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). METHODS: Retrospective analysis was performed on all Bethesda III and IV cytology thyroid nodules tested with GEC and GSC. Test performance was evaluated by surgical pathology outcomes. RESULTS: In total, 167 cases were tested with GEC, of which 49% were reported as benign. Fourteen cases had surgical follow-up with 11 benign, one non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and two malignant diagnoses. Of the 167 cases, 51% had suspicious GEC result. Fifty-seven of these suspicious GEC cases had surgical follow-up with 28 benign, nine NIFTP and 20 malignant histology. There 133 cases tested with GSC, of which 61% were reported as benign. Ten cases had surgical follow-up, all of which showed benign results and 32% of the cases were tested as suspicious. Thirty-six cases with suspicious GSC had surgical follow-up. Fourteen of them had benign, five NIFTP, and 17 malignant surgical pathology. Based on molecular testing, surgical resection could have been be prevented 61% with GSC, compared to 49% with GEC test. CONCLUSION: Our experience shows that GSC has a better test performance than GEC. Also, our data support that GSC identify more cases as benign and reduces the number of unnecessary surgeries compared to GEC.

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. 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.