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

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.

Comparison of Afirma Gene Expression Classifier with Gene Sequencing Classifier in indeterminate thyroid nodules: A single-institutional experience.

Introduction: The Afirma test has been used in the diagnosis of cytologically indeterminate thyroid nodules to reduce diagnostic uncertainty and unnecessary surgeries. Gene Sequencing Classifier (GSC) was developed to improve the positive predictive value and overall test performance of Gene Expression Classifier (GEC). Here we present our experience comparing the performance of first-generation assay of Afirma (GEC) with the new assay (GSC).