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Comment
. 2020 Mar;17(3):131.
doi: 10.1038/s41585-020-0294-z.

Automated systems comparable to expert pathologists for prostate cancer Gleason grading

Tim Thomas  1
Affiliations
Comment

Automated systems comparable to expert pathologists for prostate cancer Gleason grading

Tim Thomas. Nat Rev Urol. 2020 Mar.
No abstract available

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Comment on

  • Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.
    Ström P, Kartasalo K, Olsson H, Solorzano L, Delahunt B, Berney DM, Bostwick DG, Evans AJ, Grignon DJ, Humphrey PA, Iczkowski KA, Kench JG, Kristiansen G, van der Kwast TH, Leite KRM, McKenney JK, Oxley J, Pan CC, Samaratunga H, Srigley JR, Takahashi H, Tsuzuki T, Varma M, Zhou M, Lindberg J, Lindskog C, Ruusuvuori P, Wählby C, Grönberg H, Rantalainen M, Egevad L, Eklund M. Ström P, et al. Lancet Oncol. 2020 Feb;21(2):222-232. doi: 10.1016/S1470-2045(19)30738-7. Epub 2020 Jan 8. Lancet Oncol. 2020. PMID: 31926806

References

Original articles
    1. Ström, P. et al. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 21, 222–232 (2020) - DOI
    1. Bulten, W. et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 21, 233–241 (2020) - DOI
    1. Madabhushi, A., Feldman, M. D. & Leo, P. Deep-learning approaches for Gleason grading of prostate biopsies. Lancet Oncol. 21, 187–189 (2020) - DOI
Related article
    1. Goldenberg, S. L., Nir, G. & Salcudean, S. E. A new era: artificial intelligence and machine learning in prostate cancer. Nat. Rev. Urol. 16, 391–403 (2019) - DOI

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