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Ström, P., Kartasalo, K., Olsson, H., Solorzano, L., Delahunt, B., Berney, D.M., Bostwick, D.G., Evans, A.J., Grignon, D.J., Humphrey, P.A., Iczkowski, K.A., Kench, J.G., Kristiansen, G., van der Kwast, T.H., Leite, K.R.M., McKenney, J.K., Oxley, J., Pan, C.C., Samaratunga, H., Srigley, J.R., Takahashi, H., Tsuzuki, T., Varma, M., Zhou, M., Lindberg, J., Lindskog, C., Ruusuvuori, P., Wählby, C., Grönberg, H., Rantalainen, M., Egevad, L. and Eklund, M. (2020) Artificial Intelligence for Diagnosis and Grading of Prostate Cancer in Biopsies: A Population-Based, Diagnostic Study. The Lancet Oncology, 21, 222-232.
https://doi.org/10.1016/S1470-2045(19)30738-7

has been cited by the following article:

• TITLE: Automatic Computer Analysis of Digital Images of Triple-Antibody-Stained Prostate Biopsies

  AUTHORS: Erik Wilander, Manuel de la Torre, Ursula Wilhelmsson, Sören Nygren

  KEYWORDS: Prostate, Adenocarcinoma, LGPIN, HGPIN, Antibody, Computer, Digital, Images, Automatic, Analysis, AMACR, P504S, Microscopy, Scanning

  JOURNAL NAME: Open Journal of Urology, Vol.11 No.1, January 29, 2021

  ABSTRACT: Background: Worldwide, prostatic adenocarcinoma is the most common tumour type among men. Aim: The aim of the present investigation was to develop a computer program to identify normal prostate biopsies and distinguish them from biopsies showing premalignant alterations (LGPIN, HGPIN) and adenocarcinoma. Method: Prostate biopsies (n = 2094) taken from 191 consecutive men during 2016 were stained with triple immunehistochemisty (antibodies to AMACRA, p63 and CK 5). Digital images of the biopsies were obtained with a scanning microscope and used to develop an automatic computer program (CelldaTM), intended to identify the morphological alterations. Visual microscopic finding was used as a reference. Result: Of the 191 men, 121 (63.4%) were diagnosed as having prostate adenocarcinoma and 70 (36.6%) as having no malignancy on the basis of the visual microscopy. In comparison, computer analysis identified 134 (70.2%) men with malignant disease and 57 (29.8%) with non-malignant disease after exclusion of artifacts, which constituted 10.4% of areas (indicated as malignant disease). Discrepant results were recorded in 15 (7.9%) men, and in 14 of these cases, HGPIN and areas suggestive of early invasion were common. Thus, it was uncertain whether these cases should be regarded as malignant or not. The agreement between the visual examination and the computer analysis was 92.1% (kappa value 0.823, sensitivity 99.2 and specificity was 0.80). Conclusion: It seems that computer analysis could serve as an adjunct to simplify and shorten the diagnostic procedure, first of all by ensuring that normal prostate biopsies are sorted out from those sent for visual microscopic evaluation.