Church, David N

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study

Skrede, O.-J., De Raedt, S., Kleppe, A., Hveem, T. S., Liestøl, K., Maddison, J., Askautrud, H. A., Pradhan, M., Nesheim, J. A., Albregtsen, F., et al. (2020) · The Lancet

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

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Refining prognosis and identifying targetable pathways for high-risk endometrial cancer: a TransPORTEC initiative.

Stelloo, E., Bosse, T., Nout, R. A., MacKay, H. J., Church, D. N., Nijman, H. W., Leary, A., Edmondson, R. J., Powell, M. E., Crosbie, E. J., et al. (2015) · Modern Pathology

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Somatic POLE proofreading domain mutation, immune response, and prognosis in colorectal cancer: A retrospective, pooled biomarker study

Domingo, E., Freeman-Mills, L., Rayner, E., Glaire, M., Briggs, S., Vermeulen, L., Fessler, E., Medema, J. P., Boot, A., Morreau, H., et al. (2016) · The Lancet Gastroenterology & Hepatology

Genetic factors in colorectal cancerColorectal Cancer Treatments and Studies

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