Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.
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....
| Publicado en: | Virchows Archiv: European Journal of Pathology Vol. 475; no. 1; pp. 77 - 84 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137339392&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137339392 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09456317 O1Z jtl: Virchows Archiv: European Journal of Pathology issn: 09456317 maglogo: N pubinfo: dt: Jul2019 vid: 475 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137339392 137339392 NLM31098801 10.1007/s00428-019-02577-x NLM31098801 137339392 ppf: 77 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies. aug: au: 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. affil: Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands sug: subj: Prostatic Neoplasms Pathology Information Science Methods Image Interpretation, Computer Assisted Methods Neoplasm Grading Methods Reproducibility of Results Male Predictive Value of Tests Prostatic Neoplasms Classification Observer Bias Biopsy Automation, Laboratory Male ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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