Hepatocellular carcinoma risk stratification for cirrhosis patients: Integrating radiomics and deep learning computed tomography signatures of the liver and spleen into a clinical model

Background and Aims

Given the high burden of hepatocellular carcinoma (HCC), risk stratification in patients with cirrhosis is critical but remains inadequate. In this study, we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography (CT) images into the established age-male-ALBI-platelet (aMAP) clinical model.

Methods

Patients were enrolled between 2018 and 2023 from a Chinese multicenter, prospective, observational cirrhosis cohort, all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment. The aMAP clinical score was calculated, and radiomic (PyRadiomics) and deep learning (ResNet-18) features were extracted from liver and spleen regions of interest. Feature selection was performed using the least absolute shrinkage and selection operator.

Results

Among 2,411 patients (median follow-up: 42.7 months [IQR: 32.9–54.1]), 118 developed HCC (three-year cumulative incidence: 3.59%). Chronic hepatitis B virus infection was the main etiology, accounting for 91.5% of cases. The aMAP-CT model, which incorporates CT signatures, significantly outperformed existing models (area under the receiver-operating characteristic curve: 0.809–0.869 in three cohorts). It stratified patients into high-risk (three-year HCC incidence: 26.3%) and low-risk (1.7%) groups. Stepwise application (aMAP → aMAP-CT) further refined stratification (three-year incidences: 1.8% [93.0% of the cohort] vs. 27.2% [7.0%]).

Conclusions

Incorporating liver and spleen image signatures into the aMAP score using AI techniques offers a more accessible and superior approach for individualized HCC risk prediction in cirrhosis patients. The stepwise application of the aMAP and aMAP-CT scores enhances enrichment strategies, effectively identifying 7% of cirrhosis patients at very high risk for HCC. This method provides a powerful tool for guiding individualized HCC surveillance, potentially improving early detection and patient outcomes.

Full text

https://www.xiahepublishing.com/2310-8819/JCTH-2025-00091

The study was recently published in the Journal of Clinical and Translational Hepatology.

The Journal of Clinical and Translational Hepatology (JCTH) is owned by the Second Affiliated Hospital of Chongqing Medical University and published by XIA & HE Publishing Inc. JCTH publishes high quality, peer reviewed studies in the translational and clinical human health sciences of liver diseases. JCTH has established high standards for publication of original research, which are characterized by a study’s novelty, quality, and ethical conduct in the scientific process as well as in the communication of the research findings. Each issue includes articles by leading authorities on topics in hepatology that are germane to the most current challenges in the field. Special features include reports on the latest advances in drug development and technology that are relevant to liver diseases. Regular features of JCTH also include editorials, correspondences and invited commentaries on rapidly progressing areas in hepatology. All articles published by JCTH, both solicited and unsolicited, must pass our rigorous peer review process.

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