Pathology reporting of neuroendocrine tumors: Application of the delphic consensus process to the development of a minimum pathology data set Journal Article


Authors: Klimstra, D. S.; Modlin, I. R.; Adsay, N. V.; Chetty, R.; Deshpande, V.; Gonen, M.; Jensen, R. T.; Kidd, M.; Kulke, M. H.; Lloyd, R. V.; Moran, C.; Moss, S. F.; Oberg, K.; Otoole, D.; Rindi, G.; Robert, M. E.; Suster, S.; Tang, L. H.; Tzen, C. Y.; Washington, M. K.; Wiedenmann, B.; Yao, J.
Article Title: Pathology reporting of neuroendocrine tumors: Application of the delphic consensus process to the development of a minimum pathology data set
Abstract: Epithelial neuroendocrine tumors (NETs) have been the subject of much debate regarding their optimal classification. Although multiple systems of nomenclature, grading, and staging have been proposed, none has achieved universal acceptance. To help define the underlying common features of these classification systems and to identify the minimal pathology data that should be reported to ensure consistent clinical management and reproducibility of data from therapeutic trials, a multidisciplinary team of physicians interested in NETs was assembled. At a group meeting, the participants discussed a series of "yes" or "no" questions related to the pathology of NETs and the minimal data to be included in the reports. After discussion, anonymous votes were taken, using the Delphic principle that 80% agreement on a vote of either yes or no would define a consensus. Questions that failed to achieve a consensus were rephrased once or twice and discussed, and additional votes were taken. Of 108 questions, 91 were answerable either yes or no by more than 80% of the participants. There was agreement about the importance of proliferation rate for tumor grading, the landmarks to use for staging, the prognostic factors assessable by routine histology that should be reported, the potential for tumors to progress biologically with metastasis, and the current status of advanced immunohistochemical and molecular testing for treatment-related biomarkers. The lack of utility of a variety of immunohistochemical stains and pathologic findings was also agreed upon. A consensus could not be reached for the remaining 17 questions, which included both minor points related to extent of disease assessment and some major areas such as terminology, routine immunohistochemical staining for general neuroendocrine markers, use of Ki67 staining to assess proliferation, and the relationship of tumor grade to degree of differentiation. On the basis of the results of the Delphic voting, a minimum pathology data set was developed. Although there remains disagreement among experts about the specific classification system that should be used, there is agreement about the fundamental pathology data that should be reported. Examination of the areas of disagreement reveals significant opportunities for collaborative study to resolve unanswered questions. © 2010 by Lippincott Williams & Wilkins.
Keywords: immunohistochemistry; cancer staging; neoplasm staging; cell proliferation; reproducibility; reproducibility of results; consensus; metastasis; classification; tumor markers, biological; practice guideline; cell differentiation; pathology; tumor marker; neuroendocrine tumor; standard; chemistry; practice guidelines as topic; prediction and forecasting; predictive value of tests; consensus development; terminology as topic; delphi study; delphi technique; nomenclature; genetic techniques; genetic procedures; carcinoid tumor; consensus development conferences as topic; neuroendocrine tumors; delphic; checklist
Journal Title: American Journal of Surgical Pathology
Volume: 34
Issue: 3
ISSN: 0147-5185
Publisher: Lippincott Williams & Wilkins  
Date Published: 2010-03-01
Start Page: 300
End Page: 313
Language: English
DOI: 10.1097/PAS.0b013e3181ce1447
PUBMED: 20118772
PROVIDER: scopus
DOI/URL:
Notes: --- - "Cited By (since 1996): 18" - "Export Date: 20 April 2011" - "CODEN: AJSPD" - "Source: Scopus"
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  1. Mithat Gonen
    1028 Gonen
  2. David S Klimstra
    978 Klimstra
  3. Laura Hong Tang
    447 Tang