A Preliminary Study of Deep-Learning Algorithm for Analyzing Multiplex Immunofluorescence Biomarkers in Body Fluid Cytology Specimens.
Journal:
Acta cytologica
PMID:
34077933
Abstract
INTRODUCTION: Multiplex biomarker analysis of cytological body fluid specimens is often used to assist cytologists in distiguishing metastatic cancer cells from reactive mesothelial cells. However, evaluating biomarker expression visually may be challenging, especially when the cells of interest are scant. Deep-learning algorithms (DLAs) may be able to assist cytologists in analyzing multiple biomarker expression at the single cell level in the multiplex fluorescence imaging (MFI) setting. This preliminary study was performed to test the feasibility of using DLAs to identify immunofluorescence-stained metastatic adenocarcinoma cells in body fluid cytology samples.
Authors
Keywords
Adenocarcinoma
Biomarkers, Tumor
Cytodiagnosis
Deep Learning
Diagnosis, Computer-Assisted
Diagnosis, Differential
Feasibility Studies
Fluorescent Antibody Technique
Humans
Image Processing, Computer-Assisted
Microscopy, Fluorescence
Pleural Effusion, Malignant
Predictive Value of Tests
Reproducibility of Results
Retrospective Studies