Neuroendocrine tumors of the lung: the current classification and pathology diagnosis algorithm
https://doi.org/10.17650/2313-805X-2017-4-2-46-58
Abstract
Bronchopulmonary neuroendocrine tumors (NET) comprise one of the most common categories within the heterogeneous group of human neuroendocrine neoplasms. Tumors of this type often occur in practical pathology diagnosis; however, their classification and histological grading are not the same as for the gastrointestinal and pancreatic NET. Terminology of lung NET is still based on using the term «carcinoid». In the recent World Health Organization classification of lung tumors published in 2015, all NETs of this localization for the first time were presented in one single chapter. According to the current classification scheme the group of neuroendocrine proliferation processes consists of carcinoid tumors (including typical carcinoid and atypical carcinoid), large-cell neuroendocrine carcinoma and small cell lung carcinoma, along with diffuse idiopathic pulmonary neuroendocrine cell hyperplasia as a pre-invasive lesion with a potential toward the development of carcinoids. Each tumor category has characteristic morphological and immunohistochemical features, which are the key diagnostic criteria of these tumors. Histology parameters for grading have remained unchanged in new edition. However, uncertainties remain in relation to the role of Ki-67 in tumor grading in resection specimens and small samples. This review outlines the main key questions in the field of classification and pathology diagnosis of lung NET, the answers to which are still inconclusive. Thus additional research is required to improve our understanding on NET of this localization.
About the Author
V. V. Delektorskaya
N.N. Blokhin Russian Cancer Research Center, Ministry of Health of Russia
Russian Federation
24 Kashirskoe Shosse, Moscow 115478
Russian Federation
24 Kashirskoe Shosse, Moscow 115478
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For citations:
Delektorskaya V.V. Neuroendocrine tumors of the lung: the current classification and pathology diagnosis algorithm. Advances in Molecular Oncology. 2017;4(2):46-58. (In Russ.) https://doi.org/10.17650/2313-805X-2017-4-2-46-58
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