Multicenter Bronchoscopy Study Using AI and Stimulated Raman Histology for Fast Lung Cancer Detection

What this Trial Is About

This research aims to evaluate deep learning algorithms designed to detect suspicious cell and tissue patterns for cancer in biopsies taken from peripheral lung nodules, masses, and mediastinal or hilar lymph nodes. The study uses the NIO Laser Imaging System during bronchoscopy procedures to image samples without traditional processing. It is a prospective, observational study focusing on rapid lung cancer detection with artificial intelligence assistance. The study includes four groups based on biopsy location and method transbronchial forceps biopsy of peripheral lung nodulesmasses, transbronchial needle aspiration biopsy of peripheral lung nodulesmasses, transbronchial needle aspiration biopsy of mediastinalhilar lymph nodes, and transbronchial cryo biopsy of peripheral lung nodulesmasses. Each biopsy procedure is performed as part of routine clinical care, and imaging is done in the procedure room using the NIO Laser Imaging System. Participants are adults scheduled for clinically indicated lung biopsies with an intermediate to high likelihood of lung cancer. The study tracks outcomes from enrollment until the end of each biopsy procedure. Researchers will assess how well the deep learning algorithms identify suspicious morphology during the procedure. Participants will be monitored during their procedures, and data will be collected to validate the technologys performance in detecting cancerous changes.

A Multicenter Study in Bronchoscopy Combining Stimulated Raman Histology With Artificial Intelligence for Rapid Lung Cancer Detection - The ON-SITE Study