Aligning Computational Pathology with Clinical Practice: A Systematic Review of AI Tools for Pathology Report Elements in Colorectal Cancer
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BACKGROUND
Pathology reporting of colorectal cancer (CRC) follows the International Collaboration on Cancer Reporting (ICCR) guidelines which define a set of 25 elements (such as tumor grade, TNM stage and microsatellite instability) to be assessed for diagnosis. With the aim to further develop the CRC diagnostic routine, multiple computational tools have been developed in the last ten years. Despite the excellent sensitivity and potential advantages, including reduced inter-observer variability, many tools do not reach clinical deployment. This suggests that there are critical challenges to address when developing these algorithms given the aim to reliably and automatically assess ICCR elements.
METHODS
To summarize existing efforts in deep- and machine learning for ICCR CRC elements and highlight existing gaps between development and clinical deployment, this systematic review collected studies on computational tools for colorectal cancer histopathology analysis published between 2015 and 2024.
RESULTS
In total, 4863 studies were retrieved for the analysis, of which 66 remained after screening. Most of the reviewed studies focus on a subset of just three ICCR elements, namely mismatch repair status, BRAFV600E mutation testing, and lymph node status. Notably, many of the studies did not include clinically relevant and validated results, which puts into question their reliability for routine diagnosis.
CONCLUSION
These results show the wide gap between research and clinical practice in pathology with the example of CRC diagnosis. There is an unmet need for publicly available datasets addressing a variety of topics, and a stronger focus on clinically important tasks. This review is critical to help the community align their contributions in computation pathology with the clinic and ultimately increase the translational potential of the developed tools.
