Association of 18F-FDG PET/CT Radiomics with Tumor Molecular Features in Breast Cancer

Objectives: F-FDG) positron emission tomography (PET)/computed tomography (CT) images of patients with breast cancer and molecular markers including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2) and Ki-67. Methods: F-FDG PET/CT images of 162 patients with histopathologically confirmed breast cancer. In order to examine the association between each molecular marker and radiomic features, machine learning models were developed seperately using the Python software. After the data were divided into test and training subsets, features were selected and scaled. Using the selected features, five different machine learning models (Random Forest, XGBoost, Support Vector Machine, Logistic Regression and Naive Bayes) were established and their performance was evaluated based on accuracy, sensitivity, specificity, F1 score, balanced accuracy, MCC and area under the curve (AUC) the receiver operating characteristic curve. Results: For HER-2 status, the model yielded an AUC of 0.76, a balanced accuracy of 0.76, a sensitivity of 62.5% and a specificity of 90.0%. For ER, PR and Ki-67 status, AUC values were 0.59, 0.55, 0.60, balanced accuracies were 0.65, 0.61, 0.62, sensitivities were 96.6%, 81.8%, 68.4% and specificities were 33.3%, 40.0%, 55.2% respectively. Conclusion: F-FDG PET/CT images demonstrated poor or non-discriminatory performance in the assessment of ER, PR and Ki-67 status. Although a preliminary signal of an association was observed for HER-2 status, this finding should be considered hypothesis-generating only due to the low number of HER-2-positive cases in the test subset and the lack of independent external validation. In larger cohorts prospective, multicenter studies are needed to validate this potential association.