Molecular subtype classification of breast cancer using established radiomic signature models based on 18F-FDG PET/CT images.

ABSTRACT

Backgrounds To evaluate the predictive power of 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) derived radiomics in molecular subtype classification of breast cancer (BC). Methods: A total of 273 primary BC patients who underwent a 18F-FDG PET/CT imaging prior to any treatment were included in this retrospective study, and the values of five conventional PET parameters were calculated, including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The ImageJ 1.50i software and METLAB package were used to delineate the contour of BC lesions and extract PET/CT derived radiomic features reflecting heterogeneity. Then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select optimal subsets of radiomic features and establish several corresponding radiomic signature models. The predictive powers of individual PET parameters and developed PET/CT derived radiomic signature models in molecular subtype classification of BC were evaluated by using receiver operating curves (ROCs) analyses with areas under the curve (AUCs) as the main outcomes. Results: All of the three SUV parameters but not MTV nor TLG were found to be significantly underrepresented in luminal and non-triple (TN) subgroups in comparison with corresponding non-luminal and TN subgroups. Whereas, no significant differences existed in all the five conventional PET parameters between human epidermal growth factor receptor 2+ (HER2+) and HER2- subgroups. Furthermore, all of the developed radiomic signature models correspondingly exhibited much more better performances than all the individual PET parameters in molecular subtype classification of BC, including luminal vs. non-luminal, HER2+ vs. HER2-, and TN vs. non-TN classification, with a mean value of 0.856, 0.818, and 0.888 for AUC. Conclusions: PET/CT derived radiomic signature models outperformed individual significant PET parameters in molecular subtype classification of BC.