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MATCH 2 Study Reports 96.7% Diagnostic Yield for Noah Medical Robot

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MATCH 2 Study Reports 96.7% Diagnostic Yield for Noah Medical Robot

San Carlos – October 12, 2026 -- Noah Medical's Galaxy System achieved a 96.7% overall diagnostic yield under a strict definition in the MATCH 2 study, now published online ahead of print in the peer-reviewed journal Respiratory Medicine.

Study tested imaging-guided targeting in 31 patients with peripheral nodules

Thirty-one patients with peripheral pulmonary nodules underwent robotic-assisted bronchoscopy (RAB) with the Galaxy System. Researchers evaluated three-dimensional targeting accuracy using embedded digital tomosynthesis (DT) and integrated augmented fluoroscopy (AF). Cone-beam computed tomography (CBCT), which provides volumetric lung imaging during procedures, was used to confirm tool-in-lesion (TIL) placement.

Tool-in-lesion confirmation and CBCT concordance both reached 96.7%
  • 96.7% overall diagnostic yield under strict definition
  • 96.7% tool-in-lesion confirmation using digital tomosynthesis
  • 96.7% concordance between DT with AF and CBCT confirmation
Clinical problem: screening detects more nodules than traditional bronchoscopy can reliably diagnose

Peripheral lung nodules are increasingly found through screening programs and incidental imaging. Traditional bronchoscopic approaches can struggle with navigation and confirmation, particularly for small or hard-to-reach lesions.

The lead author, Dr. Amit Mahajan, Medical Director of the Interventional Pulmonology and Complex Airway Disease Program at Inova Health System, said reliable confirmation of lesion location is critical to diagnostic confidence and to diagnosing lung cancer earlier. "Early diagnosis of lung cancer is our best chance for cure," he said.

Noah Medical CEO frames results as evidence for standard-of-care shift

Noah Medical CEO Jian Zhang said the publication adds to a growing body of evidence and expects such data to shape the standard of care. The study was authored by Ankit K. Mahajan, Duy K. Duong, Johanna Cortes and Krish Bhadra.

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