Automated assessment of Ki-67 proliferation index in neuroendocrine tumors by deep learning

Autor:	Tiina, Vesterinen, Jenni, Säilä, Sami, Blom, Mirkka, Pennanen, Helena, Leijon, Johanna, Arola
Rok vydání:	2021
Předmět:	Automation Neuroendocrine Tumors Deep Learning Ki-67 Antigen Pathology Clinical Diagnostic Tests Routine Biomarkers Tumor Image Processing Computer-Assisted Humans Reproducibility of Results Algorithms Finland Cell Proliferation
Zdroj:	APMIS : acta pathologica, microbiologica, et immunologica Scandinavica. 130(1)
ISSN:	1600-0463
Popis:	The Ki-67 proliferation index (PI) is a prognostic factor in neuroendocrine tumors (NETs) and defines tumor grade. Analysis of Ki-67 PI requires calculation of Ki-67-positive and Ki-67-negative tumor cells, which is highly subjective. To overcome this, we developed a deep learning-based Ki-67 PI algorithm (KAI) that objectively calculates Ki-67 PI. Our study material consisted of NETs divided into training (n = 39), testing (n = 124), and validation (n = 60) series. All slides were digitized and processed in the Aiforia
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=pmid________::37e580bf5cb2afcd2b5371efe2fb8da3 https://pubmed.ncbi.nlm.nih.gov/34741788 Zobrazit plný text záznamu Plný text