Personalized treatment options for patients with lung cancer have come a long way in the past two decades. For patients with non-small cell lung cancer, the most common subtype of lung cancer and the leading cause of cancer-related death worldwide, two major treatment strategies have emerged: tyrosine kinase inhibitors and immune checkpoint inhibitors. However, choosing the right therapy for a non-small cell lung cancer patient isn’t always an easy decision, as biomarkers can change during therapy rendering that treatment ineffective. Moffitt Cancer Center researchers are developing a noninvasive, accurate method to analyze a patient’s tumor mutations and biomarkers to determine the best course of treatment.
In a new article published in Nature Communications, the research team demonstrates how a deep learning model using positron emission tomography/computerized tomography radiomics can identify which non-small cell lung cancer patients may be sensitive to tyrosine kinase inhibitor treatment and those who would benefit from immune checkpoint inhibitor therapy. The model uses PET/CT imaging with the radiotracer 18F-Fluorodeoxyglucose, a type of sugar molecule. Imaging with 18F-FDG PET/CT can pinpoint sites of abnormal glucose metabolism and help accurately characterize tumors.
“This type of imaging, 18F-FDG PET/CT, is widely used in determining the staging of patients with non-small cell lung cancer. The glucose radiotracer used is also known to be affected by EGFR activation and inflammation,” said Matthew Schabath, Ph.D., associate member of the Cancer Epidemiology Department. “EGFR, or epidermal growth factor receptor, is a common mutation found in non-small cell lung cancer patients. EGFR mutation status can be a predictor for treatment, as patients with an active EGFR mutation have better response to tyrosine kinase inhibitor treatment.”
For the study, the Moffitt team developed an 18F-FDG PET/CT-based deep learning model using retrospective data from non-small cell lung cancer