Aligning computational pathology with clinical practice for colorectal cancer

Abstract Pathology reporting of colorectal cancer (CRC) follows the International Collaboration on Cancer Reporting (ICCR) guidelines which define a set of 25 diagnostic report elements. To further develop the CRC diagnostic routine, multiple computational tools have been proposed in the last years. Despite the excellent sensitivity and potential advantages, many tools do not reach clinical deployment, suggesting that there are critical challenges to address when developing these algorithms. To summarize existing efforts in deep 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. Most of the 66 included studies focus on a subset of just three ICCR elements, namely mismatch repair status, BRAFV600E mutation testing, and lymph node status. Moreover, many of the studies did not include clinically relevant and validated results. These results show the gap between research and clinical practice in pathology with the example of CRC diagnosis. There is an unmet need for publicly available datasets, and a stronger focus on clinically important tasks. This review will contribute to aligning computation pathology with the clinic to increase the translational potential of developed tools.