Lung Cancer Diagnostics
LungLife AI’s LungLB test is positioned to bring certainty into early stage diagnosis confirming for clinicians whether to refer onto biopsy where the LDCT Scan reveals suspicious nodules. Providing a liquid biopsy approach for the patient also promotes a non-invasive monitoring program.
LungLife AI is also developing a series of companion diagnostics. Designed for later stage treatments, understanding likely patient therapeutic responses improves outcomes. Patient stratification is also an important component to improve performance metrics in research.
LungLife AI’s liquid biopsy testing is leading the way to more affordable and clinically actionable precision medicine strategies for lung cancer patients.
Artificial Intelligence (AI)
LungLife AI’s diagnostic technology combined with machine learning with image analysis results in workflow efficiencies and improvement in performance. This integrated regulated solution is expected to positively support clinician decision making and ease the burden of increased testing in the healthcare system.
To expand upon the success of the LungLB testing in increasing clinician and patient confidence in using LDCT scanning, LungLife AI will be applying machine learning algorithms to deliver an integrated high performing approach to early detection solution in lung cancer.
These algorithms are also expected to be applied in research collaborations to promote new treatment options.
Lung Cancer Today
Early detection is key to improving lung cancer patient outcomes. The National Cancer Institute’s estimates for lung cancer in the United States for 2018 are:
About 234,030 new cases of lung cancer for 20181
About 154,050 deaths from lung cancer, accounting for 25% of all cancer deaths¹
Lack of effective early detection results in a mere 16% of patients being diagnosed at early stages of disease when cure is possible¹
It has been estimated as high as 30% of patients receiving curative surgery return to the clinic with recurrence within 2-3 years.²
¹Howlader N et al. SEER Cancer Statistics Review, 1975-2014, National Cancer Institute. Bethesda, MD, https://seer.cancer.gov/csr/1975_2014/, based on November 2016 SEER data submission, posted to the SEER web site, April 2017.
²Schuchert MJ et al. Factors influencing recurrence following anatomic lung resection for clinical stage I non-small cell lung cancer. Lung Cancer 2019; 128:145-151