LungLB® publication in peer-reviewed journal
LungLife AI, Inc.
(the "Company" or "LungLife")
LungLB® analytical validation accepted for publication in peer-reviewed journal
LungLife AI (AIM: LLAI), a developer of clinical diagnostic solutions for lung cancer, announces the acceptance for publication of data confirming analytical performance of the Company's blood-based LungLB® test in the journal BMC Pulmonary Medicine.
Springer Nature's BMC Pulmonary Medicine is a globally recognised peer-reviewed medical journal with articles covering all aspects of the prevention, diagnosis and management of pulmonary and associated disorders. The full publication, entitled "Analytical Validation of the LungLB® test: a 4-color fluorescence in-situ hybridization assay for the evaluation of indeterminate pulmonary nodules" can be accessed here https://doi.org/10.1186/s12890-024-03280-7.
Publication in scientific journals is a crucial step in the commercialisation of LungLB® as the peer-review process supports the verification of the reliability and credibility of the research, building trust and confidence within the scientific and medical communities.
This publication adds to the growing body of evidence supporting LungLB®, including clinical validity and health economics data published in 2023, and is a key element in obtaining coverage for Medicare reimbursement, opening the test up for Medicare patients and increasing the likelihood of the test being adopted by centres.
As a reminder, the analytic validation experiments were done in accordance with the globally recognised Clinical and Laboratory Standards Institute (CLSI) guidelines, of which the US FDA recognises over 100 CLSI consensus standards, to evaluate sample stability and assay reproducibility under a variety of clinical and laboratory conditions. Collectively, the results demonstrate the LungLB® test worked consistently well, no matter which lab technician was running it, when it was done, or which batch of materials was used. This is important as it shows that the test is reliable and well-designed.
Eric Vail MD, Director of Molecular Pathology at Cedars Sinai Medical Center, Laboratory Director of LungLife AI, and co-author on the study commented:
"I am delighted that the analytic validation has been published in a highly regarded journal. Novel diagnostic tests must undergo rigorous analytical testing prior to clinical use to demonstrate accuracy and reliability in routine laboratory settings. The data show to the scientific and medical community that the LungLB® test is robust and suitable to everyday clinical use."
Paul Pagano, Chief Executive Officer of LungLife, added:
"Having the results of our study peer-reviewed and published is a significant milestone, validating the scientific rigour of our research. This achievement is a key part of our commercialisation strategy to build a bank of evidence of the efficacy and utility of our testing. It underscores both the credibility of our LungLB® test and provides greater confidence in its use, and importantly, by raising awareness about the test it supports our commitment to expanding access for those who need it most."
For further information please contact:
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LungLife AI, Inc. |
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Paul Pagano, CEO |
via investors@lunglifeai.com |
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David Anderson, CFO |
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Investec Bank plc (Nominated Adviser & Broker) |
Tel: +44 (0)20 7597 5970 |
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Virginia Bull / Lydia Zychowska / Sara Wallace |
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Goodbody (Joint Broker) |
Tel: +44 (0) 20 3841 6202 |
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Tom Nicholson / Cameron Duncan |
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About LungLife
LungLife AI is a developer of clinical diagnostic solutions designed to make a significant impact in the early detection of lung cancer, the deadliest cancer globally. Using a minimally invasive blood draw, the Company's LungLB® test is designed to deliver additional information to clinicians who are evaluating indeterminate lung nodules. For more information visit www.lunglifeai.com
Our Purpose is to be a driving force in the early detection to lung cancer. And our Vision is to invert the 20:80 ratio such that in years to come at least 80% of lung cancer is detected early.
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