Defining the optimal segmentation method for measuring somatostatin receptor expressing tumor volume on 68Ga-DOTATATE positron emission tomography/computed tomography to predict prognosis in patients with gastroenteropancreatic neuroendocrine tumors.

Abstract

We aimed to compare different segmentation methods used to calculate prognostically valuable volumetric parameters, somatostatin receptor expressing tumor volume (SRETV), and total lesion somatostatin receptor expression (TLSRE), measured by 68Ga-DOTATATE PET/CT and to find the optimal segmentation method to predict prognosis. Images of 34 patients diagnosed with gastroenteropancreatic neuroendocrine tumor (GEPNET) who underwent 68Ga-DOTATATE PET/CT imaging were reanalyzed. Four different threshold-based methods (fixed relative threshold method, normal liver background threshold method, fixed absolute standardized uptake value (SUV) threshold method, and adaptive threshold method) were used to calculate SRETV and TLSRE values. SRETV of all lesions of a patient was summarized as whole body SRETV (WB-SRETV) and TLSRE of all lesions of a patient was computed as whole body TLSRE (WB-TLSRE). WB-SRETVs calculated with all segmentation methods were statistically significantly associated with progression-free survival except WB-SRETVat which was calculated using adaptive threshold method. The fixed relative threshold methods calculated by using 45% (WB-SRETV45%) and 60% (WB-SRETV60%) of the SUV value as threshold respectively, were found to have statistically significant highest prognostic value (C-index = 0.704, CI = 0.622-0.786, P = 0.007). Among WB-TLSRE parameters, WB-TLSRE35%, WB-TLSRE40%, and WB-TLSRE50% had the highest prognostic value (C-index = 0.689, CI = 0.604-0.774, P = 0.008). The fixed relative threshold method was found to be the most effective and easily applicable method to measure SRETV on pretreatment 68Ga-DOTATATE PET/CT to predict prognosis in GEPNET patients. WB-SRETV45% (cutoff value of 11.8 cm3) and WB-SRETV60% (cutoff value of 6.3 cm3) were found to be the strongest predictors of prognosis in GEPNET patients.