Deep learning can mine clinically useful information from histology. In gastrointestinal and liver cancer, such algorithms can predict survival and molecular alterations. Once pathology workflows are widely digitized, these methods could be used as inexpensive biomarkers. However, clinical translation requires training interdisciplinary researchers in both programming and clinical applications.
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J.N.K. has an informal, unpaid advisory role at Pathomix (Heidelberg, Germany). J.C. receives consulting fees from Owkin, Inc. (New York, NY, USA) and Crosscope (San Francisco, CA, USA).
EQUATOR network: https://www.equator-network.org/
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Kather, J.N., Calderaro, J. Development of AI-based pathology biomarkers in gastrointestinal and liver cancer. Nat Rev Gastroenterol Hepatol 17, 591–592 (2020). https://doi.org/10.1038/s41575-020-0343-3
Computational and Structural Biotechnology Journal (2020)