Automated quantification of Ki-67 index associates with pathologic grade of pulmonary neuroendocrine tumors

• Published in: Chinese medical journal 2019-03, Vol.132 (5), p.551-561
• Main Authors: Wang, Hai-Yue, Li, Zhong-Wu, Sun, Wei, Yang, Xin, Zhou, Li-Xin, Huang, Xiao-Zheng, Jia, Ling, Lin, Dong-Mei
• Format: Article
• Language: English
• Subjects: Algorithms; Automation; Chemotherapy; Cloning; Lung cancer; Lymphatic system; Medical prognosis; Morphology; Neuroendocrine tumors; Software
• ISSN: 0366-6999; 2542-5641
• DOI: 10.1097/CM9.0000000000000109

Classification of the pulmonary neuroendocrine tumor (pNET) categories is a step-wise process identified by the presence of necrosis and number of mitoses per 2 mm. In neuroendocrine tumor pathology, Ki-67 was first described as a prognostic factor in the pancreas and incorporated into the grading system of digestive tract neuroendocrine neoplasms in the 2010 WHO classification. However, the significance of Ki-67 in pNETs was still a controversial issue. This study was to investigate the potentially diagnostic value of Ki-67 in pNETs. We retrieved 159 surgical specimens of pNETs, including 35 typical carcinoids (TCs), 2 atypical carcinoid (ACs), 28 large-cell neuroendocrine carcinomas (LCNECs), 94 small-cell lung cancers (SCLCs). Manual conventional method (MCM) and computer-assisted image analysis method (CIAM) were used to calculate the Ki-67 proliferative index. In CIAM, 6 equivalent fields (500 × 500 μm) at 10× magnification were manually annotated for digital image analysis. The Ki-67 index among the 4 groups with ranges of 0.38% to 12.66% for TC, 4.34% to 29.48% for AC, 30.67% to 93.74% for LCNEC, and 40.71% to 96.87% for SCLC. The cutoff value of Ki-67 index to distinguish low grade with high grade was 30.07%. For the univariate survival analyses in pNETs, both the overall survival and progression-free survival correlated with Ki-67 index. In addition, the Ki-67 index performed by CIAM was proved to be of great positive correlation with MCM. Ki-67 index counted by CIAM is a reliable method and can be a useful adjunct to classify the low- and high-grade NETs.This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0.