Deep-learning model enables rapid lymphoma detection in PET/CT images

From left to right: Timothy Perk, Alison Roth, Peter Ferjančič, Robert Jeraj, Daniel Huff, Brayden Schott, Ali Deatsch, Victor Santoro Fernandes, Amy Weisman, Vince Streif. Whole-body positron emission tomography combined with computed tomography (PET/CT) is a cornerstone in the management of lymphoma (cancer in the lymphatic system). PET/CT scans are used to diagnose disease and then to monitor how well patients respond to therapy. However, accurately classifying every single lymph node in a scan as healthy or cancerous is a complex and time-consuming process. Because of this, detailed quantitative treatment monitoring is often not feasible in clinical day-to-day practice. Researchers at the University of Wisconsin-Madison have recently developed a deep-learning model that can perform this task automatically.