LungLife AI, INC LungLB(R) study published in peer-reviewed journal (7208B)
June 06 2023 - 2:00AM
UK Regulatory
TIDMLLAI
RNS Number : 7208B
LungLife AI, INC
06 June 2023
LungLife AI, Inc.
(the "Company" or "LungLife")
LungLB(R) study demonstrating high performance published in
peer-reviewed journal
LungLife AI (AIM: LLAI), a developer of clinical diagnostic
solutions for lung cancer, announces the publication of successful
performance results for the Company's blood-based LungLB(R) test
from a multi-site prospective study in patients with indeterminate
pulmonary nodules. The study was performed in collaboration with MD
Anderson Cancer Center (Houston, TX) and Icahn School of Medicine
at Mount Sinai (New York, NY) and appears in the journal BMC
Pulmonary Medicine.
The primary objective of the study was to compare the LungLB(R)
test result with a lung biopsy diagnosis and assess performance in
a patient cohort where commonly used nodule evaluation tools were
not informative.
Key points from the study include:
-- 151 study participants scheduled for CT-guided lung biopsy,
70% of whom were found to have "intermediate risk" nodules that
represent the most challenging diagnostic subtype
-- The LungLB(R) test outperformed commonly used evaluation
tools, including the Mayo Clinic risk model and PET scan
-- The test demonstrated robust performance in smaller nodules
(<2 cm in diameter) and in early-stage cancer
-- The LungLB(R) biomarker was found to be the strongest
independent predictor of cancer in this study, exceeding commonly
known strong predictors such as nodule size and smoking status
-- The LungLB(R) test performed equally well in current, former, and never smokers
-- The data support potential clinical utility of LungLB(R) in
reducing delays in treatment, in which positive LungLB(R) test
results were available months ahead of lung cancer diagnosis in
three highlighted cases
LungLB(R) is a blood-based liquid biopsy assay that uses
fluorescence in situ hybridisation ("FISH") and image analysis to
identify circulating genetically abnormal cells ("CGAC"), which
include circulating tumour cells ("CTCs"). The technique
incorporates an AI-derived image analysis strategy to identify
unique cell populations reflective of the disease state under
interrogation.
The current publication is an independent validation in the US
population and informed the design of LungLife's larger, pivotal
validation study that started in February 2022. The pivotal
validation study results are expected later this year and are not
included in this publication.
The publication can be found here:
https://doi.org/10.1186/s12890-023-02433-4 .
Commenting, Paul Pagano, Chief Executive Officer of LungLife,
said : "Having the results of our study peer-reviewed is not only
an independent confirmation of the scientific validity of our
approach, but is also an important part of our strategic effort to
build evidence for efficacy and utility in our efforts to increase
access of our testing to those who need it most.
"We were excited to see that the LungLB(R) performance remained
strong in people who never smoked, because evidence has shown that
the incidence of lung cancer in never smokers is increasing, and it
is important new technologies address this expanding group.
Additionally, LungLB(R) was able to detect cancer in its earliest
stages, which can be the most challenging to diagnose for doctors
and most beneficial for patients."
BMC Pulmonary Medicine is in the Springer-Nature family of
journals, and is an open access, peer-reviewed journal that
considers articles on all aspects of the prevention, diagnosis and
management of pulmonary and associated disorders. Studies must be
scientifically valid; for research articles this includes a
scientifically sound research question, the use of suitable methods
and analysis, and following community-agreed standards relevant to
the research field. Peer-review is the system used to assess the
quality of a manuscript before it is published. Independent
researchers in the relevant research area assess submitted
manuscripts for originality, validity and significance to help
editors determine whether the manuscript should be published in
their journal.
For further information please contact:
LungLife AI, Inc. www.lunglifeai.com
Paul Pagano, CEO Via Walbrook PR
David Anderson, CFO
Investec Bank plc (Nominated Adviser Tel: +44 (0)20 7597 5970
& Broker)
Virginia Bull / Cameron MacRitchie
/ Lydia Zychowska
Goodbody (Joint Broker) Tel: +44 (0) 20 3841 6202 / +353 (1)
667 0420
Tom Nicholson / Stephen Kane
Walbrook PR Limited Tel: +44 (0)20 7933 8780 or LungLifeAI@walbrookpr.com
Stephanie Cuthbert / Alice Woodings Mob: 07980 541 893 / 07407 804 654 /
/ Phillip Marriage 07867 984 082
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(R) 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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