Gut decisions based on the liver: A radiomics approach to boost colorectal cancer screening
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912673568915456 |
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| author | Hinterberger, Anna Bohn, Jonas Trofimova, Dasha Knabe, Nicolas Dettling, Julia Norajitra, Tobias Isensee, Fabian Betge, Johannes Schönberg, Stefan O. Nörenberg, Dominik Grosu, Sergio Loges, Sonja Floca, Ralf Kather, Jakob Nikolas Maier-Hein, Klaus Grawe, Freba |
| author_facet | Hinterberger, Anna Bohn, Jonas Trofimova, Dasha Knabe, Nicolas Dettling, Julia Norajitra, Tobias Isensee, Fabian Betge, Johannes Schönberg, Stefan O. Nörenberg, Dominik Grosu, Sergio Loges, Sonja Floca, Ralf Kather, Jakob Nikolas Maier-Hein, Klaus Grawe, Freba |
| contents | Non-invasive colorectal cancer (CRC) screening represents a key opportunity to improve colonoscopy participation rates and reduce CRC mortality. This study explores the potential of the gut-liver axis for predicting colorectal neoplasia through liver-derived radiomic features extracted from routine CT images as a novel opportunistic screening approach. In this retrospective study, we analyzed data from 1,997 patients who underwent colonoscopy and abdominal CT. Patients either had no colorectal neoplasia (n=1,189) or colorectal neoplasia (n_total=808; adenomas n=423, CRC n=385). Radiomics features were extracted from 3D liver segmentations using the Radiomics Processing ToolKit (RPTK), which performed feature extraction, filtering, and classification. The dataset was split into training (n=1,397) and test (n=600) cohorts. Five machine learning models were trained with 5-fold cross-validation on the 20 most informative features, and the best model ensemble was selected based on the validation AUROC. The best radiomics-based XGBoost model achieved a test AUROC of 0.810, clearly outperforming the best clinical-only model (test AUROC: 0.457). Subclassification between colorectal cancer and adenoma showed lower accuracy (test AUROC: 0.674). Our findings establish proof-of-concept that liver-derived radiomics from routine abdominal CT can predict colorectal neoplasia. Beyond offering a pragmatic, widely accessible adjunct to CRC screening, this approach highlights the gut-liver axis as a novel biomarker source for opportunistic screening and sparks new mechanistic hypotheses for future translational research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23687 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gut decisions based on the liver: A radiomics approach to boost colorectal cancer screening Hinterberger, Anna Bohn, Jonas Trofimova, Dasha Knabe, Nicolas Dettling, Julia Norajitra, Tobias Isensee, Fabian Betge, Johannes Schönberg, Stefan O. Nörenberg, Dominik Grosu, Sergio Loges, Sonja Floca, Ralf Kather, Jakob Nikolas Maier-Hein, Klaus Grawe, Freba Quantitative Methods Image and Video Processing Non-invasive colorectal cancer (CRC) screening represents a key opportunity to improve colonoscopy participation rates and reduce CRC mortality. This study explores the potential of the gut-liver axis for predicting colorectal neoplasia through liver-derived radiomic features extracted from routine CT images as a novel opportunistic screening approach. In this retrospective study, we analyzed data from 1,997 patients who underwent colonoscopy and abdominal CT. Patients either had no colorectal neoplasia (n=1,189) or colorectal neoplasia (n_total=808; adenomas n=423, CRC n=385). Radiomics features were extracted from 3D liver segmentations using the Radiomics Processing ToolKit (RPTK), which performed feature extraction, filtering, and classification. The dataset was split into training (n=1,397) and test (n=600) cohorts. Five machine learning models were trained with 5-fold cross-validation on the 20 most informative features, and the best model ensemble was selected based on the validation AUROC. The best radiomics-based XGBoost model achieved a test AUROC of 0.810, clearly outperforming the best clinical-only model (test AUROC: 0.457). Subclassification between colorectal cancer and adenoma showed lower accuracy (test AUROC: 0.674). Our findings establish proof-of-concept that liver-derived radiomics from routine abdominal CT can predict colorectal neoplasia. Beyond offering a pragmatic, widely accessible adjunct to CRC screening, this approach highlights the gut-liver axis as a novel biomarker source for opportunistic screening and sparks new mechanistic hypotheses for future translational research. |
| title | Gut decisions based on the liver: A radiomics approach to boost colorectal cancer screening |
| topic | Quantitative Methods Image and Video Processing |
| url | https://arxiv.org/abs/2510.23687 |