Deep learning algorithm may streamline lung cancer radiotherapy treatment

Lung cancer, the most common cancer worldwide, is targeted with radiation therapy (RT) in nearly one-half of cases. RT planning is a manual, resource-intensive process that can take days to weeks to complete, and even highly trained physicians vary in their determinations of how much tissue to target with radiation. Furthermore, a shortage of radiation-oncology practitioners and clinics worldwide is expected to grow as cancer rates increase. Brigham and Women's Hospital researchers and collaborators, working under the Artificial Intelligence in Medicine Program of Mass General Brigham, developed and validated a deep learning algorithm that can identify and outline ("segment") a non-small cell lung cancer (NSCLC) tumor on a computed tomography (CT) scan within seconds. Their research, published in Lancet Digital Health, also demonstrates that radiation oncologists using the algorithm in simulated clinics performed as well as physicians not using the algorithm, while working 65 percent more quickly.