Cancer treatment with machine learning

Regina Barzilay is working with MIT students and medical doctors in an ambitious bid to revolutionize cancer care. She is relying on a tool largely unrecognized in the oncology world but deeply familiar to hers: machine learning.

Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science, was diagnosed with breast cancer in 2014. She soon learned that good data about the disease is hard to find. “You are desperate for information — for data,” she says now. “Should I use this drug or that? Is that treatment best? What are the odds of recurrence? Without reliable empirical evidence, your treatment choices become your own best guesses.”

Across different areas of cancer care — be it diagnosis, treatment, or prevention — the data protocol is similar. Doctors start the process by mapping patient information into structured data by hand, and then run basic statistical analyses to identify correlations. The approach is primitive compared with what is possible in computer science today, Barzilay says.

These kinds of delays and lapses (which are not limited to cancer treatment), can really hamper scientific advances, Barzilay says. For example, 1.7 million people are diagnosed with cancer in the U.S. 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. “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. 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.

Barzilay has struck up new research collaborations, drawn in MIT students, launched projects with doctors at Massachusetts General Hospital, and begun empowering cancer treatment with the machine learning insight that has already transformed so many areas of modern life.

Machine learning, real people

At the MIT Stata Center, Barzilay, a lively presence, interrupts herself mid-sentence, leaps up from her office couch, and runs off to check on her students.

She returns with a laugh. An undergraduate group is assisting Barzilay with a federal grant application, and they’re down to the wire on the submission deadline. The funds, she says, would enable her to pay the students for their time. Like Barzilay, they are doing much of this research for free, because they believe in its power to do good. “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.

Applying it to patient data can offer tremendous assistance to people who, as Barzilay knows well, really need the help. Today, she says, a woman cannot retrieve answers to simple questions such as: What was the disease progression for women in my age range with the same tumor characteristics?