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Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.

Authors :
Lucas M
Jansen I
Savci-Heijink CD
Meijer SL
de Boer OJ
van Leeuwen TG
de Bruin DM
Marquering HA
Source :
Virchows Archiv : an international journal of pathology [Virchows Arch] 2019 Jul; Vol. 475 (1), pp. 77-83. Date of Electronic Publication: 2019 May 16.
Publication Year :
2019

Abstract

Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate biopsies, computer-aided grading becomes feasible. Computer-aided grading has the potential to improve histopathological grading and treatment selection for prostate cancer. Automated detection of GPs and determination of the grade groups (GG) using a convolutional neural network. In total, 96 prostate biopsies from 38 patients are annotated on pixel-level. Automated detection of GP 3 and GP ≥ 4 in digitized prostate biopsies is performed by re-training the Inception-v3 convolutional neural network (CNN). The outcome of the CNN is subsequently converted into probability maps of GP ≥ 3 and GP ≥ 4, and the GG of the whole biopsy is obtained according to these probability maps. Differentiation between non-atypical and malignant (GP ≥ 3) areas resulted in an accuracy of 92% with a sensitivity and specificity of 90 and 93%, respectively. The differentiation between GP ≥ 4 and GP ≤ 3 was accurate for 90%, with a sensitivity and specificity of 77 and 94%, respectively. Concordance of our automated GG determination method with a genitourinary pathologist was obtained in 65% (κ = 0.70), indicating substantial agreement. A CNN allows for accurate differentiation between non-atypical and malignant areas as defined by GPs, leading to a substantial agreement with the pathologist in defining the GG.

Details

Language :
English
ISSN :
1432-2307
Volume :
475
Issue :
1
Database :
MEDLINE
Journal :
Virchows Archiv : an international journal of pathology
Publication Type :
Academic Journal
Accession number :
31098801
Full Text :
https://doi.org/10.1007/s00428-019-02577-x