Effect of different segmentation algorithms on metabolic tumor volume measured on 18F-FDG PET/CT of cervical primary squamous cell carcinoma. Issue 3 (March 2017) - Xu, Weina; Yu, Shupeng; Ma, Ying; Liu, Changping; Xin, Jun
Abstract : 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% SUVmax ) threshold and iterative adaptive algorithm. We divided all patients into four different groups according to the SUVmax within target volume. The comparisons of absolute values and percentage differences between MTVs by segmentation and GTV were performed in different SUVmax subgroups. The optimal threshold percentage was determined from MTV20% ∼MTV60%, and was correlated with SUVmax . The correlation of MTViterative adaptive with GTV was also investigated. Results: MTV50% and MTV60% were similar to GTV in the SUVmax up to 5 ( P >0.05). MTV30% ∼MTV60% were similar to GTV ( P >0.05) in the 5max ⩽10 group. MTV20% ∼MTV60% were similar to GTV ( P >0.05) in the 10max ⩽15 group. MTV20% and MTV30% were similar to GTV ( P >0.05) in the SUVmax of at least 15 group. MTViterative adaptive was similar to GTV in both total and different SUVmax groups ( P >0.05). Significant differences were observed among the fixed percentage method and the optimal threshold percentage was inversely correlated with SUVmax . The iterative adaptive segmentation algorithm led to the highest accuracy (6.66±50.83%). A significantly positive correlation was also observed between MTViterative adaptive and GTV (Pearson's correlation r =0.87, P <0.0001). Conclusion: MTViterative adaptive is independent of SUVmax, 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.