Telling the difference between benign and cancerous thyroid nodules before surgery is notoriously challenging, but a new study finds that a combination of artificial intelligence and data analysis techniques may yield surprisingly accurate cancer predictions.

The proof-of-concept study was conducted by researchers from the Cornell Ann S. Bowers College of Computing and Information Science and the Icahn School of Medicine at Mount Sinai.

The team trained machine learning models to identify thyroid cancer using thyroid ultrasound images and a type of modeling called topological data analysis (TDA) that captures shape and pattern information from the ultrasounds. They showed that this approach was a strong improvement over the use of only basic features of ultrasounds to predict thyroid cancer. If these findings hold true in larger studies, the approach could be combined with traditional risk assessment methods to better advise patients and possibly prevent unnecessary surgeries.

“A lot of what we're trying to do is improve our ability to counsel patients preoperatively and to counsel patients with cancer about their risk for recurrence,” said senior author Denise Lee, assistant professor of surgery at Icahn School of Medicine at Mount Sinai.

Read the full story on the Cornell Bowers website.