We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.
- Authors
Lucas, Marit; Jansen, Ilaria; Savci-Heijink, C. Dilara; Meijer, Sybren L.; de Boer, Onno J.; van Leeuwen, Ton G.; de Bruin, Daniel M.; Marquering, Henk A.
- 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.
- Subjects
PROSTATE biopsy; EXOCRINE glands; DEEP learning; PATIENT selection; ARTIFICIAL neural networks; DIGITAL image processing
- Publication
Virchows Archiv: European Journal of Pathology, 2019, Vol 475, Issue 1, p77
- ISSN
0945-6317
- Publication type
journal article
- DOI
10.1007/s00428-019-02577-x