Machine learning predicts liver cancer risk from routine clinical data: a large population-based multicentric study
Abstract
Abstract Background and aims Hepatocellular carcinoma (HCC) is a highly fatal tumor, for which early detection and risk stratification is crucial, yet remains challenging. We aimed to develop an interpretable machine-learning framework for HCC risk stratification based on routinely collected clinical data. Methods We leverage data obtained from over 900,000 individuals and 983 cases of HCC across two large-scale population-based cohorts: the UK Biobank study and the “All Of Us Research Program”. For all of these patients, clinical data from timepoints years before diagnosis of HCC was available. We integrate data modalities including demographics, electronic health records, lifestyle, routine blood tests, genomics and metabolomics to offer a unique, multi-modal perspective on HCC risk. Results Our random-forest-based model significantly outperforms all publicly available state-of-the-art risk-scores, with an AUROC of 0.88 both for internal and external test sets. We demonstrate robustness of our model across ethnic subgroups, a major advance over previous models with variable performance by ethnicity. Further, we perform extensive feature-importance analysis, showcasing our approach as an interpretable framework. We provide all model weights and an open-source web calculator to facili-tate further validation of our model. Conclusion Our study presents a robust and interpretable machine-learning framework for HCC risk stratification, which offers the potential to improve early detection and could ultimately reduce disease burden through targeted interventions. Lay summary Finding liver cancer early is crucial for successful treatment. Therefore, screening with abdominal ultra-sound can be performed. However, it is not clear who should receive ultrasound screening, as with the current standard of screening only patients with liver cirrhosis, a severe liver disease, many patients are diagnosed with liver cancer in late stages. Therefore, we trained a machine learning model, acting like many decision trees at the same time, to detect patients with high risk of liver cancer by looking at patterns of almost 1000 cases of liver cancer in a population of 900.000 individuals. In a separate set of patients, which the model has not seen during training, our model worked better than all available models. Additionally, we investigated 1. how the model comes to its prediction, 2. whether it works in males and females alike and 3. which data is most relevant for the model. Like this, our model can help sort patients into categories like “high-risk”, “medium-risk” and “low-risk”, via which screening strategies can then be decided, to help improve early detection of liver cancer.
Authors 15
-
Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany;
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
-
Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany;
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
-
Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany;
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
-
Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; StratifAI GmbH, Dresden, Germany;
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
StratifAI GmbH, Dresden, Germany
-
Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany;
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
-
Else Kröner Fresenius Center for Digital Health
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany;
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
-
Affiliation as printed
Gastroenterology and Hepatology Unit, Division of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand;
Gastroenterology and Hepatology Unit, Division of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
-
Inserm · Université Paris-Est Créteil · Centre Hospitalier Universitaire Henri-Mondor · Assistance Publique – Hôpitaux de Paris · Hôpitaux Universitaires Henri-Mondor · Hôpital Albert-Chenevier · Institut Mondor de Recherche Biomédicale
Affiliation as printed
Universite Paris Est Creteil, INSERM, IMRB, F-94010, Creteil, France; Assistance Publique-Hopitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Pathology, Creteil, France; Inserm, U955, Team 18, Creteil, France; European Refe;
Assistance Publique-Hopitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Pathology, Creteil, France
European Refe
Inserm, U955, Team 18, Creteil, France
Universite Paris Est Creteil, INSERM, IMRB, F-94010, Creteil, France
-
Charité - Universitätsmedizin Berlin
Affiliation as printed
Department of Hepatology and Gastroenterology, Charite - Universitaetsmedizin Berlin, Campus Virchow-Klinikum and Campus Charite Mitte, Berlin, Germany;
Department of Hepatology and Gastroenterology, Charite - Universitaetsmedizin Berlin, Campus Virchow-Klinikum and Campus Charite Mitte, Berlin, Germany
-
Düsseldorf University Hospital
Affiliation as printed
Department for Gastroenterology, Hepatology and Infectiology, University Hospital Duesseldorf, Germany;
Department for Gastroenterology, Hepatology and Infectiology, University Hospital Duesseldorf, Germany
-
Affiliation as printed
Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Germany;
Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Germany
-
Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany;
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
-
Universitätsklinikum Aachen · Center for Regenerative Therapies Dresden · Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden · RWTH Aachen University
Affiliation as printed
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Center for Regenerative Therapies Dresden (CRTD), Technische Universitaet (TU),;
Center for Regenerative Therapies Dresden (CRTD), Technische Universitaet (TU),
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
-
Heidelberg University · University Hospital Heidelberg · National Center for Tumor Diseases · Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Heidelberg, Germany
Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Heidelberg, Germany
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
-
Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany;
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
Cited by 4 stored of 4
4 results
No patents citing this paper on Lens.org (checked 2026-10-06).