Literature DB >> 23906418

Interobserver variability in the pathological assessment of radical prostatectomy specimens: findings of the Laparoscopic Prostatectomy Robot Open (LAPPRO) study.

Josefin Persson1, Ulrica Wilderäng, Thomas Jiborn, Peter N Wiklund, Jan-Erik Damber, Jonas Hugosson, Gunnar Steineck, Eva Haglind, Anders Bjartell.   

Abstract

OBJECTIVE: The aim of this study was to strengthen the validity of future findings in the Laparoscopic Prostatectomy Robot Open (LAPPRO) study by investigating the extent of interobserver variability between local pathologists and re-evaluating reference pathologists.
MATERIAL AND METHODS: LAPPRO is a Swedish prospective study comparing robot-assisted laparoscopic prostatectomy to open retropubic radical prostatectomy. Patients were recruited from 2008 to 2011. A random selection of 289 prostatectomy specimens was re-evaluated, in a blind fashion, by two reference pathologists from a University Hospital in Denmark and compared with original reports from local pathologists.
RESULTS: The exact concordance rate of Gleason score (GS) between local and reference pathologists was 56% (Spearman correlation coefficient 0.54). Exact concordance rates (κ value) for pathological tumour stage (pT), extraprostatic extension (EPE), surgical margin status (SMS) and seminal vesicle invasion (SVI) were 87% (0.63), 86% (0.59), 92% (0.76) and 98% (0.82), respectively. In subanalyses for surgical technique, exact concordance rates of GS, pT, EPE, SMS and SVI were 58%, 83%, 84%, 90% and 97%, respectively, for surgical technique 1 (ST1), compared to 55%, 88%, 87%, 93% and 98%, for surgical technique 2 (ST2). In ST1 specimens undergrading of GS by the local pathologists compared to central review was more common than overgrading (26% vs 16%). The inverse relationship was seen in ST2 specimens (14% vs 32%).
CONCLUSION: Re-evaluation of randomly selected prostatectomy specimens in the LAPPRO cohort showed comparable results compared to previous studies of this kind. A systematic variation in the assessment of GS exists, attributable to individual differences in judgement between pathologists. Dichotomising GS (≤ 7 vs ≥ 8) overcomes the systematic variation.

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Year:  2013        PMID: 23906418     DOI: 10.3109/21681805.2013.820788

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


  5 in total

1.  Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer.

Authors:  Kunal Nagpal; Davis Foote; Yun Liu; Po-Hsuan Cameron Chen; Ellery Wulczyn; Fraser Tan; Niels Olson; Jenny L Smith; Arash Mohtashamian; James H Wren; Greg S Corrado; Robert MacDonald; Lily H Peng; Mahul B Amin; Andrew J Evans; Ankur R Sangoi; Craig H Mermel; Jason D Hipp; Martin C Stumpe
Journal:  NPJ Digit Med       Date:  2019-06-07

2.  Agreement of two pre-trained deep-learning neural networks built with transfer learning with six pathologists on 6000 patches of prostate cancer from Gleason2019 Challenge.

Authors:  Mircea Sebastian Şerbănescu; Carmen Nicoleta Oancea; Costin Teodor Streba; Iancu Emil Pleşea; Daniel Pirici; Liliana Streba; Răzvan Mihail Pleşea
Journal:  Rom J Morphol Embryol       Date:  2020 Apr-Jun       Impact factor: 1.033

3.  Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition.

Authors:  Julie A Schmidt; Georgina K Fensom; Sabina Rinaldi; Augustin Scalbert; Paul N Appleby; David Achaintre; Audrey Gicquiau; Marc J Gunter; Pietro Ferrari; Rudolf Kaaks; Tilman Kühn; Anna Floegel; Heiner Boeing; Antonia Trichopoulou; Pagona Lagiou; Eleutherios Anifantis; Claudia Agnoli; Domenico Palli; Morena Trevisan; Rosario Tumino; H Bas Bueno-de-Mesquita; Antonio Agudo; Nerea Larrañaga; Daniel Redondo-Sánchez; Aurelio Barricarte; José Maria Huerta; J Ramón Quirós; Nick Wareham; Kay-Tee Khaw; Aurora Perez-Cornago; Mattias Johansson; Amanda J Cross; Konstantinos K Tsilidis; Elio Riboli; Timothy J Key; Ruth C Travis
Journal:  BMC Med       Date:  2017-07-05       Impact factor: 8.775

4.  Automated Gleason grading of prostate cancer using transfer learning from general-purpose deep-learning networks.

Authors:  Mircea Sebastian Şerbănescu; Nicolae Cătălin Manea; Liliana Streba; Smaranda Belciug; Iancu Emil Pleşea; Ionica Pirici; Raluca Maria Bungărdean; Răzvan Mihail Pleşea
Journal:  Rom J Morphol Embryol       Date:  2020       Impact factor: 1.033

5.  Degree of Preservation of Neurovascular Bundles in Radical Prostatectomy and Recurrence of Prostate Cancer.

Authors:  Elin Axén; Rebecka Arnsrud Godtman; Anders Bjartell; Stefan Carlsson; Eva Haglind; Jonas Hugosson; Anna Lantz; Marianne Månsson; Gunnar Steineck; Peter Wiklund; Johan Stranne
Journal:  Eur Urol Open Sci       Date:  2021-06-19
  5 in total

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