Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens Article Swipe
Related Concepts
Medicine
Grading (engineering)
Biopsy
Prostate cancer
Prostate
Second opinion
Prostate biopsy
Radiology
Tumor grade
Medical physics
Cancer
Pathology
Internal medicine
Engineering
Civil engineering
Kunal Nagpal
,
Davis Foote
,
Fraser Elisabeth Tan
,
Yun Liu
,
Po-Hsuan Cameron Chen
,
David F. Steiner
,
Naren Sarayu Manoj
,
Niels Olson
,
Jenny L. Smith
,
Arash Mohtashamian
,
Brandon Peterson
,
Mahul B. Amin
,
Andrew Evans
,
Joan W. Sweet
,
Carol C. Cheung
,
Theodorus van der Kwast
,
Ankur R. Sangoi
,
Ming Zhou
,
Robert W. Allan
,
Peter A. Humphrey
,
Jason Hipp
,
Krishna Gadepalli
,
Greg S. Corrado
,
Lily H. Peng
,
Martin C. Stumpe
,
Craig H. Mermel
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1001/jamaoncol.2020.2485
· OA: W3044088237
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1001/jamaoncol.2020.2485
· OA: W3044088237
In this study, the DLS showed higher proficiency than general pathologists at Gleason grading prostate needle core biopsy specimens and generalized to an independent institution. Future research is necessary to evaluate the potential utility of using the DLS as a decision support tool in clinical workflows and to improve the quality of prostate cancer grading for therapy decisions.
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