Gut decisions based on the liver: A radiomics approach to boost colorectal cancer screening
Anna Hinterberger (1, 2), Jonas Bohn (3, 4, 5, 6), Dasha Trofimova (3, 7), Nicolas Knabe (8), Julia Dettling (8), Tobias Norajitra (3, 4, 9), Fabian Isensee (3, 7), Johannes Betge (1, 10, 11, 12), Stefan O. Sch\"onberg (8), Dominik N\"orenberg (8), Sergio Grosu (13)

TL;DR
This study demonstrates that radiomic features extracted from routine liver CT scans can effectively predict colorectal neoplasia, offering a non-invasive, opportunistic screening method that outperforms clinical models.
Contribution
It introduces a novel approach using liver-derived radiomics from standard CT images to predict colorectal neoplasia, highlighting the gut-liver axis as a biomarker source.
Findings
Best radiomics model achieved AUROC of 0.810
Outperformed clinical-only model with AUROC of 0.457
Potential for non-invasive, opportunistic CRC screening
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
