ISSN 1849-9031 (Online)

ISSN 1849-8922    (Print)

Algorithm for diagnosing primary neuroendocrine neoplasms of the lung.
Marija Gomerčić Palčić, Marija Perić, Luka Vrbanić, Nevenka Piskač Živković.


Pulmonary neuroendocrine neoplasms (PNEN) represent 25% of primary lung neoplasms. Diagnosing PNEN presents a significant challenge because of the heterogeneous clinical, radiographic and endoscopic presentations as well as varying degrees of malignant potential. The incidence of PNEN is expected to increase as a result of better diagnostic methods. Methods used in the diagnosis and staging of PNEN are similar to methods used in other types of lung cancers. Differentiation of PNEN from other types of lung tumors and between PNEN subtypes is difficult, which results in misdiagnosis, inadequate treatment and poor prognosis. Imaging methods represent a gold standard in diagnosing and staging PNEN. Almost half of pulmonary carcinoids (PC) are incidentally discovered on standard chest X-rays. Computed tomography (CT) of the chest and upper abdomen is required to define the primary tumor and its local and distant extent. When a lung tumor is suspected based on imaging methods, appropriate additional endoscopic and/or imagining methods need to be selected. It is of great importance to choose an adequate method for disease staging because the utility of 18F-fluorodeoxyglucose (18F-FDG) and isotope-labeled somatostatin analogues varies according to tumor histology. Positron emission tomography (PET)-CT plays an important role in identifying the tumor, its localization, size, invasion, as well as staging, and new radionuclide tracers are the future of PNEN diagnostics. Besides the use of biopsies and cytological findings in the classification of PNEN, immunohistochemical markers have an important role in the differential diagnosis of these tumors, and are used to help pathologists diagnose various subtypes of PNEN. The aim of this review is to present available biochemical, imaging and endoscopic markers/methods used in the diagnosis of PNEN and to provide a suitable diagnostic algorithm.