Veracyte announces new data, highlights Afirma GSC performance
Veracyte announced that data presented at the 87th Annual Meeting of the American Thyroid Association show that the Afirma Genomic Sequencing Classifier's ensemble machine learning algorithms can effectively distinguish challenging-to-interpret thyroid cancer subtypes among thyroid nodules deemed indeterminate - not clearly benign or malignant - by cytopathology. The findings, shared in two oral presentations, show significant advances in identifying benign versus cancerous Hurthle cells and 100 percent accuracy in identification of medullary thyroid cancer in patients with indeterminate thyroid nodules. Earlier in the week, three validation studies demonstrating the performance of the Afirma GSC were presented as posters at the ATA annual meeting. Key findings include: the Afirma GSC's 100 percent sensitivity and 99% specificity in the detection of BRAF V600E; sensitivity and specificity of 100% each in distinguishing parathyroid from non-parathyroid tissue; and strong analytical verification data demonstrating robust Afirma GSC performance on thyroid nodule FNA samples with as little as 5 nanograms of RNA and in situations where contaminants such as blood are present.