Effect of different segmentation algorithms on metabolic tumor volume measured on (18)F-FDG PET/CT of cervical primary squamous cell carcinoma

BACKGROUND AND PURPOSE: It is known that fluorine-18 fluorodeoxyglucose PET/computed tomography (CT) segmentation algorithms have an impact on the metabolic tumor volume (MTV). This leads to some uncertainties in PET/CT guidance of tumor radiotherapy. The aim of this study was to investigate the effect of segmentation algorithms on the PET/CT-based MTV and their correlations with the gross tumor volumes (GTVs) of cervical primary squamous cell carcinoma. MATERIALS AND METHODS: Fifty-five patients with International Federation of Gynecology and Obstetrics stage Ia∼IIb and histologically proven cervical squamous cell carcinoma were enrolled. A fluorine-18 fluorodeoxyglucose PET/CT scan was performed before definitive surgery. GTV was measured on surgical specimens. MTVs were estimated on PET/CT scans using different segmentation algorithms, including a fixed percentage of the maximum standardized uptake value (20∼60% SUV(max)) threshold and iterative adaptive algorithm. We divided all patients into four different groups according to the SUV(max) within target volume. The comparisons of absolute values and percentage differences between MTVs by segmentation and GTV were performed in different SUV(max) subgroups. The optimal threshold percentage was determined from MTV(20%)∼MTV(60%), and was correlated with SUV(max). The correlation of MTV(iterative adaptive) with GTV was also investigated. RESULTS: MTV(50%) and MTV(60%) were similar to GTV in the SUV(max) up to 5 (P>0.05). MTV(30%)∼MTV(60%) were similar to GTV (P>0.05) in the 5<SUV(max)≤10 group. MTV(20%)∼MTV(60%) were similar to GTV (P>0.05) in the 10<SUV(max)≤15 group. MTV(20%) and MTV(30%) were similar to GTV (P>0.05) in the SUV(max) of at least 15 group. MTV(iterative adaptive) was similar to GTV in both total and different SUV(max) groups (P>0.05). Significant differences were observed among the fixed percentage method and the optimal threshold percentage was inversely correlated with SUV(max). The iterative adaptive segmentation algorithm led to the highest accuracy (6.66±50.83%). A significantly positive correlation was also observed between MTV(iterative adaptive) and GTV (Pearson’s correlation r=0.87, P<0.0001). CONCLUSION: MTV(iterative adaptive) is independent of SUV(max), more accurate, and correlated with GTV. Iterative adaptive algorithm segmentation may be more suitable than the fixed percentage threshold method to estimate the tumor volume of cervical primary squamous cell carcinoma.