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The Problem

Whether or not to Biopsy – Being on the fence about next steps in the diagnostic journey can create anxiety and fear in you and your patient and potentially stalling next steps.

And for many pulmonary nodules you’re not even sure a biopsy is the right next step; the uncertainty in those cases can be just as challenging—stalling decisions and putting both you and your patient in a holding pattern.

Lung Cancer Statistics:

  • Up to 13% of surgical lung resections from lung cancer screening programs are benign. (https://pmc.ncbi.nlm.nih.gov/articles/PMC10730375/)
  • Early detection improves survival rates significantly. (https://shorturl.at/CywEU)
  • CTB and RAB are revolutionizing the diagnosis of lung cancer, providing diagnostic yields above 80- 90%. (https://pmc.ncbi.nlm.nih.gov/articles/PMC10888048)

THE SOLUTION – LungLifeAI

LungLifeAI: Clinical Utility – More accurate assessment of pulmonary nodules.

Key Benefits:

  • Non-invasive blood-based test
  • Improves early lung cancer detection
  • Enhances clinical decision-making
  • Now included in the National Cancer Institute Early Detection Research Network as a CLIA-approved test

How LungLifeAI Works:
The Science Behind the Innovation

LungLifeAI is powered by a proprietary 4-color fluorescence in situ hybridization (FISH) assay that identifies Circulating Genetically Abnormal Cells (CGACs) in a standard blood sample. These CGACs exhibit specific chromosomal abnormalities frequently found in lung cancer cells and focus on whole-cell abnormalities that are reliably detected even in small tumors, allowing early detection of malignant transformation when imaging results are inconclusive.

FIGURE B: CGAC IDENTIFICATION

  • Illustrates the clear difference between normal and genetically abnormal cells under FISH imaging.
  • Normal cells show two signals per probe, while CGACs show multiple or missing signals, indicating genomic instability.

Utilizing the Power of AI

The LungLifeAI test uses a locked and validated machine learning-based image analysis algorithm to automatically identify Circulating Genetically Abnormal Cells (CGACs) from fluorescent imaging. To develop this classifier, this algorithm scanned thousands of cells, detecting abnormal chromosomal signal patterns, and applies predefined decision rules to classify CGACs with high precision. It minimizes human error and interobserver variability, making the test more reproducible and scalable for clinical use

Clinical Validation of LungLifeAI

A pivotal study published in BMC Pulmonary Medicine evaluated the efficacy of LungLifeAI in predicting lung cancer among individuals with IPNs. The study enrolled 151 participants scheduled for biopsy across two renowned institutions: Mount Sinai Hospital and MD Anderson Cancer Center. The primary objective was to assess the correlation between LungLifeAI results and biopsy-confirmed diagnosis.

Key Benefits:

Sensitivity and Specificity

  • Sensitivity: 77%
  • Specificity: 74%
  • Positive Predictive Value (PPV): 80%
  • Area Under the Curve (AUC): 0.78

Independence from Traditional Risk Factors

LungLifeAI’s performance was not influenced by conventional clinical and radiological factors such as smoking history, previous cancer diagnosis, lesion size, or nodule appearance, suggesting it provides unique and valuable information beyond standard assessment criteria.

Comparison with Existing Models

Notably, the Mayo Clinic Model achieved an AUC of only 0.52 within the same study cohort. Highlighting LungLifeAI’s superior diagnostic accuracy in distinguishing benign and malignant nodules.

Implications for Clinical Practice:

Complementary to Existing Diagnostic Tools

LungLifeAI excels in small nodules where the uncertainty is greater and PET performs poorly, and serves as an adjunct to imaging studies, providing additional molecular insights allowing for more informed decision making for diagnosis and management.

Early Detection of Malignancy

Enhanced sensitivity facilitates the prompt identification of malignant nodules, enabling earlier intervention and potentially improving prognosis.

Reduction of Unecessary Interventions

By accurately identifying benign nodules with a Rule In test with high PPV, LungLifeAI may help avoid invasive procedures, reducing unwarranted patient risk and healthcare costs.

Clinical Highlights

American Lung Association. State of Lung Cancer – Key Findings.

Published November 14, 2023. Accessed April 4, 2025.

https://www.lung.org/research/state-of-lung-cancer/key-findings

Case Study 1

  • Former Smoker (37.5 pack years)

  • No history of cancer

  • No underlying lung disease

  • Mayo Risk Score: 25%

  • Days LungLifeAI May Have Saved: 365

November 2018

Initial Evaluation, CT scan shows solitary subsolid 1.3 cm nodule in upper left lobe

January 2020

Biopsy negative for lung cancer

LungLifeAI test-increased risk

January 2021

Surgical resection of indeterminate nodule

Stage 1 Adenocarcinoma

Case Study 2

  • Former Smoker (78 pack years)

  • No history of cancer

  • No underlying lung disease

  • Mayo Risk Score: 47%

  • Days LungLifeAI May Have Saved: 365

October 2019

Initial Nodule Size 1.68cm in left upper lobe

February 2020

Slight increase of nodule size

March 2020

Biopsy found atypical-rare cells

LungLifeAI test-increased risk

May 2020

Nodule negative for cancer (scar tissue)

Lymph Nodes N2 & N3 Small Cell Lung Cancer

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