SBIR Phase I: Development of AI Software to Capture and Identify Circulating Rare Cells in Lung Patients

This broader impacts/commercial potential of this SBIR Phase I NSF project is to develop Artificial Intelligence (AI) software to identify circulating lung cancer related cells efficiently and accurately. It is estimated that there will be over 200,000 new cases of lung cancer in the US in 2020, driving the cost above $166 billion. The current standard of care requires close monitoring of these patients, with chest Computed Tomography (CT) scans taken every 6 weeks. Patients also undergo pelvis CT scans concurrently if their cancer is determined to be at later stages. The proposed technology will provide the clinician additional data for early detection of lung cancer with a simple blood draw in a clinical laboratory setting for immediate feedback to the patient and clinician, thus avoiding more invasive procedures and radiation exposures. The proposed project will advance liquid biopsy techniques in R&D clinical settings. This project’s novel imaging system’s ability to identify the fluorescent tumor-derived cells will provide a more sensitive and reliable methodology to detect early-stage disease and differentiate indolent from aggressive lung cancer, with further potential to be integrated into lung cancer screening programs. Utilizing advanced Artificial Intelligence (AI) algorithms and world-class optical immunofluorescent detection methods, this project’s fluorescent microscope will be an AI-driven image processing system. This project provides an unprecedented solution for detecting low levels of rare cells in a clinical setting through the combination of high resolution multichannel optical imaging, proprietary fluorescent taggants and assays, and state-of-the-art AI segmentation and classification techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.