A Preliminary Study of Deep-Learning Algorithm for Analyzing Multiplex Immunofluorescence Biomarkers in Body Fluid Cytology Specimens

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. Methods: A DLA was developed to analyze MFI-stained cells in body fluid cytological samples. A total of 41 pleural fluid samples, comprising of 20 positives and 21 negatives, were retrospectively collected. Multiplex immunofluorescence labeling for MOC31, BerEP4, and calretinin, were performed on cell block sections, and results were analyzed by manual analysis (manual MFI) and DLA analysis (MFI-DLA) independently. Results: All cases with positive original cytological diagnoses showed positive results either by manual MFI or MFI-DLA, but 2 of the 14 (14.3%) original cytologically negative cases had rare cells with positive MOC31 and/or BerEP4 staining in addition to calretinin. Manual MFI analysis and MFI-DLA showed 100% concordance. Conclusion: MFI combined with DLA provides a potential tool to assist in cytological diagnosis of metastatic malignancy in body fluid samples. Larger studies are warranted to test the clinical validity of the approach.