AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B

Abstract

Background & aims

Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by incorporating imaging biomarkers derived from abdominal computed tomography (CT) images along with clinical variables.

Methods

An AI prediction model employing a gradient-boosting machine algorithm was developed utilizing imaging biomarkers extracted by DeepFore, a deep learning-based CT auto-segmentation software. The derivation cohort (n=5,585) was randomly divided into the training and internal validation sets at a 3:1 ratio. The external validation cohort included 2,883 patients. Six imaging biomarkers (i.e., abdominal visceral fat–total fat volume ratio, total fat–trunk volume ratio, spleen, and liver volume; liver–spleen Hounsfield unit [HU] ratio; and muscle HU) and eight clinical variables were selected as the main variables of our model, PLAN-B-DF.

Results

In the internal validation set (median follow-up duration=7.4 years), PLAN-B-DF demonstrated an excellent predictive performance with a c-index of 0.91 and good calibration function (P=0.78 by the Hosmer-Lemeshow test). In the external validation cohort (median follow-up duration=4.6 years), PLAN-B-DF showed a significantly better discrimination function compared to previous models including PLAN-B, PAGE-B, modified PAGE-B, and CU-HCC (c-index, 0.89 vs. 0.65—0.78; all P<0.001) and maintained a good calibration function (P=0.42 by the Hosmer-Lemeshow test). When patients were classified into four groups according to the risk probability calculated by PLAN-B-DF, the 10-year cumulative HCC incidence was 0.0%, 0.4%, 16.0%, and 46.2% in the minimal-, low-, intermediate-, and high-risk groups, respectively.

Conclusion

This AI prediction model, integrating deep learning-based auto-segmentation of CT images, offers improved performance in predicting HCC risk among patients with CHB compared to previous models.

Impact and implications

The AI-driven HCC prediction model (PLAN-B-DF), employing an automated CT segmentation algorithm, demonstrates a significant improvement in predictive accuracy and risk stratification among patients with CHB. Using a gradient-boosting algorithm and CT metrics such as visceral fat volume and myosteatosis, PLAN-B-DF outperforms previous models based solely on clinical and demographic data. This model not only shows a higher c-index compared to previous models, but also effectively classifies CHB patients into different risk groups. This model uses machine learning to analyze the complex relationships among various risk factors contributing to HCC occurrence, thereby offering more personalized surveillance for CHB patients.