18F-FDG-PET-based tumor delineation in cervical cancer: Threshold contouring and lesion volumes

Objective: To evaluate a semi-automated PET-image tumor segmentation algorithm for gross tumor volume (GTV) delineation in patients with locally advanced cervical cancer. Material and methods: Thirty-two patients with locally advanced cervical cancer were retrospectively evaluated. Semi-automated PET-image-based GTV delineation was applied using a previous established algorithm (GTV(2SD)) and 2 fixed threshold-based methods (GTV(40%) and GTV(50%)). GTV(2SD) was determined as the pixel with the mean value plus 2-standard deviation of the liver intensity, and GTV(40%) and GTV(50%) with 40% and 50% of the maximum tumor intensity (T-max), respectively. The derived volumes were then compared with the GTVs generated manually using MR (GTV(MR)). Results: The mean value of GTV(2SD), GTV(40%) and GTV(50%) was 85.3 cc, 16.2 cc and 24.1 cc, respectively. Good agreement was noticed between GTV(2SD) and GTV(MR) (rho = 0.88). GTV(40%) and GTV(50%) showed weaker correlation with GTV(MR) (rho = 0.68 and rho = 0.71, respectively). Conclusions: This study provides preliminary evidence that metabolic tumor volume delineation is feasible using computer-generated measurements in F-18-FDG PET images. Generation of PET-based tumor volumes is affected by the choice of threshold level used. Metabolic tumor bulk calculated using the pixel with the mean value plus 2-standard deviations of the liver intensity (GTV(2SD)) correlates better with the MR-derived tumor volumes. The method is a simple and clinically applicable approach to generate PET-derived GTV for radiation therapy planning of cervical cancer. (C) 2012 Elsevier Espana, S.L. and SEMNIM. All rights reserved.