Multiparametric Integrated 18F-FDG PET/MRI-Based Radiomics for Breast Cancer Phenotyping and Tumor Decoding

Simple Summary Breast cancer is considered the leading cancer type and main cause of cancer death in women. In this study, we assess simultaneous F-18-FDG PET/MRI of the breast as a platform for comprehensive radiomics analysis for breast cancer subtype. The radiomics-based analysis comprised prediction of molecular subtype, hormone receptor status, proliferation rate and lymphonodular and distant metastatic spread. Our results demonstrated high accuracy for multiparametric MRI alone as well as F-18-FDG PET/MRI as an imaging platform for high-quality non-invasive tissue characterization. Background: This study investigated the performance of simultaneous F-18-FDG PET/MRI of the breast as a platform for comprehensive radiomics analysis for breast cancer subtype analysis, hormone receptor status, proliferation rate and lymphonodular and distant metastatic spread. Methods: One hundred and twenty-four patients underwent simultaneous F-18-FDG PET/MRI. Breast tumors were segmented and radiomic features were extracted utilizing CERR software following the IBSI guidelines. LASSO regression was employed to select the most important radiomics features prior to model development. Five-fold cross validation was then utilized alongside support vector machines, resulting in predictive models for various combinations of imaging data series. Results: The highest AUC and accuracy for differentiation between luminal A and B was achieved by all MR sequences (AUC 0.98; accuracy 97.3). The best results in AUC for prediction of hormone receptor status and proliferation rate were found based on all MR and PET data (ER AUC 0.87, PR AUC 0.88, Ki-67 AUC 0.997). PET provided the best determination of grading (AUC 0.71), while all MR and PET analyses yielded the best results for lymphonodular and distant metastatic spread (0.81 and 0.99, respectively). Conclusion: F-18-FDG PET/MRI enables comprehensive high-quality radiomics analysis for breast cancer phenotyping and tumor decoding, utilizing the perks of simultaneously acquired morphologic, functional and metabolic data.