Clinicopathologic characteristics of neuroendocrine tumors with assessment by digital image analysis for Ki-67 index with a focus on the gastroenteropancreatic tract: a single-center study

Abstract

Objectives: Neuroendocrine tumors (NETs) are a heterogeneous group of tumors that arise at various sites throughout the body. The gastroenteropancreatic (GEP) tract is the most common site of NETs. We investigated the clinicopathologic features of patients with GEP-NETs and the utility of digital image analysis, which was compared to eyeball estimation, a conventional method used to determine the Ki-67 labeling index.

Methods: The clinicopathologic data of GEP-NET patients at Gangnam Severance Hospital from January 2008 to October 2019 were retrospectively analyzed. Each case was reclassified according to the 2019 World Health Organization classification system, to which the classification of grade 3 was added. Comparisons between eyeball estimation and the digital image analysis method for Ki-67 index assessment were performed by calculating Cohen's kappa (k) coefficient.

Results: In total, 345 patients with GEP-NETs were enrolled. The mean age was 49.3 (range 13-79) years, with more male (61.1%) than female patients. The primary tumor sites were the rectum (70.1%), pancreas (12.5%), stomach (6.7%), and duodenum (5.8%). Overall, 298 (86.4%), 35 (10.1%), 2 (0.6%), and 10 (2.9%) patients exhibited grade 1, 2, and 3 and neuroendocrine carcinoma, respectively. Statistical analysis revealed that age > 50 years, tumor size > 2 cm, and presence of lymphovascular invasion, nodal metastasis, and distant metastasis were significantly associated with short overall survival. Additionally, 283 patients underwent digital image analysis of the Ki-67 index, and substantial agreement was found between the two methods (κ value: 0.765).

Conclusions: Eyeball estimation revealed non-inferior results compared with digital image analysis. Further research is needed to evaluate the possibility of using digital image analysis as an alternative analysis method.