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Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

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Quantification of Colonic Stem Cell Mutations
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Automating tumor-stroma ratio quantification in colon cancer patients from the UNITED study.

F Heilijgers1, M Polack1, A G H Roodvoets2

  • 1Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands.

ESMO Open
|December 30, 2025
PubMed
Summary

An artificial intelligence tool accurately quantifies tumor-stroma ratio (TSR) in colon cancer, identifying patients with worse outcomes. This automated TSR scoring shows prognostic value and may predict response to adjuvant chemotherapy (ACT).

Keywords:
artificial intelligencecolon cancerdisease-free survivalpathologytumor microenvironmenttumor–stroma ratio

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Area of Science:

  • Digital pathology
  • Artificial intelligence in oncology
  • Cancer biomarkers

Background:

  • Colon cancer is a major cause of cancer mortality globally.
  • Tumor-stroma ratio (TSR) is a prognostic indicator in colon cancer, with high stroma correlating to poorer outcomes.
  • Automated scoring of TSR aligns with advancements in digital pathology.

Purpose of the Study:

  • To validate a fully automated artificial intelligence (AI)-based algorithm for tumor-stroma ratio (TSR) quantification in colon cancer.
  • To establish an optimal region of interest and cut-off value for automated TSR scoring.
  • To assess the prognostic and potential predictive value of automated TSR for disease-free survival (DFS) and overall survival (OS).

Main Methods:

  • The AI algorithm was validated on whole-slide images from 853 stage II and III colon cancer patients in the UNITED cohort.
  • The algorithm segmented 11 tissue classes to calculate TSR (stroma / (stroma + epithelial tumor)).
  • Receiver operating characteristic analyses determined an optimal automated cut-off of 77% for DFS and OS, using a 1-mm region of interest.

Main Results:

  • Stroma-high colon cancer patients exhibited significantly worse DFS (3-year DFS 71% vs. 82%, P < 0.001) and OS (5-year OS 69% vs. 85%, P = 0.002).
  • The prognostic impact of high stroma remained significant in multivariate analysis (P = 0.016) and was consistent across stages II and III.
  • Worse DFS was observed in stroma-high patients receiving adjuvant chemotherapy (ACT), suggesting potential predictive value.

Conclusions:

  • A clinically applicable, automated tool for TSR quantification in colon cancer was validated.
  • Automated TSR is an independent prognosticator for DFS and OS in colon cancer.
  • The findings support integrating automated TSR into digital pathology workflows for improved clinical decision-making and treatment planning.