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Published on: August 25, 2023
Classification of pulmonary neuroendocrine tumors: new insights
Giuseppe Pelosi1,2, Angelica Sonzogni3, Sergio Harari4
1Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy.
Abstract:
Neuroendocrine tumors of the lung (Lu-NETs) embrace a heterogeneous family of neoplasms classified into four histological variants, namely typical carcinoid (TC), atypical carcinoid (AC), large cell neuroendocrine carcinoma (LCNEC) and small cell lung carcinoma (SCLC). Defining criteria on resection specimens include mitotic count in 2 mm2 and the presence or absence of necrosis, alongside a constellation of cytological and histological traits including cell size and shape, nuclear features and overall architecture. Clinically, TC are low-grade malignant tumors, AC intermediate-grade malignant tumors and SCLC/LCNEC high-grade malignant full-blown carcinomas with no significant differences in survival between them. Homologous tumors arise in the thymus that occasionally have some difficulties in differentiating from the lung counterparts when presented with large unresectable or metastatic lesions. Immunohistochemistry (IHC) helps refine NE diagnosis at various anatomical sites, particularly on small-sized tissue material, in which only TC and small cell carcinoma categories can be recognized easily on hematoxylin & eosin stain, while AC and LCNEC can only be suggested on such material. The Ki-67 labeling index effectively separates carcinoids from small cell carcinoma and may prove useful for the clinical management of a metastatic disease to help the therapeutic decision-making process. Although carcinoids and high-grade neuroendocrine carcinomas in the lung and elsewhere make up separate tumor categories on molecular grounds, emerging data supports the concept of secondary high-grade NETs arising in the preexisting carcinoids, whose clinical and biological relevance will have to be placed into the proper context for the optimal management of these patients. In this review, we will discuss the selected, recent literature with a focus on current issues regarding Lu-NET nosology, i.e., classification, derivation and tumor evolution.
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