Aligning computational pathology with clinical practice for colorectal cancer.

The pathology report in colorectal cancer (CRC) consists of more than 20 elements defined in guidelines such as the International Collaboration on Cancer Reporting (ICCR) guidelines. Recently, computational tools have been proposed to advance the CRC diagnostic routine, yet most lack clinically validated results and focus on only three report elements. This review gives an overview of the current gaps and will contribute to aligning computational pathology with clinical practice.

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Publication Details

Journal
Open Access CRIS of the University of Bern
Published
2025-11-27
DOI
https://doi.org/10.48620/93014
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
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article

Aligning computational pathology with clinical practice for colorectal cancer.

Open Access CRIS of the University of Bern
AI in cancer detection
article

Aligning computational pathology with clinical practice for colorectal cancer.

article en

Abstract

The pathology report in colorectal cancer (CRC) consists of more than 20 elements defined in guidelines such as the International Collaboration on Cancer Reporting (ICCR) guidelines. Recently, computational tools have been proposed to advance the CRC diagnostic routine, yet most lack clinically validated results and focus on only three report elements. This review gives an overview of the current gaps and will contribute to aligning computational pathology with clinical practice.

Open Access CRIS of the University of Bern
Tissue Genetics (United States) (US)
Partnerships for the goals
Openalex Percentile: Top 20%
AI in cancer detection
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Aligning computational pathology with clinical practice for colorectal cancer. · Open Access CRIS of the University of Bern (2025) | TGRS Research Map | TGRS