FDG PET/CT for prognostic stratification of patients with metastatic breast cancer treated with first line systemic therapy: Comparison of EORTC criteria and PERCIST.

AIM: Evaluate response and predict prognosis of patients with newly diagnosed metastatic breast cancer treated with first line systemic therapy using European Organization for Research and Treatment of Cancer (EORTC) criteria and PET Response Criteria in solid Tumours (PERCIST). METHODS: From December 2006 to August 2013, 57 women with newly diagnosed metastatic breast cancer were retrospectively evaluated. FDG-PET/CT was performed within one month before treatment and repeated after at least 3 cycles of treatment. Metabolic response evaluation was evaluated by two readers according to both EORTC criteria and PERCIST, classifying the patients into 4 response groups: complete metabolic response (CMR), partial metabolic response (PMR), stable metabolic disease (SMD), and progressive metabolic disease (PMD). RESULTS: With EORTC criteria, 22 patients had CMR, 17 PMR, 6 SMD and 12 PMD. With PERCIST, 20 patients had CMR, 15 PMR, 10 SMD and 12 PMD. There was agreement between EORTC and PERCIST in 84% of the patients. By log-rank analysis, metabolic response evaluated with both EORTC criteria and PERCIST was able to predict overall survival (p = 0.028 and 0.002 respectively). CMR patient group had longer median OS than patients in the combined PMR+SMD+PMD group (60 vs 26 months both with EORTC and PERCIST; p = 0.009 and 0.006 respectively). By multivariate analysis, CMR either with EORTC or PERCIST remained an independent predictor of survival. CONCLUSION: Metabolic response evaluation with EORTC criteria and PERCIST gave similar prognostic stratification for metastatic breast cancer treated with a first line of systemic therapy.

Breast Cancer Blood Flow and Metabolism on Dual-Acquisition 18F-FDG PET: Correlation with Tumor Phenotype and Neoadjuvant Chemotherapy Response.

Early changes in tumor glucose metabolism (SUVmax) and in tumor blood flow (BF) have been evaluated separately for monitoring breast cancer response to neoadjuvant chemotherapy (NAC). This study used a single 18F-FDG dual-acquisition PET examination to simultaneously assess these two imaging features and to determine whether they correlate with the same pretherapy tumor phenotypic features and whether they are comparable or complementary in predicting pathologic complete response (pCR). Methods: This prospective study included 150 women with breast cancer and an indication for NAC. A 2-min chest-centered dynamic PET acquisition was performed at the time of 18F-FDG injection, followed by a delayed static PET acquisition 90 min later. Tumor BF was calculated from the dynamic acquisition using a validated first-pass model, and tumor SUVmax was calculated from the delayed acquisition. This dual acquisition was repeated after the first cycle of NAC to measure early changes in tumor BF and SUVmaxResults: A weak correlation was found between SUVmax and BF at baseline (r = 0.22; P = 0.006). A high baseline SUVmax was associated with all biologic markers of tumor aggressiveness, including the triple-negative breast cancer subtype (P < 0.0001). In contrast, a high baseline BF was associated only with obesity (P = 0.002). The change in SUVmax (mean, -44.6% ± 27.4%) varied depending on the Scarff-Bloom-Richardson grade, overexpression of human epidermal growth factor receptor 2 (HER2-positive), and lack of hormone receptor expression (P = 0.04, P < 0.001, and P = 0.01, respectively). BF (mean change, -26.9% ± 54.3%) showed a drastic reduction only in HER2-positive subtypes (-58.7% ± 30.0%), supporting the antiangiogenic effect of trastuzumab. Changes in SUVmax outperformed changes in BF for predicting pCR in all tumor subtypes: the areas under the curve for change in SUVmax were 0.82, 0.65, and 0.90 in the triple-negative, HER2-positive, and luminal subtypes, respectively. Conclusion: Of the two biologic hallmarks of cancer evaluated in this study, a reduction in tumor glucose metabolism was more accurate than a reduction in tumor BF for predicting pCR in the different subtypes of breast cancer.