Hepatocellular Carcinoma Risk Stratification for Cirrhosis Patients: Integrating Radiomics and Deep Learning Computed Tomography Signatures of the Liver and Spleen into a Clinical Model.
Authors
Affiliations (16)
Affiliations (16)
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University; Guangdong Provincial Key Laboratory for Prevention and Control of Major Liver Diseases; Guangdong Provincial Clinical Research Center for Viral Hepatitis; Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Guangzhou, Guangdong, China.
- International Cooperation Laboratory on Signal Transduction, National Center for Liver Cancer, Eastern Hepatobiliary Surgery Institute/Hospital, Shanghai, China.
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
- Hepatology Department, Ningbo Hwamei Hospital, University of Chinese Academy of Sciences, Ningbo, Zhejiang, China.
- The First Hospital of Jilin University, Changchun, Jilin, China.
- Xuzhou Infectious Diseases Hospital, Xuzhou, Jiangsu, China.
- Department of Hepatology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China.
- The Department of Infectious Disease, The First People's Hospital of Foshan, Foshan, Guangdong, China.
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Department of Hepatobiliary Surgery, Clinical Medical College, Yangzhou University, Yangzhou, Jiangsu, China.
- Chifeng Clinical Medical School of Inner Mongolia Medical University, Chifeng, Inner Mongolia, China.
- Department of Gastroenterology, Changzheng Hospital, Naval Medical University, Shanghai, China.
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
- Department of Hepatopancreatobiliary Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
- Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University); Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, Jiangsu, China.
Abstract
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. 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. 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%]). The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.