News Release

Deep learning model trained with stage II colorectal cancer whole slide images identifies features associated with risk of recurrence – with higher success rate than clinical prognostic parameters

Peer-Reviewed Publication

PLOS

Deep learning model trained with stage II colorectal cancer whole slide images identifies features associated with risk of recurrence – with higher success rate than clinical prognostic parameters

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The workflow of SurvFinder. Created in Biorender.

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Credit: Zhao Z, et al., 2025, PLOS Medicine, CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

In your coverage, please use this URL to provide access to the freely available paper in PLOS Medicine: https://plos.io/48KLRz7  

Article title: Multiview deep-learning-enabled histopathology for prognostic and therapeutic stratification in stage II colorectal cancer: A retrospective multicenter study

Author countries: China, United States

Funding: see manuscript

Journal

PLOS Medicine

DOI

10.1371/journal.pmed.1004614

Method of Research

Observational study

Subject of Research

People

COI Statement

Competing interests: The authors have declared that no competing interests exist.

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