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“We need treatment insights from the other 97 percent receiving cancer care,” she says.

To be clear: Barzilay isn’t looking to up-end the way current clinical research is conducted.

Photo: Lillie Paquette/School of Engineering Computer scientist Regina Barzilay is working with MIT students and medical doctors in an ambitious bid to revolutionize cancer care.

She just believes that doctors and biologists — and patients — could benefit if she and other data scientists lent them a helping hand.

Innovation is needed and the tools are there to be used.

Jointly with Lehman and graduate student Nicolas Locascio, Barzilay is applying deep learning for automating analysis of mammogram data.

As the first step, they are aiming to compute density and other scores currently derived by radiologists who manually analyze these images.

Next she wants to incorporate treatment outcomes into it.

For another study, Barzilay has developed a database that Hughes and his team can use to monitor the development of atypias, which help identify which patients are at risk of developing cancer later in life.For example, 1.7 million people are diagnosed with cancer in the U. every year, but only about 3 percent enroll in clinical trials, according to the American Society of Clinical Oncology.Current research practice relies exclusively on data drawn from this tiny fraction of patients.“In all my years at MIT I have never seen students get so excited about the research and volunteer so much of their time,” Barzilay says.At the center of Barzilay’s project is machine learning, or algorithms that learn from data and find insights without being explicitly programmed where to look for them.This tool, just like the ones Amazon, Netflix, and other sites use to track and predict your preferences as a consumer, can make short work of gaining insight into massive quantities of data.

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