Abstract
OBJECTIVE: To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from (68)Ga-DOTA-TOC PET images in patients with neuroendocrine tumors. METHODS: Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUV(max) fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COV(L)). The RFs' correlation with volume and SUV(max) was analyzed by calculating Pearson's correlation coefficients. RESULTS: DSC mean value was 0.75 ± 0.11 (0.45-0.92) between SAEB and operators and 0.78 ± 0.09 (0.36-0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUV(max) threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUV(max) thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUV(max). CONCLUSIONS: RFs robustness to manual segmentation resulted higher in NET (68)Ga-DOTA-TOC images compared to (18)F-FDG PET/CT images. Forty percent SUV(max) thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated.