Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions.

Background: This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [F]F-FDG PET/CT images.

Methods: A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [F]F-FDG PET/CT scan before and after the treatment.