Molecular imaging and radionuclide therapy
Association of 18F-FDG PET/CT Radiomics with Tumor Molecular Features in Breast Cancer
By Püren BA, Usta S, Salihoğlu YS and 1 others.
Abstract
Objectives
This study aims to investigate the potential relationship between radiomic features extracted from 18F-fluorodeoxyglucose (18F-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
Radiomic features were obtained via an open-source software (LIFEx) from 18F-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.