Background: This study aimed to evaluate the outcome of patients with type 1 gastric neuroendocrine neoplasia (T1gNENs) treated with different endoscopic approaches.
Methods: Patients were managed with endoscopic surveillance at regular intervals. Resection was performed by forceps or cold snare in tumours < 10 mm, otherwise mucosal resection (EMR) or submucosal dissection (ESD) were done.
Results: 127 T1gNENs, detected in 80 patients, were included. 87.4% of them were <5 mm, whereas 8.7% were 6-10 mm, 3.1% were 11-20 mm, and 0.8% was >20 mm. Ki67 <3%% was found in 85.8% tumours, whereas it was 3%-20% in the remaining 14.2% lesions. Noninterventional management (surveillance without radical resection) was performed in 15 patients (18.7%) with T1gNENs <5 mm. None of them underwent disease progression during follow-up. among the 65 patients treated by radical endoscopic resection, 37 patients (56.9%) had disease recurrence. 48.5% T1gNENs were removed by forceps, 16.8% by cold snare, 31.7% by EMR and 3% by ESD. The recurrence rate was similar among the different endoscopic techniques used.
Conclusions: The management of T1gNENs may be planned based on tumour size. T1gNENs < 5 mm for which the initial removal is not radical could be followed up by noninterventional endoscopic surveillance.
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http://dx.doi.org/10.1016/j.dld.2021.11.012 | DOI Listing |
Cancer Res Commun
January 2025
University of Minnesota, Minnesota, MN, United States.
Neuroendocrine neoplasms (NENs) encompass a diverse set of malignancies with limited precision therapy options. Recently, therapies targeting DLL3 have shown clinical efficacy in aggressive NENs, including small cell lung cancers and neuroendocrine prostate cancers. Given the continued development and expansion of DLL3-targeted therapies, we sought to characterize the expression of DLL3 and identify its clinical and molecular correlates across diverse neuroendocrine and non-neuroendocrine cancers.
View Article and Find Full Text PDFAnn Surg Oncol
January 2025
Department of Gastrointestinal Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Background: This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).
Patients And Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015) were analyzed and split into training and validation groups (7:3 ratio). CS profiles for patients with GNEC were examined in the full cohort.
Ther Adv Med Oncol
January 2025
Department of Precision Medicine, Università degli Studi della Campania 'Luigi Vanvitelli', Naples, Italy.
Gastric cancer (GC), one of the tumours with the highest mortality worldwide, is not a homogeneous disease, showing different features according to location, macroscopic aspect, histotype and molecular alterations. Adenocarcinoma is the most frequent epithelial GC (95%), the remaining 5% comprising rare epithelial tumours with their peculiarities, behaviour and incidence <6 cases/100,000/year. Due to the low number of cases, many aspects must be elucidated in this context.
View Article and Find Full Text PDFSci Rep
January 2025
State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Pathology, Peking University Cancer Hospital and Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China.
Delta-like protein (DLL3) is a novel therapeutic target. DLL3 expression in gastroenteropancreatic neuroendocrine tumors (GEP-NECs) is poorly understood, complicating the distinction between well-differentiated neuroendocrine tumors G3 (NET G3) and poorly differentiated NEC. DLL3 immunohistochemistry (IHC) was performed on 248 primary GEP-NECs, correlating with clinicopathological parameters, NE markers, PD-L1, Ki67 index, and prognosis.
View Article and Find Full Text PDFZhonghua Bing Li Xue Za Zhi
February 2025
Departmen of Pathology, Jiangsu Province (Suqian) Hospital/Suqian First Hospital, Suqian 223800, China.
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