New research advances predictive treatment for lung cancer
his imaging method allows scientists to pinpoint abnormal glucose metabolism, as well as characterize tumors accurately.
By JERUSALEM POST STAFFLung cancer maliciously inserted into image in Ben-Gurion University of the Negev study(photo credit: BGU)
Researchers at the Moffitt Cancer Center have made new advancements in predictive lung cancer treatments, developing a noninvasive method to analyze tumor mutations and determine treatment course, Medicalxpress reported Friday.The research, which was published in the academic journal Nature Communications, uses a deep learning model with positron emission tomography and computerized tomography radiomics to identify non-small cell lung cancer patients who are either sensitive to or could benefit from certain treatments. The specific type of imaging used is called 18F-FDG PET/CT, which is PET/CT scan imaging with the radiotracer 18F-Fluorodeoxyglucose, which is a type of sugar molecule. This imaging method allows scientists to pinpoint abnormal glucose metabolism, as well as characterize tumors accurately, Medicalxpress reported."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, according to Medicalxpress."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.""Prior studies have utilized radiomics as a noninvasive approach to predict EGFR mutation," said Wei Mu, Ph.D., study first author and postdoctoral fellow in the Cancer Physiology Department, according to Medicalxpress."However, compared to other studies, our analysis yielded among the highest accuracy to predict EGFR and had many advantages, including training, validating and testing the deep learning score with multiple cohorts from four institutions, which increased its generalizability.""We found that the EGFR deep learning score was positively associated with longer progression free survival in patients treated with tyrosine kinase inhibitors, and negatively associated with durable clinical benefit and longer progression free survival in patients being treated with immune checkpoint inhibitor immunotherapy," said Robert Gillies, Ph.D., chair of the Cancer Physiology Department, according to Medicalxpress."We would like to perform further studies but believe this model could serve as a clinical decision support tool for different treatments."
