For all the research attention paid to cancer, there’s still no foolproof way to catch it early. Now AI is homing in on tumors that doctors previously missed.
What's new: Researchers at Johns Hopkins University developed CompCyst, a machine learning model capable of diagnosing early-stage pancreatic cancer better than previous methods.
How it works: The system interprets several data types to recognize precursors to pancreatic cancer.
- The model bases its predictions on the patient’s symptoms, genetic factors, ultrasound images, and fluid taken from the pancreas.
- It was trained on data from 800 patients who had already been treated by Johns Hopkins and had their tumors analyzed after removal.
- The model cuts the misdiagnoses between 60 and 74 percent, depending on the cyst type.
Behind the news: 800,000 Americans are diagnosed with pancreatic cysts annually, but only a small fraction of those will develop a cancerous tumor. Because the pancreas is buried deep in the body, it’s often impossible to tell tiny, pre-cancerous cysts from benign lumps. Fear drives a lot of these patients to pursue very aggressive treatments.
Why it matters: About 95 percent of people diagnosed with pancreatic cancer die from it. One reason is that most patients are diagnosed very late in the disease’s progress. On the other hand, up to 5 percent of patients die from the most common pancreatic surgery, so it’s not safe to be overly cautious, either. In either case, better diagnostics could save many lives.
We’re thinking: When it comes to many cancers, early intervention can either save a life or disrupt one that wasn’t in danger. If the FDA clears CompCyst for new patients, it could open the door to a slew of similar models that make a cancer diagnosis less of a gamble.