Abstract
BACKGROUND: We aimed to establish and validate 2 machine learning models using (18)F-flurodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) radiomic features to predict human epidermal growth factor receptor 2 (HER2) expression and prognosis in gastric cancer (GC) patients. METHODS: We retrospectively enrolled 90 patients diagnosed with GC, including their clinical information and the (18)F-FDG PET/CT images. Patients were allocated to a training cohort of 72 patients and an independent validation cohort (IVC) of 18 patients. There were 2,100 radiomic features extracted from the (18)F-FDG PET/CT scans. A sequential combination of multivariate and univariate feature selection was applied, including sequential forward selection and a redundancy-based analysis. The justification of the model performance was conducted by cross-validation analysis on the training set and an independent validation analysis. RESULTS: The machine learning models were developed using a balanced bagging approach for HER2 expression prediction and prognosis prediction, which differentiated HER2 positive expression from negative expression in the IVC with an area under the receiver operating characteristic curve (AUC) of 0.72, sensitivity of 0.85, and specificity of 0.80. The IVC for prognosis prediction achieved an AUC of 0.75, sensitivity of 0.82, and specificity of 0.71. We also conducted a reasonable interpretation for the selected features in each classification task from multiple aspects, including normalized feature importance analysis and statistical correlation analysis with the clinical features that were defaulted to be effective. CONCLUSIONS: (18)F-FDG PET/CT radiomics analysis with a machine learning model provides a quantitative, efficient, and objective mechanism for predicting HER2 expression and prognosis in GC patients.