Gut decisions based on the liver: prediction of colorectal neoplasia using AI-based liver analysis of routine CT scans.
Authors
Affiliations (21)
Affiliations (21)
- German Cancer Research Center (DKFZ) Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany.
- Junior Clinical Cooperation Unit Translational Molecular Imaging in Oncologic Therapy Monitoring (E310), German Cancer Research Center, Heidelberg, Germany.
- Institute for Diagnostic and Interventional Radiology, Technical University of Munich (TUM) School of Medicine and Health, Technical University of Munich (TUM) University Hospital Kinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
- The National Center for Tumor Diseases (NCT Heidelberg), Heidelberg, Germany.
- Helmholtz Imaging, Heidelberg, Germany.
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
- Pattern Analysis and Learning Group, Heidelberg University Hospital, Heidelberg, Germany.
- Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Mannheim, Germany.
- Junior Clinical Cooperation Unit Translational Gastrointestinal Oncology and Preclinical Models, German Cancer Research Center, Heidelberg, Germany.
- German Cancer Consortium, DKTK, Heidelberg, Germany.
- Department of Radiology, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany.
- Division of Personalized Medical Oncology (A420), German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Department of Personalized Oncology, University Hospital Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden University of Technology (TUD) Dresden University of Technology, Dresden, Dresden, Germany.
- Department of Medicine I, University Hospital Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
- Faculty of Medicine, University of Heidelberg, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
Abstract
Non-invasive colorectal cancer (CRC) screening offers an important opportunity to increase colonoscopy participation and reduce mortality. This study evaluates the potential of the gut-liver axis to predict colorectal neoplasia using artificial intelligence (AI)-based analysis of the liver in routine CT images as an opportunistic screening approach. In this retrospective study, data from 1,997 patients were analyzed, including 1,189 without neoplasia and 808 with colorectal neoplasia (423 adenomas, 385 CRC). Radiomic features were extracted from three-dimensional liver segmentations, and the dataset was split into training (n = 1,397) and test (n = 600) cohorts. Five machine learning models were trained using five-fold cross-validation on the 20 most informative features. The best-performing radiomics-based XGBoost model achieved a test AUROC of 0.810 (95% CI: 0.767-0.837), outperforming a clinical-only model (AUROC: 0.457). After threshold optimization, sensitivity reached 74.1% and specificity 72.3% for detecting colorectal neoplasia. Subclassification between CRC and adenoma was less accurate (AUROC: 0.674). These findings demonstrate that AI-based liver analysis from routine CT scans can predict colorectal neoplasia, supporting its potential as an accessible adjunct to CRC screening and highlighting the gut-liver axis as a novel biomarker source.