SUV calculation in breast cancer: which normalization should be applied when using 18F-FDG PET?

Abstract

When using 18F-FDG PET, glucose metabolism quantification is affected by various factors. We aimed to investigate the benefit of different standardized uptake value (SUV) normalizations to improve the accuracy of 18F-FDG uptake to predict breast cancer aggressiveness and response to treatment. Two hundred fifty-two women with locally advanced breast cancer treated with neoadjuvant chemotherapy (NAC) were included. Women underwent 18F-FDG PET before and after the first course of NAC. Glucose serum levels, patient heights and weights were recorded at the time of each PET exam. Four different procedures for SUV normalization of the primary tumor were used: by body weight (SUVBW) by blood glucose level (SUVG), by lean body mass (SUL) and then corrected for both lean body mass and blood glucose level (SULG). At baseline, SUL was significantly lower than SUVBW (5.9±4.0 and 9.5±6.5, respectively; P<0.0001), whereas SUVG and SUVBW were not significantly different (9.7±6.4 and 9.5±6.5, respectively; P=0.67). Concerning SUV changes (ΔSUV), the different normalizations methods did not induce significant quantitative differences. The correlation coefficients were high between the four normalizations methods of SUV1, SUV2 and ΔSUVB (R>0.95; P<0.0001). High baseline SUVBW measures were positively correlated with the biological tumor characteristics of aggressiveness and proliferation (P<0.001): ductal carcinoma, high tumor grading, high mitotic activity, negative estrogen receptor status and the TNBC subtype. ΔSUVBW was highly predictive of pCR (AUC=0.76 on ROC curve analysis; P<0.0001). The different SUV normalizations yields identical statistical results and AUC to predict tumor biological aggressiveness and response to therapy. In the present setting, SUVBW and SUL can be considered as robust measures and be used in future multicenter trials. The additional normalization of SUV by glycemia involves stringent methodologic procedures to avoid biased risk measurements and offers no statistical advantages.