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Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens
222
Zitationen
26
Autoren
2020
Jahr
Abstract
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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Autoren
- 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
Institutionen
- Google (United States)(US)
- Toyota Technological Institute at Chicago(US)
- Naval Medical Center San Diego(US)
- University of Tennessee Health Science Center(US)
- University Health Network(CA)
- University of Toronto(CA)
- El Camino Hospital(US)
- Tufts Medical Center(US)
- North Florida/South Georgia Veterans Health System(US)
- Yale University(US)
- AstraZeneca (United States)(US)
- Tempus Labs (United States)(US)