In the study, the authors demonstrated that the initial genomic classifier could accurately identify those patients with a histologic pattern of usual interstitial pneumonia (UIP) without the need for surgery. The Envisia classifier was developed using machine learning and whole-genome RNA sequencing to identify the genomic signature of UIP, a pattern whose presence is essential to IPF diagnosis, from less-invasive transbronchial biopsies (TBB) which frequently are insufficient to yield a standard histopathology diagnosis. In the study published today, researchers evaluated 283 TBB samples from 84 patients who were enrolled in the prospective, multicenter BRAVE Study. They found the classifier had a specificity of 86 percent and sensitivity of 63 percent, suggesting it could identify nearly two-thirds of UIP cases with a high degree of accuracy. This performance was compared to a reference standard of paired surgical samples whose UIP/non-UIP histopathology pattern was conferred by a central panel of three pathologists with expertise in ILD.
"These strong early results informed the development of our commercialized Envisia Classifier, which we believe can help significant numbers of patients with suspected IPF obtain a more timely, accurate and safer diagnosis," said
At the recent
About Interstitial Lung Disease and Idiopathic Pulmonary Fibrosis
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About the Envisia Genomic Classifier
The Envisia Genomic Classifier is designed to improve physicians' ability to differentiate IPF from other ILDs without the need for surgery. The 190-gene classifier uses machine learning coupled with powerful, deep RNA sequencing to detect the presence or absence of UIP, a classic diagnostic pattern whose presence is essential for the diagnosis of IPF, using samples obtained through less-invasive bronchoscopy.
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