Can FDG-PET/CT predict early response to neoadjuvant chemotherapy in breast cancer?

Purpose: Neoadjuvant chemotherapy (NAC) in breast cancer is currently used not only for locally advanced tumors, but also for large operable tumors when breast preservation is considered. It also provides the opportunity to evaluate chemotherapy tumor response. Our aim was to correlate the relative change in the standardized uptake value (SUV) of (18)F-2-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET/CT) with pathologic response after NAC.

Methods: We prospectively evaluated 40 patients with invasive ductal breast carcinomas from February 2010 to December 2011. FDG-PET/CT was performed at baseline and after the second cycle of NAC. All patients underwent surgery after NAC. Pathologic response was evaluated according to Residual Cancer Burden (RCB) index.

Results: The mean age was 41.9 years. Median primary tumor size was 6 cm. Pathologic complete response (pCR) was obtained in 12 (30%) patients. The tumor baseline mean maximum SUV (SUV(max)), and after second cycle were: 8.97 (sd.4.3) and 4.07 (sd.3.2), respectively. The relative change (ΔSUV) after the second course of NAC was significantly higher for patients with pCR (-81.58%) when compared to the non-pCR patients (-40.18%) (p = 0.001). The optimal ΔSUV threshold that discriminates between pCR and non-pCR was -71.8% (83.3% sensitivity; 78.5% specificity). Moreover, the optimal ΔSUV threshold to discriminate between NAC responders and non-responders was -59.1% (68% sensitivity; 75.0% specificity).

Conclusions: Our data suggest that the FDG-PET/CT ΔSUV after the second course of NAC can predict pathological response in ductal breast carcinomas, and potentially identify a subgroup of non-responding patients for whom ineffective chemotherapy should be avoided.

Synopsis: Breast cancer is the most frequently diagnosed cancer in women. The indications for neoadjuvant chemotherapy are increasing. Early information on chemotherapy response is crucial and methods that predict the therapeutic effectiveness might avoid potentially ineffective chemotherapies in non-responding patients.