Hybrid 18FFDG PET/MR Imaging Parameters for the Prediction of Tissue Biomarkers in Invasive Ductal Breast Cancer

Breast cancer (BC) requires the evaluation of tumor aggressiveness features to guide treatment decisions. Biopsy-derived prognostic information may differ from surgical histopathology due to tumor heterogeneity. Hybrid PET/MRI can provide additional information for tumor characterization, supporting initial therapy planning and prognosis. In this work, we acquired 157 BC patients using a hybrid PET/MRI scanner. The PET data were combined with ADC and semi-quantitative DCE-MRI metrics to derive “hybrid PET/MRI parameters.” Pathological data such as tumor grade, hormone receptors, proliferation index (Ki67), and surrogate molecular subtype were collected, and we evaluated their associations with hybrid imaging, also comparing with the PET and MRI data analyzed separately. Ki67 showed moderate correlations with PET, ADCmin, and most hybrid parameters. The PET and hybrid data differentiate histopathological factors, while ADCmin differentiates G1 vs. G2 and luminal A vs. luminal B. In the ROC analysis, hybrid SUVmax/ADCmin shows better performance to predict luminal B from luminal A (AUC 0.720, sensitivity 73.1%, specificity 63.2%, PPV 54.3%, NPV 79.7%) than SUVmean alone. Our findings suggest that these novel hybrid PET/MRI parameters may help the characterization of tumor tissue in IDC. However, a multivariate analysis is needed to confirm our preliminary results.