REG - LungLife AI, INC - LungLB® study published in peer-reviewed journal

LungLife AIAnnouncement

06/06/2023 07:00

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RNS Number : 7208B  LungLife AI, INC  06 June 2023

LungLife AI, Inc.

(the "Company" or "LungLife")

 

LungLB® 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® 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® 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® 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® 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® test performed equally well in current, former, and never
smokers

·   The data support potential clinical utility of LungLB® in reducing
delays in treatment, in which positive LungLB® test results were available
months ahead of lung cancer diagnosis in three highlighted cases

 

LungLB® 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
(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® 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® 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 (https://www.lunglifeai.com/)
 Paul Pagano, CEO                                        Via Walbrook PR
 David Anderson, CFO

 Investec Bank plc (Nominated Adviser & Broker)          Tel: +44 (0)20 7597 5970
 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
                                                         (mailto:LungLifeAI@walbrookpr.com)
 Stephanie Cuthbert / Alice Woodings / Phillip Marriage  Mob: 07980 541 893 / 07407 804 654 / 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® test
is designed to deliver additional information to clinicians who are evaluating
indeterminate lung nodules. For more information visit www.lunglifeai.com
(http://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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