Literature DB >> 1938244

Histologic grading of prostatic adenocarcinoma: intraobserver reproducibility of the Mostofi, Gleason and Böcking grading systems.

M L Cintra1, A Billis.   

Abstract

Intraobserver variation of three grading systems--Mostofi, Gleason and Böcking--is examined. No significant difference was noted between the histological grades found in the two examinations by any of the three methods used. Neither the type of surgical procedure nor the number of slices with tumour influenced the reproducibility of histological grading within each system studied. In the Gleason system the intraobserver highest disagreement would not have resulted in change of therapy choice, but in 2% of tumours graded according to the Mostofi system this would have occurred if the choice of therapy would depend on the grading results.

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Year:  1991        PMID: 1938244     DOI: 10.1007/bf02583988

Source DB:  PubMed          Journal:  Int Urol Nephrol        ISSN: 0301-1623            Impact factor:   2.370


  14 in total

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Authors:  A Böcking; J Kiehn; M Heinzel-Wach
Journal:  Cancer       Date:  1982-07-15       Impact factor: 6.860

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Authors:  D E Henson
Journal:  Arch Pathol Lab Med       Date:  1988-11       Impact factor: 5.534

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Authors:  P Faul; F Eisenberger; E Elsässer
Journal:  Urologe A       Date:  1985-11       Impact factor: 0.639

8.  Predictors of lymphatic spread in prostatic adenocarcinoma: uro-oncology research group study.

Authors:  D F Paulson; P V Piserchia; W Gardner
Journal:  J Urol       Date:  1980-05       Impact factor: 7.450

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Journal:  Acta Urol Belg       Date:  1972-01

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Authors:  J F Gaeta; L C Englander; G P Murphy
Journal:  Urology       Date:  1986-04       Impact factor: 2.649

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  9 in total

1.  Intraobserver and interobserver reproducibility of WHO and Gleason histologic grading systems in prostatic adenocarcinomas.

Authors:  S O Ozdamar; S Sarikaya; L Yildiz; M K Atilla; B Kandemir; S Yildiz
Journal:  Int Urol Nephrol       Date:  1996       Impact factor: 2.370

2.  Automated prostate tissue referencing for cancer detection and diagnosis.

Authors:  Jin Tae Kwak; Stephen M Hewitt; André Alexander Kajdacsy-Balla; Saurabh Sinha; Rohit Bhargava
Journal:  BMC Bioinformatics       Date:  2016-06-01       Impact factor: 3.169

3.  Predicting the risk of harboring high-grade disease for patients diagnosed with prostate cancer scored as Gleason ≤ 6 on biopsy cores.

Authors:  Thomas Seisen; Françoise Roudot-Thoraval; Pierre Olivier Bosset; Aurélien Beaugerie; Yves Allory; Dimitri Vordos; Claude-Clément Abbou; Alexandre De La Taille; Laurent Salomon
Journal:  World J Urol       Date:  2014-07-02       Impact factor: 4.226

4.  Multiview boosting digital pathology analysis of prostate cancer.

Authors:  Jin Tae Kwak; Stephen M Hewitt
Journal:  Comput Methods Programs Biomed       Date:  2017-02-22       Impact factor: 5.428

Review 5.  Reproducibility and reliability of tumor grading in urological neoplasms.

Authors:  Rainer Engers
Journal:  World J Urol       Date:  2007-09-09       Impact factor: 4.226

6.  Stereologically estimated mean nuclear volume of prostatic cancer is a reliable prognostic parameter.

Authors:  K Arima; Y Sugimura; T Hioki; A Yamashita; J Kawamura
Journal:  Br J Cancer       Date:  1997       Impact factor: 7.640

7.  The Impact of Prostate Cancer Upgrading and Upstaging on Biochemical Recurrence and Cancer-Specific Survival.

Authors:  Arnas Bakavičius; Mingailė Drevinskaitė; Kristina Daniūnaitė; Marija Barisienė; Sonata Jarmalaitė; Feliksas Jankevičius
Journal:  Medicina (Kaunas)       Date:  2020-02-04       Impact factor: 2.430

8.  Subspecialty surgical pathologist's performances as triage pathologists on a telepathology-enabled quality assurance surgical pathology service: A human factors study.

Authors:  Beth L Braunhut; Anna R Graham; Fangru Lian; Phyllis D Webster; Elizabeth A Krupinski; Achyut K Bhattacharyya; Ronald S Weinstein
Journal:  J Pathol Inform       Date:  2014-05-26

9.  Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques.

Authors:  Subrata Bhattacharjee; Kobiljon Ikromjanov; Kouayep Sonia Carole; Nuwan Madusanka; Nam-Hoon Cho; Yeong-Byn Hwang; Rashadul Islam Sumon; Hee-Cheol Kim; Heung-Kook Choi
Journal:  Diagnostics (Basel)       Date:  2021-12-22
  9 in total

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