Literature DB >> 27416104

Interobserver variability in Gleason histological grading of prostate cancer.

Tayyar A Ozkan1, Ahmet T Eruyar2, Oguz O Cebeci1, Omur Memik1, Levent Ozcan1, Ibrahim Kuskonmaz2.   

Abstract

OBJECTIVE: The aims of this study were to evaluate the reproducibility of the Gleason grading system and to compare its interobserver variability with the novel Gleason grade grouping proposal using a large sample volume.
MATERIALS AND METHODS: In total, 407 pathology slides of prostate needle biopsies from 34 consecutive patients with prostate cancer were re-evaluated. The International Society of Urological Pathology 2005 modified Gleason grading system with Epstein's modification was used. Two pathologists, blind to each other and to the initial pathology report, performed the pathological evaluation. To determine interobserver concordance, the kappa (κ) coefficient test was used.
RESULTS: Pathologist 1 and pathologist 2 detected a tumor in 202 and 231 cores, respectively (p < 0.001). The two pathologists disagreed on the presence of a tumor in 31 cores. Of these 31 cores, 74% (n = 23/31) were Gleason pattern 3. The mean length of the cancer foci in these 31 disputed cores was 1.54 ± 0.8 mm. Concordance rates between the two observers for primary and secondary Gleason patterns were 63.96% (κ = 0.34) and 63.45% (κ = 0.37), respectively. Concordance with respect to the Gleason sum was 57.9% (κ = 0.43). When the Gleason scores were classified into the novel Gleason grade grouping, concordance was found to be 51.7% (κ = 0.39).
CONCLUSIONS: The agreement between observers on the Gleason sum was moderate. The novel Gleason grade grouping did not improve interobserver agreement. Further studies are needed to confirm these results on interobserver variability.

Entities:  

Keywords:  Gleason grading; neoplasm grading; observer variation; prostatic neoplasms

Mesh:

Year:  2016        PMID: 27416104     DOI: 10.1080/21681805.2016.1206619

Source DB:  PubMed          Journal:  Scand J Urol        ISSN: 2168-1805            Impact factor:   1.612


  27 in total

Review 1.  Future Perspectives and Challenges of Prostate MR Imaging.

Authors:  Baris Turkbey; Peter L Choyke
Journal:  Radiol Clin North Am       Date:  2017-12-09       Impact factor: 2.303

Review 2.  Artificial intelligence in histopathology: enhancing cancer research and clinical oncology.

Authors:  Artem Shmatko; Narmin Ghaffari Laleh; Moritz Gerstung; Jakob Nikolas Kather
Journal:  Nat Cancer       Date:  2022-09-22

Review 3.  Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway.

Authors:  Tristan Barrett; Maarten de Rooij; Francesco Giganti; Clare Allen; Jelle O Barentsz; Anwar R Padhani
Journal:  Nat Rev Urol       Date:  2022-09-27       Impact factor: 16.430

4.  Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains.

Authors:  Soheil Soltani; Ashkan Ojaghi; Hui Qiao; Nischita Kaza; Xinyang Li; Qionghai Dai; Adeboye O Osunkoya; Francisco E Robles
Journal:  Sci Rep       Date:  2022-06-04       Impact factor: 4.996

5.  AI Model for Prostate Biopsies Predicts Cancer Survival.

Authors:  Kevin Sandeman; Sami Blom; Ville Koponen; Anniina Manninen; Juuso Juhila; Antti Rannikko; Tuomas Ropponen; Tuomas Mirtti
Journal:  Diagnostics (Basel)       Date:  2022-04-20

6.  Relative Contribution of Sampling and Grading to the Quality of Prostate Biopsy: Results from a Single High-volume Institution.

Authors:  Carlo Andrea Bravi; Emily Vertosick; Amy Tin; Simone Scuderi; Giuseppe Fallara; Giuseppe Rosiello; Elio Mazzone; Marco Bandini; Giorgio Gandaglia; Nicola Fossati; Massimo Freschi; Rodolfo Montironi; Alberto Briganti; Francesco Montorsi; Andrew Vickers
Journal:  Eur Urol Oncol       Date:  2018-11-24

7.  Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2.

Authors:  Patrick Leo; Sacheth Chandramouli; Xavier Farré; Robin Elliott; Andrew Janowczyk; Kaustav Bera; Pingfu Fu; Nafiseh Janaki; Ayah El-Fahmawi; Mohammed Shahait; Jessica Kim; David Lee; Kosj Yamoah; Timothy R Rebbeck; Francesca Khani; Brian D Robinson; Natalie N C Shih; Michael Feldman; Sanjay Gupta; Jesse McKenney; Priti Lal; Anant Madabhushi
Journal:  Eur Urol Focus       Date:  2021-04-30

8.  Opposing prognostic relevance of junction plakoglobin in distinct prostate cancer patient subsets.

Authors:  Tanja Spethmann; Lukas Clemens Böckelmann; Vera Labitzky; Ann-Kristin Ahlers; Jennifer Schröder-Schwarz; Sarah Bonk; Ronald Simon; Guido Sauter; Hartwig Huland; Robert Kypta; Udo Schumacher; Tobias Lange
Journal:  Mol Oncol       Date:  2021-02-17       Impact factor: 6.603

9.  Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learning.

Authors:  Yechan Mun; Inyoung Paik; Su-Jin Shin; Tae-Yeong Kwak; Hyeyoon Chang
Journal:  NPJ Digit Med       Date:  2021-06-14

10.  miR‑367‑3p downregulates Rab23 expression and inhibits Hedgehog signaling resulting in the inhibition of the proliferation, migration, and invasion of prostate cancer cells.

Authors:  Wei Du; Dong Li; Jianhao Xie; Ping Tang
Journal:  Oncol Rep       Date:  2021-07-19       Impact factor: 3.906

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.