Health / Pharma

Using big data to identify triple-negative breast, oropharyngeal, and lung cancers

19th Mar `14, 04:01 AM in Health / Pharma

Researchers at Case Western Reserve University and colleagues used “big data” analytics to predict if a patient is…

BDMS
Guest Contributor
 

Researchers at Case Western Reserve University and colleagues used “big data” analytics to predict if a patient is suffering from aggressive triple-negative breast cancer, slower-moving cancers or non-cancerous lesions with 95 percent accuracy.

If the tiny patterns they found in magnetic resonance images prove consistent in further studies, the technique may enable doctors to use an MRI scan to diagnose more aggressive cancers earlier and fast track these patients for therapy. Their work is published online in the journal Radiology at http://pubs.rsna.org/doi/full/10.1148/radiol.14131384.

The work comes just two months after senior author Anant Madabhushi and another group of researchers showed they can detect differences between persistent and treatable forms of head and neck cancers caused by exposure to human papillomavirus, with 87.5 percent accuracy. In that study, digital images were made from slides of patients’ tumors.

Next up, Madabhushi’s lab recently received a $534,000, 2-year grant from the Department of Defense to find the patterns of indolent versus aggressive cancer in the lungs. The goal is to diagnose the presence of aggressive lung cancers from CT scans alone.

“Literally, what we’re trying to do is squeeze out the information we’re not able to see just by looking at an image,” said Madabhushi, a professor of biomedical engineering at Case School of Engineering and director of the Center for Computational Imaging and Personalized Diagnostics.

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