Lung Cancer

Leading the way is key

Lung Cancer Diagnostics

LungLife AI’s LungLB® test is positioned to bring certainty into early stage diagnosis confirming for clinicians whether to refer on to biopsy where the LDCT Scan reveals suspicious nodules. Providing a liquid biopsy approach for the patient also promotes a noninvasive monitoring program. LungLife AI is also developing a series of companion diagnostics. Designed for later stage treatments, understanding likely patient therapeutic responses improves outcomes. Patient stratification is also an important component to improve performance metrics in research.

LungLife AI’s liquid biopsy testing is leading the way to more affordable and clinically actionable precision medicine strategies for lung cancer patients.

Artificial Intelligence (AI)

LungLife AI’s diagnostic technology combined with machine learning with image analysis results in workflow efficiencies and improvement in performance. This integrated solution is expected to positively support clinician decision making and ease the burden of increased testing in the healthcare system.

To expand upon the success of the LungLB® testing in increasing clinician and patient confidence in using LDCT scanning, LungLife AI will be applying machine learning algorithms to deliver an integrated high performing approach to an early detection solution in lung cancer.

These algorithms are also expected to be applied in research collaborations to promote new treatment options.

Lung Cancer Diagnostics & AI

LungLife AI’s use of machine learning and artificial intelligence enables life-saving diagnosis of lung cancer. Take a look at how Persistent’s ML algorithms have reduced analysis time by almost 70%, accelerating LungLife AI’s efforts to greatly reduce the impact of a disease that claims around 400 lives per day.

Lung Cancer Today

Early detection is key to improving lung cancer patient outcomes. The National Cancer Institute’s estimates for lung cancer in the United States for 2018 are:

About 234,030 new cases of lung cancer for year 20181

About 154,050 deaths from lung cancer, accounting for 25% of all cancer deaths1

Lack of effective early detection results in a mere 16% of patients being diagnosed at early stages of disease when cure is possible1

It has been estimated as high as 30% of patients receiving curative surgery return to the clinic with recurrence within 2-3 years2

Approximately 20% of lung cancer cases that occur in women in the U.S. and 9% of cases in men are diagnosed in never-smokers. If lung cancer in never-smokers were a separate entity, it would be in the top 10 cancers in the U.S.3

SOURCES
1Howlander N et al. SEER Cancer Statistics Review, 1975-2014, National Cancer Institute. Bethesda, MD, https://seer.caner.gov/csr/1975_2014/, based on November 2016 SEER data submission, posted to the SEER web site, April 2017.

2Schuchert MJ et al. Factors influencing recurrence following anatomic lung resection for clinical stage 1 non- small cell lung cancer. Lung Cancer 2019; 128:145-151

3https://www.statnews.com/2021/01/26/growing-share-of-lung-cancer-turning-up-in-never-smokers/

Publications

Joshua D Kuban, Shahram Tahvilian, Lara Baden, Claudia Henschke, David Yankelevitz, Daniel Leventon, Rebecca Reed, Ashley Brown, Allison Muldoon, Michael J Donovan, Paul C Pagano. Pilot Study of a Novel Liquid Biopsy Test to Discriminate Benign vs. Malignant Processes in Subjects with Indeterminate Pulmonary Nodules [Abstract]. IASLC 2020 Hot Topic Meeting Liquid Biopsy October 2-3, 2020.

Shahram Tahvilian, Chinmay Savadikar, Lara Baden, Daniel Leventon, Rebecca Reed, Ashley Brown, Michael J Donovan, Bhushan Garware, Paul C Pagano. Use of an Artificial Intelligence-Derived Algorithm for Accurate FISH Probe Detection in a Liquid Biopsy Test for Lung Cancer [Abstract]. IASLC 2020 Hot Topic Meeting Liquid Biopsy October 2-3, 2020.

Chinmay Savadikar, Shahram Tahvilian, Lara Baden, Rebecca Reed, Daniel Leventon, Paul Pagano, and Bhushan Garware. 2020. Towards Designing Accurate FISH Probe Detection using 3D U-Nets on Microscopic Blood Cell Images. In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (CoDS COMAD 2020). Association for Computing Machinery, New York, NY, USA, 282–288. DOI:https://doi.org/10.1145/3371158.3371201.

Katz RL, He W, Khanna A, Fernandez RL, Zaidi TM, Krebs M, Caraway NP, Zhang HZ, Jiang F, Spitz MR, Blowers DP, Jimenez CA, Mehran RJ, Swisher SG, Roth JA, Morris JS, Etzel CJ, El-Zein R. Genetically abnormal circulating cells in lung cancer patients: an antigen-independent fluorescence in situ hybridization-based case-control study. Clin Cancer Res. 2010 Aug 1;16(15):3976-87. doi: 10.1158/1078-0432.CCR-09-3358. Epub 2010 Jul 22. PMID: 20651054; PMCID: PMC2949278.

Katz RL, Zaidi TM, Pujara D, Shanbhag ND, Truong D, Patil S, Mehran RJ, El-Zein RA, Shete SS, Kuban JD. Identification of circulating tumor cells using 4-color fluorescence in situ hybridization: Validation of a noninvasive aid for ruling out lung cancer in patients with low-dose computed tomography-detected lung nodules. Cancer Cytopathol. 2020 Aug;128(8):553-562. doi: 10.1002/cncy.22278. Epub 2020 Apr 22. PMID: 32320527.

Amber Smith, Tanweer Zaidi, Namita Shanbhag, Duy Truong, Joshua Kuban, Ruth L. Katz. Circulating Tumor Cell Detection via a Novel FISH Assay Prior to Lung Biopsy Enables Accurate Prediction of Pulmonary Malignancy: Results of a Liquid Biopsy Study in Seventy-two Patients. Journal of the American Society of Cytopathology (2017) 6, S79eS84

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