Universität zu Köln

Barroso, Vincenzo Mitchell ORCID: 0009-0006-7961-089X, Weng, Zhilong ORCID: 0009-0002-0321-9406, Glamann, Lennert, Bauer, Marcus, Wickenhauser, Claudia, Zander, Thomas ORCID: 0000-0002-4266-6818, Büttner, Reinhard ORCID: 0000-0001-8806-4786, Quaas, Alexander ORCID: 0000-0002-3537-6011 and Tolkach, Yuri ORCID: 0000-0001-5239-2841 (2025). Artificial Intelligence–Based Single-Cell Analysis as a Next-Generation Histologic Grading Approach in Colorectal Cancer: Prognostic Role and Tumor Biology Assessment. Modern Pathology, 38 (7). pp. 1-11. Elsevier. ISSN 08933952

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Abstract

[Artikel-Nr.: 100771] The management of colorectal carcinoma (CRC) relies on pathological interpretation. Digital pathology approaches allow for development of new potent artificial intelligence–based prognostic parameters. The study aimed to develop an artificial intelligence–based image analysis platform allowing fully automatized, quantitative, and explainable tumor microenvironment analysis and extraction of prognostic information from hematoxylin and eosin–stained whole-slide images of CRC patients. Three well--characterized, multi-institutional patient cohorts were included (patient n = 1438, whole-slide image n > 2400). The developed image analysis platform implements quality control and established algorithms to segment tissue and detect cell types. It enabled systematic analysis of immune infiltrate, assessing its prognostic relevance, intratumoral heterogeneity, and biological concepts across multiple survival end points. Analyzing single-cell types and their combinations reveals independent, prognostic parameters, highlighting significant intratumoral heterogeneity, especially in the biopsy setting, which must be accounted for. A key morphologic concept related to tumor control by the immune system is described, resulting in a capable, independent prognostic parameter (tumor “out of control”). Our findings have direct clinical implications and can be used as a foundation for updating the existing CRC grading systems.

Item Type:	Article
Creators:	Creators Email ORCID ORCID Put Code Barroso, Vincenzo Mitchell UNSPECIFIED https://orcid.org/0009-0006-7961-089X UNSPECIFIED Weng, Zhilong UNSPECIFIED https://orcid.org/0009-0002-0321-9406 UNSPECIFIED Glamann, Lennert UNSPECIFIED UNSPECIFIED UNSPECIFIED Bauer, Marcus UNSPECIFIED UNSPECIFIED UNSPECIFIED Wickenhauser, Claudia UNSPECIFIED UNSPECIFIED UNSPECIFIED Zander, Thomas UNSPECIFIED https://orcid.org/0000-0002-4266-6818 UNSPECIFIED Büttner, Reinhard UNSPECIFIED https://orcid.org/0000-0001-8806-4786 UNSPECIFIED Quaas, Alexander UNSPECIFIED https://orcid.org/0000-0002-3537-6011 UNSPECIFIED Tolkach, Yuri UNSPECIFIED https://orcid.org/0000-0001-5239-2841 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-803505
Identification Number:	10.1016/j.modpat.2025.100771
Journal or Publication Title:	Modern Pathology
Volume:	38
Number:	7
Page Range:	pp. 1-11
Number of Pages:	11
Date:	July 2025
Publisher:	Elsevier
ISSN:	08933952
Language:	English
Faculty:	Faculty of Medicine
Divisions:	Faculty of Medicine > Innere Medizin > Klinik I für Innere Medizin - Hämatologie und Onkologie Faculty of Medicine > Pathologie und Neuropathologie > Institut für Pathologie
Subjects:	Medical sciences Medicine
Uncontrolled Keywords:	Keywords Language artificial intelligence ; colorectal cancer ; grading ; hematoxylin and eosin ; prognosis ; single-cell English
['eprint_fieldname_oa_funders' not defined]:	Publikationsfonds UzK
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/80350