Related Experiment Video
Updated: Jan 28, 2026
05:33
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
846
Integrating artificial intelligence (AI) into colorectal cancer reporting
Konstantin Bräutigam1, Ann-Marie Baker1, Viktor H Koelzer2,3
1Centre for Evolution and Cancer, Institute of Cancer Research, London, UK.
The Journal of Pathology
|January 27, 2026
Summary
Artificial intelligence (AI) and deep learning (DL) are revolutionizing colorectal cancer (CRC) research. AI tools can standardize pathology reports and identify new prognostic biomarkers, improving patient outcomes.
Area of Science:
- Oncology
- Digital Pathology
- Artificial Intelligence
Background:
- Colorectal cancer (CRC) is a leading cause of cancer mortality worldwide.
- Histopathology is crucial for CRC diagnosis and prognosis, but reporting variability persists.
- Artificial intelligence (AI) and deep learning (DL) offer potential solutions for standardizing CRC pathology and discovering new biomarkers.
Purpose of the Study:
- To review recent advances in AI-assisted standardization of CRC pathology reporting.
- To explore AI-driven identification of novel prognostic biomarkers in CRC.
- To propose a harmonized approach for improved CRC risk assessment.
Main Methods:
- Review of recent studies on AI and DL in CRC histopathology.
- Analysis of AI applications for feature extraction from whole-slide images.
- Evaluation of AI-based biomarkers for prognostic prediction in CRC.
Main Results:
- AI tools can enhance the standardization of histopathology reporting for CRC.
- DL models applied to pathology slides can outperform traditional prognostic indicators.
- Novel prognostic parameters, including tumor-adipocyte interactions and immune cell patterns, are identified by AI.
Conclusions:
- AI holds significant promise for refining CRC pathology reporting and risk stratification.
- A combination of established pathology features and AI-derived indicators can improve patient outcomes.
- Further standardization and harmonization of AI approaches are needed for widespread clinical adoption.
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