Abstract
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Researchers from the Moffitt Cancer Center report they are developing a noninvasive, method to analyze a patient's tumor mutations and biomarkers to determine the best course of treatment. The findings, published in Nature Communications, demonstrate how a deep learning model using positron emission tomography/computerized tomography (PET/CT) radiomics could differentiate between 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 researchers developed an 18F-FDG PET/CT-based deep learning model using retrospective data from non-small cell lung cancer patients at two institutions: Shanghai Pulmonary Hospital and Fourth Hospital of Hebei Medical University in China. 18F-FDG PET/CT is used in determining the staging of patients with non-small cell lung cancer.
The researchers trained the tool to predict EGFR mutation status across patient cohorts from the participating institutions. Prior research has shown that 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. “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.”