newsfilter
← Latest News
Business Wire·

bioAffinity Technologies to Present Positive Clinical and Veteran-Focused Data on CyPath® Lung at NAVREF 2026 Annual Conference

  • bioAffinity Technologies presented two posters on CyPath Lung at the 2026 NAVREF Annual Conference held September 27-30 in Arlington, Virginia.
  • The company's new distribution partner, AvMEDICAL, joined bioAffinity for the first time to engage with the federal healthcare market regarding CyPath Lung.
  • Lung cancer affects approximately 7,700 veterans annually, & the VA's expanded screening has increased the volume of indeterminate lung nodules requiring evaluation.
  • Poster #1 detailed three clinical cases where CyPath Lung's noninvasive flow cytometry & AI diagnostic accelerated diagnosis & prevented unnecessary invasive procedures.
  • Poster #2 outlined the ongoing BA-004 FlowPath Lung study, a 2,000-patient longitudinal trial evaluating the test in veteran & military populations across 12 VA centers & two military hospitals.
  • The study features at-home sputum collection to eliminate the need for patients to travel to medical facilities for specimen collection.
  • Previous performance data for pulmonary nodules of 20 mm or less showed 92% sensitivity, 87% specificity, & 99% negative predictive value.
  • A 2024 economic analysis suggested the test could save thousands of dollars per person & hundreds of millions overall in healthcare costs for lung cancer screening & diagnosis.
  • CyPath Lung is a Laboratory Developed Test (LDT) using flow cytometry & proprietary AI to identify malignancy-indicating cell populations in sputum.
  • The test incorporates a fluorescent porphyrin preferentially absorbed by cancer cells & is not intended for use as a sole diagnostic tool.
  • The John P. Murtha Cancer Center Research Program provided support & partial funding for the FlowPath Lung clinical study.
  • Forward-looking statements regarding potential cost savings & clinical outcomes are subject to risks & uncertainties that could cause actual results to differ from expectations.