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Review
. 2019 Sep;475(3):263-277.
doi: 10.1007/s00428-019-02574-0. Epub 2019 May 16.

Dataset for the reporting of prostate carcinoma in radical prostatectomy specimens: updated recommendations from the International Collaboration on Cancer Reporting

Affiliations
Review

Dataset for the reporting of prostate carcinoma in radical prostatectomy specimens: updated recommendations from the International Collaboration on Cancer Reporting

James G Kench et al. Virchows Arch. 2019 Sep.

Abstract

The International Collaboration on Cancer Reporting (ICCR) was formed in 2011 to harmonise the datasets, protocols and checklists for pathological reporting of various cancers and develop internationally agreed upon, evidence-based datasets. A dataset for prostate cancer in radical prostatectomy specimens was developed in 2011-2012 as part of a pilot project; however, it required substantial revision following the ISUP Consensus Conference on Gleason Grading in 2014, the publication of the World Health Organisation (WHO) Classification of Tumours of the Urinary System and Male Genital Organs in 2016, and the 8th edition of the Tumour-Node-Metastasis (TNM) staging system in late 2016. This article presents the up-to-date, evidence-based ICCR dataset and associated commentary for reporting prostate cancer in radical prostatectomy specimens. PubMed and Google search engines were used to review the published literature on the subject, and the dataset was developed in line with the previously published ICCR framework for the development of cancer datasets. Substantial changes have been incorporated into the second edition of the ICCR prostate cancer (radical prostatectomy) dataset. These include revisions to prostate cancer grading, reporting of intraductal carcinoma of prostate and surgical margins, among others. Up-to-date cancer datasets underpin structured reporting and facilitate the production of consistent and accurate pathological data for patient care as well as comparisons between different cohorts and populations internationally.

Keywords: Datasets; Pathology; Prostate cancer; Protocols.

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