthy control dataset. HVPG, hepatic venous pressure gradient.

Figure 2

Deep learning segmentation framework and radiomics development workflow (A) The organ segmentation framework included two 3D fully convolutional networks (3D FCNs). In stage 1, the first 3D FCN generated the low-resolution segmentation map. In stage 2, the low-resolution feature map was upsampled to the original resolution, concatenated with the inputs, and fed into the higher resolution 3D FCNs to obtain the final segmentation results. (B) Radiomics analysis workflow of aHVPG. CT images and masks obtained from the deep learning network were pre-processed, and 1,218 features each for the liver and spleen were extracted. The tree-based pipeline optimization tool was applied to train a supervised regression model, and the hepatic venous pressure gradient was used as the ground truth. Finally, the model output the quantitative results and would be validated in internal and external test cohorts.

Figure 3

Segmentation accuracy and diagnostic performance of the deep learning network and aHVPG (A) Dice metric, Jaccard coefficient of the deep learning segmentation network for the liver and spleen in the internal test dataset (centers A–E) and external test dataset (center F). (B) Correlation between aHVPG and invasive HVPG. Scatterplot shows agreement between the aHVPG and the invasive HVPG in training and internal test datasets. (C) Receiver operating characteristic curves of the aHVPG for assessing hepatic venous pressure gradient stages, including ≥10, ≥12, ≥16, and ≥20 mm Hg in training (red line) and internal test sets (blue line). AUC, area under the curve.

Figure 4

Receiver operating characteristic curves of aHVPG and the top 3 conventional non-invasive tools (A–D) Top: Receiver operating characteristic curves (ROCs) of aHVPG and the top three conventional non-invasive tools in the training dataset for assessing hepatic venous pressure gradient (HVPG) stages including ≥10, ≥12, ≥16, and ≥20 mm Hg. Bottom: ROCs in the internal test dataset for assessing HVPG stages. AAR, AST to ALT ratio; APRI, AST to platelet ratio index; CSPH, clinically significant portal hypertension.

Figure 5

Receiver operating characteristic curves of the robustness test (A–D) Top: Receiver operating characteristic curves (ROCs) of the original model and 3 times-retrained models for assessing HVPG stages, including ≥10, ≥12, ≥16, and ≥20 mm Hg in the training dataset (DeLong test, p > 0.05). Bottom: ROCs of the robustness test in the test dataset for assessing HVPG stages (DeLong test, p > 0.05).