medical guideline

Dataset for the Reporting of Urinary Tract Carcinoma-biopsy and Transurethral Resection Specimen: Recommendations from the International Collaboration on Cancer Reporting (ICCR)

原文:2020年 发布于 Mod Pathol 33卷 第4期 700-712 浏览量:112 原文链接

The International Collaboration on Cancer Reporting (ICCR) is an alliance of major pathology organisations in Australasia, Canada, Europe, United Kingdom, and United States of America that develops internationally standardised, evidence-based datasets for the pathology reporting of cancer specimens. This dataset was developed by a multidisciplinary panel of international experts based on previously published ICCR guidelines for the production of cancer datasets. It is composed of Required (core) and Recommended (noncore) elements identified on the basis of literature review and expert consensus. The document also includes an explanatory commentary explaining the rationale behind the categorization of individual data items and provides guidance on how these should be collected and reported. The dataset includes nine required and six recommended elements for the reporting of cancers of the urinary tract in biopsy and transurethral resection (TUR) specimens. The required elements include specimen site, operative procedure, histological tumor type, subtype/variant of urothelial carcinoma, tumor grade, extent of invasion, status of muscularis propria, noninvasive carcinoma, and lymphovascular invasion (LVI). The recommended elements include clinical information, block identification key, extent of T1 disease, associated epithelial lesions, coexistent pathology, and ancillary studies. The dataset provides a structured template for globally harmonized collection of pathology data required for management of patients diagnosed with cancer of the urinary tract in biopsy and TUR specimens. It is expected that this will facilitate international collaboration, reduce duplication of effort in updating current national/institutional datasets, and be particularly useful for countries that have not developed their own datasets.