Development of AI Software to Capture and Identify Circulating Rare Cells in Lung Patients
Award last edited on: 9/2/2023

Sponsored Program
Awarding Agency
Total Award Amount
Award Phase
Solicitation Topic Code
Principal Investigator
Yongjian Yu

Company Information

Axon Dx LLC

379 Reas Ford Road Suite 1
Earlysville, VA 22936
   (540) 239-0668
Location: Single
Congr. District: 05
County: Albemarle

Phase I

Contract Number: 2015008
Start Date: 6/1/2020    Completed: 5/31/2021
Phase I year
Phase I Amount
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.

Phase II

Contract Number: 2230782
Start Date: 3/15/2023    Completed: 2/28/2025
Phase II year
Phase II Amount
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is a new cancer treatment liquid biopsy product using Artificial Intelligence (AI) that can detect and classify cancer derived rare cell (CRC) from a blood draw. There are over 100 different types of cancers and over 1.9 million new cancer cases are expected to be diagnosed in the US in 2022 resulting in over 600,000 deaths (1,670 deaths per day). Cancer is the second most common cause of death in the US, exceeded only by heart disease. New treatment therapies are being developed for a substantial proportion of cancers with many clinical trials for new therapies on-going world-wide. The minimally invasive, high sensitivity blood test will monitor therapeutic response and progression at low-cost, supporting development of these new cancer treatments. Specifically, with a less invasive and more comprehensive diagnostic tool, the test results will give clinical researchers real-time insights into cancer tumor biology, providing better understanding of cancer heterogeneity. This Small Business Innovation Research (SBIR) Phase II project combines Artificial Intelligence (AI), specifically deep learning neural networks used for computer vision, with CRC immunofluorescent reagents integrated into an immunofluorescent microscope. The main objective of this effort is to identify and classify CRCs with high accuracy. There is increasing evidence that CRCs are correlated with cancer type, staging, treatment response, minimal residual disease, and overall disease progression. However, in a typical blood sample, there are over 7 million blood artifacts with very few CRCs present. Current techniques to analyze CRCs are expensive, lengthy, and are limited in automation. To meet project sensitivity, specificity, and runtime requirements, the AI image analysis will be further optimized to: 1) find CRCs, 2) discriminate against false positives, and 3) classify CRCs into clinically relevant types. The developed AI architectures will be selected through extensive training using thousands of clinical samples compared to expertly characterized cancer blood pathology images. After high sensitivity and specificity are demonstrated, development work will continue to mature the AI-revolutionized CRC liquid biopsy test to meet clinical research use only (RUO) requirements. For the cancer research community, the product offering will be used in the conduct of non-clinical laboratory research.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.