Integrating artificial intelligence (AI) into colorectal cancer reporting
Clinical Snapshot
PICO Framework
| P — Population | Not applicable - this is a narrative review |
| I — Intervention | Not applicable - this is a narrative review |
| C — Comparator | Not applicable - this is a narrative review |
| O — Outcomes | Not applicable - this is a narrative review |
Bottom Line
This narrative review explores the potential for artificial intelligence to standardise colorectal cancer pathology reporting and identify novel prognostic biomarkers. While the authors highlight promising applications including automated TNM staging, tumour deposit detection, and discovery of AI-derived prognostic indicators, the review lacks the systematic methodology needed to provide robust evidence synthesis. The paper identifies important clinical needs - particularly addressing variability in CRC reporting despite existing guidelines - but provides insufficient systematic evidence to guide implementation decisions. The review would benefit pathologists and oncologists by highlighting emerging AI capabilities, but clinicians should await more rigorous systematic reviews and validation studies before considering practice changes. The authors appropriately emphasise the need for harmonised approaches combining traditional pathology with AI-derived indicators, though specific recommendations for implementation remain premature without stronger evidence synthesis.
Key Findings
P Value: Not applicable - narrative review
Effect Size: Not applicable - narrative review
Primary Outcome: Narrative synthesis of AI applications in CRC pathology reporting
Nnt Or Sensitivity: Variable performance metrics reported for different AI tools, but no systematic synthesis provided
Confidence Interval: Not applicable - narrative review
Clinical Application
Implementation challenges not systematically addressed, though regulatory and standardisation issues mentioned Relevant to Australian pathology practice, though TGA approval pathways for AI diagnostic tools not specifically discussed. Could inform RCPA guidelines development Pathologists and clinicians involved in colorectal cancer diagnosis and staging
Abstract
Artificial intelligence (AI) and deep learning (DL) are transforming cancer research and clinical care, with histopathology playing a central role in this transformation. In colorectal cancer (CRC), the second leading cause of cancer mortality world-wide, multimodal and vision-language models (VLMs) hold particular promise for enhancing the standardisation of histopathology reporting, the understanding of disease biology, and the discovery of novel prognostic indicators. Despite the availability of guidelines and reporting templates for essential prognostic indicators, variability remains in how key features such as TNM staging or tumour deposits are assessed and reported in routine clinical practice. AI-based tools have the potential to support refined extraction of established and extended features directly from whole-slide images. In parallel, recent studies have shown that DL models applied to pathology slides and associated AI-based biomarkers can outperform traditional histopathological prognostic indicators and uncover novel parameters, including tumour-adipocyte interactions, tumour-stroma ratio, and immune cell patterns at the invasive margin. Here, we review recent advances in both domains: AI-assisted standardisation of CRC pathology reporting and AI-driven identification of novel prognostic biomarkers. We highlight the need to refine and standardise CRC reporting practices and propose that a harmonised approach combining established pathology features with AI-derived prognostic indicators could refine risk assessment and improve outcomes for CRC patients. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
References
- 1.Bräutigam, K., Baker, A. M., Koelzer, V. H., Kather, J. N., & Graham, T. A. (2026). Integrating artificial intelligence (AI) into colorectal cancer reporting. The Journal of Pathology. https://doi.org/10.1002/path.70029
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