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, have 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 The 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.
“The biggest translation gap in AI applications to medicine is the failure to study how to use AI to improve human clinicians, and vice versa,” said corresponding author Raymond Mak, MD, of the Brigham’s Department of Radiation Oncology. “We’re studying how to make human-AI partnerships and collaborations that result in better outcomes for patients. The benefits of this approach for patients include greater consistency in segmenting tumors and accelerated times to treatment. The clinician benefits include a reduction in mundane but difficult computer work, which can reduce burnout and increase the time they can spend with patients.”
The researchers used CT images from 787 patients to train their model to distinguish tumors from