A team of scientists at MIT and the Dana-Farber Cancer show off the development of OncoNPC that predicts where a patient’s cancer came from in their body—information that can help determine more effective treatment decisions for patients and caregivers.
—there are also more hopeful signs that powerful machine learning algorithms and humans can coexist. One good example comes from medicine, where AI is already being used to help diagnose and identify diseases to help doctors streamline treatment for patients.
In roughly 3 to 5 percent of all cancer cases, the source of a malignant tumor can’t be determined by traditional diagnostic means like radiology assessments. This results in a diagnosis of cancers of unknown primary and greatly limits the treatment options for the patients. The model successfully predicted the origins of the cancers with roughly 80 percent accuracy. It was also able to make high-confidence predictions that were 95 percent accurate. The team then tested it on a set of about 900 CUP tumors. They discovered that it was able to make high-confidence predictions for 40 percent of this set.
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