Machine learning algorithms that combine clinical and molecular data are the “wave of the future,” experts say
A man walks into a doctor’s office for a CT scan of his gallbladder. The gallbladder is fine but the doctor notices a saclike pocket of fluid on the man’s pancreas. It’s a cyst that may lead to cancer, the doctor tells him, so I’ll need to cut it out to be safe.
It’ll take three months to recover from the surgery, the doctor adds—plus, there’s a 50 percent chance of surgical complications, and a 5 percent chance the man will die on the table.
An estimated 800,000 patients in the United States are incidentally diagnosed with pancreatic cysts each year, and doctors have no good way of telling which cysts harbor a deadly form of cancer and which are benign. This ambiguity results in thousands of unnecessary surgeries: One study found that up to 78 percent of cysts for which a patient was referred to surgery ended up being not cancerous.
Now there’s a machine learning algorithm that could help. Described today in the journal Science Translational Medicine, surgeons and computer scientists at Johns Hopkins University have built a test called CompCyst (for comprehensive cyst analysis) that is significantly better than today’s standard-of-care—a.k.a. doctor observations and medical imaging—at predicting whether patients should be sent home, monitored, or undergo surgery.