The increasing amount of histological, immunohistochemical and ultrastructural information on some endocrine secretions in human lung cancers suggest the need to revise the classification of neuroendocrine lesions on surgical material. The aim of the present investigation based on lung specimens removed surgically is to give further support to recent proposal for an updated classification of neuroendocrine lung carcinomas. Our study includes 58 squamous cell carcinomas, 58 adenocarcinomas, 6 large cell carcinomas, 27 neuroendocrine carcinomas, and 30 nontumourous cases. Using histological methods (HE, Alcian PAS, Grimelius silver impregnation), we illustrate the presence of neuroendocrine cells and neuroepithelial bodies with their pathological evolutions, ranging from hyperplasia, to dysplasia, and overt neoplasia. On the basis of our experience we propose the following classification of neuroendocrine carcinomas (NEC): typical carcinoids (NECNID), peripheral carcinoid or well-differentiated NEC (NECWED), NEC of intermediate or poorly differentiated type (NECINT) and NEC of small celled or microcytoma type (NECMIC).
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Clin J Gastroenterol
January 2025
Department of Surgery, Shizuoka Medical Center NHO, 762-1, Nagasawa, Shimizu, Sunto, Shizuoka, 411-8611, Japan.
Mixed neuroendocrine-non-neuroendocrine neoplasm (MiNEN) of the colon is rare with a poor prognosis. Since the first description of a mixed neoplasm 100 years ago, the nomenclature has evolved, most recently with the 2022 World Health Organization (WHO) classification system. We describe our experience of a case of locoregionally advanced MiNEN of the descending colon treated with curative laparoscopic resection and adjuvant chemotherapy.
View Article and Find Full Text PDFWorld Neurosurg
January 2025
Department of Neurosurgery, Emory University, Atlanta, Georgia, USA; Department of Otolaryngology, Emory University, Atlanta, Georgia, USA. Electronic address:
Background: Giant pituitary neuroendocrine tumor (GPitNET) are challenging tumors with low rates of gross total resection (GTR) and high morbidity. Previously reported machine-learning (ML) models for prediction of pituitary neuroendocrine tumor extent of resection (EOR) using preoperative imaging included a heterogenous dataset of functional and non-functional pituitary neuroendocrine tumors of various sizes leading to variability in results.
Objective: The aim of this pilot study is to construct a ML model based on the multi-dimensional geometry of tumor to accurately predict the EOR of non-functioning GPitNET.
Invest New Drugs
January 2025
Postgraduate Training Base Alliance, Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, 310022, Zhejiang, China.
A novel molecular classification for small cell lung cancer (SCLC) has been established utilizing the transcription factors achaete-scute homologue 1 (ASCL1), neurogenic differentiation factor 1 (NeuroD1), POU class 2 homeobox 3 (POU2F3), and yes-associated protein 1 (YAP1). This classification was predicated on the transcription factors. Conversely, there is a paucity of information regarding the distribution of these markers in other subtypes of pulmonary neuroendocrine tumors (PNET).
View Article and Find Full Text PDFZhonghua Bing Li Xue Za Zhi
January 2025
Department of Pathology, the First Affiliated Hospital of University of Science and Technology of China/Anhui Provincial Hospital, Hefei230036, China.
Lung neuroendocrine neoplasms are a group of diverse, heterogeneous tumours that range from well-differentiated, low-grade neuroendocrine tumours-such as typical and atypical carcinoids-to high-grade, poorly differentiated aggressive malignancies, such as large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung cancer (SCLC). While the incidence of SCLC has decreased, the worldwide incidence of other pulmonary neuroendocrine neoplasms has been increasing over the past decades. In addition to the standard histopathological classification of lung neuroendocrine neoplasms, the introduction of molecular and sequencing techniques has led to new advances in understanding the biology of these diseases and might influence future classifications and staging that can subsequently improve management guidelines in the adjuvant or metastatic settings.
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