Title : Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

Pub. Date : 2021 Feb

PMID : 32063026






2 Functional Relationships(s)
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1 BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and 18F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor (EGFR) mutations. Fluorodeoxyglucose F18 epidermal growth factor receptor Homo sapiens
2 BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and 18F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor (EGFR) mutations. Fluorodeoxyglucose F18 epidermal growth factor receptor Homo sapiens