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AI in the Decision Phase

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Abstract

Digitization is transforming healthcare in general and precision oncology in particular. The ability to collect and analyze images and data will be the gatekeeper of a new era in pathology. Collecting all available information about a patient’s individual cancer and the prediction of the (immune) response to therapy is the next step in precision oncology. The information should include biomarkers with known clinical relevance and should also be broad enough to be relevant across multiple options (one test, many drugs and their possible combination). Genetic information on cancer (mutations, rearrangements, translocations, mutational load etc.) has known prognostic and predictive value. However, densities and locations of different immune cells in the TME are also known to be associated with outcomes and therapy response.

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Jasani, B., Huss, R., Taylor, C.R. (2021). AI in the Decision Phase. In: Precision Cancer Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-84087-7_22

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  • DOI: https://doi.org/10.1007/978-3-030-84087-7_22

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  • Publisher Name: Springer, Cham

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