Precision Medicine Study Highlights Role of Machine Learning

Precision Medicine Study Highlights Role of Machine Learning

Precision Medicine Study Highlights Role of Machine Learning
When it comes to the future of diagnosing and treating cancer, computers – not humans – could hold the key to delivering the best quality precision medicine.

A new study out of the Stanford University School of Medicine has found that computers can be trained to more accurately assess slides of lung cancer tissue than pathologists.

“Pathology as it is practiced now is very subjective,” said Michael Snyder, PhD, professor and chair of genetics. “Two highly skilled pathologists assessing the same slide will agree only about 60 percent of the time. This approach replaces the subjectivity with sophisticated, quantitative measurements that we feel are likely to improve patient outcomes.”

The Stanford researchers found that a machine learning approach to identifying critical disease-related features was able to accurately differentiate between two types of lung cancers – adenocarcinoma and squamous cell carcinoma – and also predict patient survival times better than pathologists, who classify tumors by grade and stage.

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The research study focused specifically on lung cancer, though researchers believe that applying a machine learning techniques to other types of cancer could also prove effective.

“Ultimately,” said Snyder, “this technique will give us insight into the molecular mechanisms of cancer by connecting important pathological features with outcome data.”

Machine learning has been at the forefront of trends in big data healthcare analytics. As computers become more powerful and analytics algorithms become smart enough to spot patterns in digital images, the process for diagnosing and treating illnesses like cancer has started to become a more data-driven practice.

Ideally, with imaging analytics, x-rays, CAT scans, or MRIs computers could be able to detect unique abnormalities in x-rays, CAT scans, or MRIs, and allow algorithms to cross-reference the data with backlogs of other stored information to provide more individualized care.

“If you had a chest x-ray, perhaps prior to surgery, the image was used to look at your lungs,” explained Carrick Carpenter, head of Dell Services’ Global Healthcare Cloud Computing division. “But it also will contain data about your spine. A computer with the right analytics software can review that image and detect your risk, if any, for osteoporosis.

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