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New Clinical Data Research from Oxford University Provides Further Validation of Medial EarlySign’s Algorithm Platform for Identifying Risk of Colorectal Cancer

New Clinical Data Research from Oxford University Provides Further Validation of Medial EarlySign’s Algorithm Platform for Identifying Risk of Colorectal Cancer

UK-Based Study Shows ColonFlagTM Stratifies Individuals According to Colorectal Cancer Risk and Could Support Earlier Detection for Improved Prognosis

KFAR MALAL, Israel – September 25, 2017 – Medial EarlySign (www.earlysign.com), a developer of machine learning tools for data-driven medicine, today announced the results of new research with Oxford University. The study provides further validation for Medial EarlySign’s ColonFlagTM algorithm platform to identify individuals at risk of having colorectal cancer and support other approaches to early detection, including screening and active case finding.

 

The peer-reviewed study, Evaluation of a Prediction Model for Colorectal Cancer: Retrospective Analysis of 2.5 Million Patient Records, published in Cancer Medicine, evaluated Medial EarlySign’s risk stratification model for colorectal cancer. The UK research follows previous ColonFlagTM study results reported in the U.S. and Israel.

 

Patients older than 40 years of age with full blood count data were risk-stratified using ColonFlag and followed up for a diagnosis of colorectal cancer over a range of time intervals. Researchers focused on the 18 – 24 month interval prior to diagnosis, as it provides a greater opportunity to intervene and modify prognosis than shorter intervals. Secondary analysis was measured at 3-6, 6-12, 12-18, and 24-36 months before diagnosis, using the same method. The researchers also undertook a case-control analysis and a cohort study of patients undergoing full blood count testing during 2012, to estimate predictive values.

 

The study showed that the area under the curve (AUC) for the 18-24 month period was 0.776 and that performance improves as the time interval reduces. Additionally, for the population risk-scored in 2012, the positive predictive value at 99.5% specificity was 8.8% (being diagnosed within the next two years), with negative predictive value of 99.6%.

 

“Application of the Medial EarlySign algorithm to routinely collected primary care data provides an additional means of identifying those at risk of colorectal cancer,” said Dr. Tim A. Holt, Senior Clinical Research Fellow of Oxford University. “Our findings may provide a role for the risk score as a tool to assist case findings in a range of settings, and could also help target individuals who do not take up invitations for fecal occult blood test lab testing, or who refuse colonoscopy.”

 

Colorectal cancer is the fourth most common cancer in the UK, with more than 41,000 new cases in 2014, representing 11% of all new cancer. Incidence is strongly related to age and survival is heavily influenced by stage at diagnosis. However, since symptoms develop insidiously, a high proportion are diagnosed at a stage beyond surgical cure.

 

“Our proven algorithms can help the healthcare system leverage the tremendous amount of data it has at its disposal to identify those members of the population who are most likely to develop a disease,” said Ori Geva, CEO of Medial EarlySign. “This Oxford study is the latest research to verify the clinical value of ColonFlag, and demonstrates how our technology can help increase the likelihood that patients will receive the preventative care they need.”

 

In addition to ColonFlagTM, Medial EarlySign is developing a range of machine learning algorithmic tools to identify high risk patients that address a variety of outcomes, including diabetes, cardiovascular, GI disorders, cancers and other life-threatening illnesses.

 

ColonFlagTM bears the CE marking to aid in identifying individuals of the general population at increased risk of colorectal cancer for whom further evaluation is recommended. ColonFlagTM is not cleared by the FDA for commercial use in the USA.

 

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Download Oxford Study Evaluating MES Colorectal Cancer Prediction Model