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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study
Peter Ström ,
Kimmo Kartasalo ,
Henrik Olsson ,
Leslie Solorzano ,
Brett Delahunt ,
Daniel M Berney ,
David G Bostwick ,
Andrew J Evans ,
David J Grignon ,
Peter A Humphrey ,
Kenneth A Iczkowski ,
James G Kench ,
Glen Kristiansen ,
Theodorus H van der Kwast ,
Katia R M Leite ,
Jesse K McKenney ,
Jon Oxley ,
Chin-Chen Pan ,
Hemamali Samaratunga ,
John R Srigley ,
Hiroyuki Takahashi ,
Toyonori Tsuzuki ,
Murali Varma ,
Ming Zhou ,
Johan Lindberg ,
Cecilia Lindskog ,
Pekka Ruusuvuori ,
Carolina Wählby ,
Henrik Grönberg ,
Mattias Rantalainen ,
Lars Egevad ,
Martin Eklund
January 2020
January 2020
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Abstract
An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists
puts a strain on pathology departments. Additionally, the high intra-observer and
inter-observer variability in grading can result in overtreatment and undertreatment
of prostate cancer. To alleviate these problems, we aimed to develop an artificial
intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection,
localisation, and Gleason grading.