LungLife AI, INC Successful validation study results for LungLB(R) (2731Y)
02 Gennaio 2024 - 8:00AM
UK Regulatory
TIDMLLAI
RNS Number : 2731Y
LungLife AI, INC
02 January 2024
LungLife AI, Inc.
(the "Company" or "LungLife")
Successful validation of LungLB(R) in multi-site, prospective
clinical study
LungLife AI (AIM: LLAI), a developer of clinical diagnostic
solutions for the early detection of lung cancer, announces the
successful validation of its LungLB(R) test for indeterminate lung
nodules from a prospective, multi-site clinical study in the
clinically important small nodules patient group.
In the study, LungLB(R) demonstrated:
-- A strong positive predictive value (PPV) of 81% in
discriminating benign from cancerous lung nodules in patients with
smaller nodules (<15 mm). Smaller nodules are the most
problematic area for early detection and represent the greatest
challenge for physicians. Current clinical standards of care
generate a 60% PPV(1) , leading to material delays in diagnosis of
deadly cancers.
-- This performance in smaller nodules, similarly demonstrated
in LungLife AI's lead-in study published in June 2023(2) ,
typically represents earlier detection capability and improved
patient outcomes and highlights the test's consistency.
-- The small nodule group in this study is of utmost importance because it is comprised of 87% "intermediate" risk nodules, which are the most challenging to evaluate and diagnose. Previous studies lack sufficient numbers of intermediate-risk nodules and is the reason why existing diagnostic tools perform poorly in this group. We believe this will also be of significant value to physicians.
-- In-line with a high percentage of intermediate risk nodules, the test also outperformed the highly-validated Mayo Risk Model nodule evaluation tool, which is a commonly used baseline comparator, with an area under the curve (AUC)(3) of 72% for LungLB(R) compared to 62% for Mayo.
-- The results were also compared to Positron emission
tomography (PET) scan, another tool often employed in nodule
evaluation clinics. LungLB(R) outperformed PET by 21% (81% vs 67%
PPV) in the small nodule group, providing physicians with a more
robust diagnostic tool in this area.
" Small lung nodules measuring less than 15 mm are often
dismissed as 'probably benign' and monitored with serial imaging to
avoid a potentially unnecessary biopsy. Yet, as the results of the
latest LungLB(R) study demonstrated, many of these nodules are
actually malignant and best managed with an immediate biopsy and
treatment initiation without delay. LungLB(R) performed remarkably
well in this validation study, warranting its consideration as a
clinical biomarker for patients presenting with indeterminate
pulmonary nodules ," said Drew Moghanaki, MD, MPH, Professor and
Chief of Thoracic Oncology at the University of California Los
Angeles (UCLA) Department of Radiation Oncology, and Scientific
Advisor to LungLife AI.
The validation study enrolled 425 patients across 17 hospital
study sites who were scheduled to receive a lung nodule biopsy, of
which 347 provided data that could be analysed. These results were
driven by a 98-patient small nodules (<15 mm) group, which
represent a major challenge to physicians practicing in lung cancer
detection and treatment. When developing a precision medicine test
it is common practice to identify a specific indicated use in order
to maximise the impact on a given patient population, which in turn
helps physicians to know exactly when to use the test. The small
nodules group is the most important indication for LungLB(R).
Paul Pagano, CEO of LungLife AI, said "The key finding from our
validation study is LungLB(R) performed strongly in participants
with smaller lung nodules. This is where physicians consistently
indicate the greatest unmet need and where currently available
tools fall short. This important clinical validation of the
LungLB(R) test is a significant milestone for our Company. While we
have identified small nodules as an early commercialisation
opportunity, in the background we will continue to optimise the
LungLB(R) test for additional indicated uses, while progressing on
the next stage of our programme."
The study results are sufficient for publication and executing
our commercial programme for LungLB(R) under our CLIA license and
New York State CLEP permit. The initial launch of LungLB(R) will be
through an Early Access Program in Q1 2024 as a Laboratory
Developed Test offered from LungLife AI's CLIA laboratory in
California and with follow-on work to support utility studies
through its network of clinical investigators and early
adopters.
(1) Lokhandwala T, et al. Costs of Diagnostic Assessment for
Lung Cancer: A Medicare Claims Analysis. Clin Lung Cancer. 2017
Jan;18(1):e27-e34. doi: 10.1016/j.cllc.2016.07.006. Epub 2016 Jul
21. PMID: 27530054.
(2)
https://bmcpulmmed.biomedcentral.com/articles/10.1186/s12890-023-02433-4
(3) AUC is a commonly used metric that demonstrates an aggregate
measure of performance and is useful in comparing diagnostic
tests.
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
& Joint Broker)
Virginia Bull / Cameron MacRitchie
/ Lydia Zychowska
Goodbody (Joint Broker) Tel: +44 (0) 20 3841 6202 / +353 (1)
Tom Nicholson / Cameron Duncan 667 0420
Walbrook PR Limited Tel: +44 (0)20 7933 8780 or LungLifeAI@walbrookpr.com
Paul McManus / Alice Woodings / Phillip Mob: 07980 541 893 / 07407 804 654 /
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
About Lung Cancer
Lung cancer is the most fatal form of cancer worldwide and early
detection is critical to achieve better outcomes. Early detection
involves the evaluation of indeterminate lung nodules, of which
there are over 1.5M identified by CT scan each year in the United
States alone. Evaluation often involves significant unnecessary
invasive procedures such as biopsy for patients with benign nodules
and long delays in potentially curative treatment for patients with
cancerous nodules that are selected for monitoring via non-invasive
imaging. LungLB(R) is intended to help with earlier diagnosis in
the evaluation process.
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