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Envisia® Genomic Classifier
Increasing Confidence in Idiopathic Pulmonary Fibrosis Diagnosis

The Envisia Genomic Classifier is the first commercially available genomic test to improve the diagnosis of idiopathic pulmonary fibrosis (IPF). The test harnesses the power of RNA sequencing and machine learning to improve physicians’ ability to differentiate IPF from other interstitial lung diseases (ILD) without the need for invasive, risky and costly 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. Obtaining a timely IPF diagnosis is important, particularly given the availability of therapies that can slow the progression of the disease and recent data showing that traditional therapies for most ILDs are actually harmful to patients with IPF.
Limitations in current technologies make IPF challenging to differentiate from other ILDs. Physicians routinely use high-resolution computed tomography (HRCT) to help identify a hallmark pattern of IPF known as usual interstitial pneumonia (UIP), but this approach frequently provides inconclusive results. This can lead to surgical lung biopsy or other invasive diagnostic procedures.

In a clinical validation study published in The Lancet Respiratory Medicine, the Envisia classifier achieved a specificity of 88 percent and sensitivity of 70 percent for UIP. These findings mean that the test would be expected to identify more than two-thirds of UIP cases with a high degree of accuracy, while minimizing the number of false positive results, providing physicians with greater confidence in their diagnosis. In comparison, data show that HRCT imaging has a sensitivity of just 43 percent for UIP.

Learn More About the Envisia Genomic Classifier