Introduction: Dedicated breast positron emission tomography (dbPET) is an emerging imaging technique with high spatial resolution needed to assess functionality and intra-tumor heterogeneity in primary breast lesions. DbPET imaging may further enable the use of targeted imaging agents, such as [18F]-fluoroestradiol and [68Ga]-fibroblast activated protein-α inhibitor, for improving prediction of treatment response in the neoadjuvant chemotherapy (NAC) setting. We have previously observed that [18F]-fluorodeoxyglucose (FDG) PET provides complementary information to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for characterizing triple-negative breast cancers (TNBC) (1). FDG uptake directly assesses active glucose metabolism, which reflect tumor proliferation and aggressiveness, while contrast kinetics in DCE-MRI with rapid early enhancement and delayed contrast washout are correlated to robust angiogenesis in high-grade tumors. In this study, we further examined the relationship between FDG-dbPET and MRI features in a cohort of breast cancer patients receiving NAC.
Methods: With institutional review board approval, patients with biopsy-proven locally- advanced breast cancer were imaged with breast MRI and dbPET before (T0) and after three weeks (T1) of NAC. Standard DCE-MRI was obtained using a dedicated breast coil. Patients also underwent dbPET imaging with 5 mCi of FDG at 45 minutes post-injection. Functional tumor volumes (FTV) were calculated from DCE-MRI by summing all voxels with an early percent enhancement (PE) exceeding 70% within a manually defined volume of interest (VOI). Maximum and mean PE (PEMax, PEMean) values within the VOI were also computed for analyses. Tumors were segmented in dbPET images using semi-automated, threshold-driven methods. Body weight corrected maximum and mean standardized uptake values (SUVMax, SUVMean), total lesion glycolysis (TLG), and metabolic tumor volume (MTV) were calculated for FDG-dbPET. Percent change relative to T0 (∆ = 100*(T1 – T0)/T0) was calculated for each feature. Spearman’s correlation coefficient was used to evaluate the relationship between MRI and dbPET features.
Results: Of the 16 patients enrolled in this study, 13 patients (N = 15 unique tumors) with pre- and early post-treatment MRI and dbPET were included in the analysis. 46% (6/13) of the patients in this cohort had TNBC. Our initial findings indicated that ∆PEMax and ∆SUVMax had the highest correlation (⍴ = 0.59, p = 0.022). ∆PEMax and TLG at T1 were also correlated (⍴ = 0.56, p = 0.032). Among all imaging features, ∆MTV showed the largest post-treatment difference between TNBC (-54.5%, IQR: -75.4% to 15.3%) and non-TNBC (-6.06%, IQR: -47.2% to 38.9%) groups. Among MRI features, ∆FTV exhibited the largest difference between the groups: -70.4% (IQR: -79.0 to -62.1%) in TNBC and -43.1% (IQR: -72.5 to 2.95%) in non-TNBC patients. ∆SUVMax and ∆TLG were additional dbPET features with large differences between TNBC and non-TNBC patients (Table 1).
Conclusion: Our study suggests that post-treatment ∆SUVMax and TLG provide complimentary metabolic information to angiogenic properties (∆PEMax and FTV, respectively) by MRI. Other dbPET features may provide independent information adjunct to MRI for describing primary breast tumors. Patients with TNBC exhibited larger reductions in FDG uptake values and metabolic volume than non-TNBC patients. Further studies in larger cohorts with outcome results are needed to validate these initial observations.
Table 1: Comparison of post-treatment reduction in MRI and dbPET imaging features in TNBC vs. non-TNBC patients
1. Bolouri MS, Elias SG, Wisner DJ, Behr SC, Hawkins RA, SuzukiSA, et al. Triple-Negative andNon-Triple-Negative Invasive Breast Cancer: Association between MR and Fluorine18 Fluorodeoxyglucose PET Imaging. Radiology 2013;269:354-61