New Research Shows EarlySign's AI Model More Accurate for Early Diagnosis of Non-small Cell Lung Cancer
Peer-Reviewed Study Demonstrates the Potential to Help Prevent lung Cancer Deaths Through Early Detection
TEL AVIV, Israel, May 26, 2021 /PRNewswire/ -- Medial EarlySign (earlysign.com), a pioneering company developing AI-based clinical data solutions for early detection and prevention of high-burden diseases, announced today the publication of new research impacting the early diagnosis of non-small cell lung cancer (NSCLC). Together with researchers from Kaiser Permanente Southern California, the Department of Health Systems Science from Kaiser Permanente Bernard J. Tyson School of Medicine, and the Department of Health Sciences, Brock University, St. Catharines, ON, Canada, study authors found that EarlySign's machine-learning model was more accurate for early diagnosis of NSCLC than either standard eligibility criteria for screening or the modified PLCOm2012, demonstrating the potential to help prevent lung cancer deaths through early detection.
The peer-reviewed retrospective data study, Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data, was published in the American Thoracic Journal, "American Journal of Respiratory and Critical Care Medicine."
The rationale for the study is that most lung cancers are diagnosed at an advanced stage while pre-symptomatic identification of high-risk individuals can prompt earlier intervention and improve long-term outcomes. The objective was to develop a model to predict a future diagnosis of lung cancer based on routine clinical and laboratory data, using machine-learning.
Results of the study indicated that based on clinical characteristics and laboratory testing performed 9-12 months before a clinical diagnosis of cancer, the EarlySign model was able to identify lung cancer with a sensitivity and specificity of 40.3% and 95%, respectively, with a positive test result indicating a 13-fold elevation in the odds of lung cancer. With further validation and refinement, this model has the potential to help prevent lung cancer deaths through earlier diagnosis.
"Lung cancer is the leading cancer killer of both men and women in the US with over 150,000 deaths expected each year," commented Michael K. Gould, MD, MS, Professor of Health System Science from Kaiser Permanente Bernard J. Tyson School of Medicine. "Earlier identification of high-risk individuals has the potential to improve lung cancer survival rates by finding the disease at a localized stage when it is more likely to be curable. The machine learning models from EarlySign can help advance lung cancer identification by nine to twelve months which can lead to earlier diagnosis and treatment, when it matters the most."
"The recent pandemic has led to significant delay of diagnosis and treatment across the board, with delays in screening meaning that cancers may be more advanced and with more serious consequences", said Eran Choman, Vice President of Clinical Research at EarlySign "the collaborative efforts with the research team have been extraordinary in revealing how advanced AI predictive modeling can increase the predictive power of a model that could have a significant beneficial impact leading to additional early diagnosis and treatment of this serious disease."
"EarlySign is now seeking to harness these results to further establish the value of this model to partner with Providers, Payers and Life Sciences and augment the identification of Lung Cancer and thus the treatment and better outcome for patients." Said Ori Geva, co-founder and CEO at EarlySign.
About Medial EarlySign
Medial EarlySign helps healthcare stakeholders keep patients healthier longer with early detection and prevention of high-burden diseases. Their software solutions derive actionable and personalized clinical insights from health data. EarlySign's AlgoMarkers and AI-based solutions can help clients identify and prioritize patient when interventions stand more chance of halting or preventing the serious complications from the onset of disease. The predictive algorithmic models developed using the company's machine learning purpose-built platform and development approach are supported by peer-reviewed research published by internationally recognized health organizations and hospitals. Founded in 2013, Medial EarlySign is headquartered in Tel Aviv, Israel. For more information, please visit: https://earlysign.com/.
Follow Medial EarlySign on LinkedIn: Medial EarlySign and Twitter: @MedialEarlySign
EarlySign Contact:
Ori Geva, Co-founder and CEO
[email protected]
Media Contact:
Darrell Atkin
[email protected]
+1.760.390.6036
SOURCE Medial EarlySign
Share this article