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.
Authors
- Jacob Hanimann (ORCID: https://orcid.org/0000-0003-3156-0441)
- Heather Dawson (ORCID: https://orcid.org/0000-0001-7527-336X)
- Mauro Gwerder
- Philipp Zens
- Alessandro Lugli (ORCID: https://orcid.org/0000-0002-9264-5185)
- Elias Baumann (ORCID: https://orcid.org/0000-0003-4655-4925)
- A. Frei (ORCID: https://orcid.org/0009-0006-0256-9470)
- Amjad Khan (ORCID: https://orcid.org/0000-0001-8281-6908)
- Inti Zlobec (ORCID: https://orcid.org/0000-0001-6741-3000)
- Rina Mehmeti
- Javier García-Baroja
- José F. Carreño-Martínez
Institutions
- University of Bern (CH)
- Bern University of Applied Sciences (CH)
Publication Details
- Journal
- npj Precision Oncology
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1038/s41698-025-01119-w
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48