Its study, “Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia,” appeared in the journal BMC Pulmonary Medicine.
The 190-gene Envisia Genomic Classifier helps physicians distinguish between IPF and other interstitial lung diseases (ILDs) without the need for invasive, risky and costly surgery. It uses samples obtained through bronchoscopy to make an IPF or ILD diagnosis, achieving highly accurate, reproducible and reliable results across a broad range of conditions and variables.
To detect the presence or absence of usual interstitial pneumonia (UIP) — a classic diagnostic pattern “key” to detecting IPF — Envisia uses machine learning[contact-form-7 404 "Not Found"]
along with deep RNA sequencing. Physicians now use high-resolution CT imaging to identify UIP in people with a suspected IPF diagnosis, but results can be inconclusive and a surgical histopathology is often needed.
In an independent set of samples from 49 study participants, however, Envisia was capable of distinguishing UIP from non-UIP samples 88 percent of the time, and with a sensitivity of 67 percent. This means the genomic classifier would be expected to identify nearly two-thirds of UIP cases.
San Francisco-based Veracyte presented its data at the official launch of the Envisia Genomic Classifier in October 2016, during the American College of Chest Physicians (CHEST 2016) annual meeting in Los Angeles.
This latest study showed that Envisia classifier’s performance is highly accurate and reproducible, offering reliable results under various lab conditions and variables (including differences in sample stability, storage, RNA amount and potential sample contamination).
“These new data underscore the scientific rigor behind the Envisia Genomic Classifier and should give physicians and their patients even more confidence in the test’s results,” Dr. Giulia Kennedy, chief scientific officer of Veracyte, said in a press release. “By providing information that until now could only be obtained through diagnostic surgery, the Envisia classifier should help patients with suspected IPF get clearer answers faster so that they can receive the treatment they need — and avoid potentially harmful invasive procedures.”
The type of information that is now made possible through Envisia was previously only available through surgical lung biopsy and by expert pathology review. Veracyte researchers believe Envisia could transform the standard of care for IPF patients, reducing the number of surgeries for a diagnosis, accelerating confirmation of a diagnosis, and saving healthcare costs in the process.