Envisia™ Genomic Classifier
Clarifying the Diagnosis of Idiopathic Pulmonary Fibrosis

The Envisia Genomic Classifier, commercialized in October 2016, is designed to improve physicians’ ability to differentiate idiopathic pulmonary fibrosis (IPF) from other interstitial lung diseases (ILD) without the need for invasive and potentially risky surgery.

Each year across the United States and Europe, up to 200,000 patients are suspected of having an ILD. IPF is among the most common and most deadly of these lung-scarring diseases. It is notoriously difficult to diagnose, often leading to treatment delays, prolonged misdiagnosis, patient distress and added healthcare expense.
The Envisia classifier uses machine learning coupled with powerful, deep RNA sequencing to detect the presence or absence of usual interstitial pneumonia (UIP), a classic diagnostic pattern that is essential for the diagnosis of IPF. Physicians routinely use high-resolution CT imaging (HRCT) to identify UIP, but this approach frequently provides inconclusive results, leading many patients to undergo surgery to secure a more definitive diagnosis using surgical histopathology. Veracyte scientists trained the Envisia classifier to differentiate UIP from non-UIP on patient samples obtained through less-invasive outpatient bronchoscopy.


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