Journal Article

DKFZ-2026-01484

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Gut decisions based on the liver: prediction of colorectal neoplasia using AI-based liver analysis of routine CT scans.

Hinterberger, A. (First author)DKFZ* ; Bohn, J. (First author)DKFZ* ; Trofimova, D.DKFZ* ; Knabe, N. ; Dettling, J. ; Norajitra, T.DKFZ* ; Isensee, F.DKFZ* ; Betge, J.DKFZ* ; Schönberg, S. O. ; Nörenberg, D. ; Grosu, S. ; Loges, S.DKFZ* ; Floca, R.DKFZ* ; Kather, J. N.DKFZ* ; Maier-Hein, K. (Last author)DKFZ* ; Grawe, F. (Last author)DKFZ*

2026
Frontiers Media Lausanne

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.

Keyword(s): CRC-screening ; RPTK ; colorectal neoplasia ; gut-liver-axis ; prevention

Note: #EA:E310#EA:E230#LA:E230#LA:E310# / #DKTKZFB26# / #NCTZFB26#

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Contributing Institute(s):

1. NWG KKE Translationale Molekulare Bildgebung im Onkologischen Therapiemonitoring (E310)
2. Medizinische Bildverarbeitung (E230)
3. NWG-KKE Translationale Gastrointestinale Onkologie und präklinische Modelle (B440)
4. DKTK HD zentral (HD01)
5. Personalisierte Medizinische Onkologie (A420)
6. Koordinierungsstelle NCT Heidelberg (HD02)

Research Program(s):

1. 315 - Bildgebung und Radioonkologie (POF4-315) (POF4-315)

Appears in the scientific report 2026

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Database coverage:
; ; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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