Successful validation of LungLB® in multi-site, prospective clinical study

LungLife AI, Inc.
(the “Company” or “LungLife”)

Successful validation of LungLB® 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® test for indeterminate lung nodules from a prospective, multi-site clinical study in the clinically important small nodules patient group.

In the study, LungLB® 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% PPV¹, 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 2023², 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® 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® 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® study demonstrated, many of these nodules are actually malignant and best managed with an immediate biopsy and treatment initiation without delay. LungLB® 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®.

Paul Pagano, CEO of LungLife AI, said “The key finding from our validation study is LungLB® 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® 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® 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® under our CLIA license and New York State CLEP permit. The initial launch of LungLB® 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.

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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

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® is intended to help with earlier diagnosis in the evaluation process.