Computed tomography–based skeletal segmentation for quantitative PET metrics of bone involvement in multiple myeloma

Abstract

Purpose Quantifications in nuclear medicine are occasionally limited by the lack of standardization for defining volumes of interest (VOIs) on functional images. In the present article, we propose the use of computed tomography (CT)–based skeletal segmentation to determine anatomically the VOI in order to calculate quantitative parameters of fluorine 18 fluorodeoxyglucose ( 18 F-FDG) PET/CT images from patients with multiple myeloma. Methods We evaluated 101 whole-body 18 F-FDG PET/CTs of 58 patients with multiple myeloma. An initial subjective visual analysis of the PET images was used to classify the bone involvement as negative/mild, moderate, or marked. Then, a fully automated CT–based segmentation of the skeleton was performed on PET images. The maximum, mean, and SD of the standardized uptake values (SUV max , SUV mean , and SD SUV ) were calculated for bone tissue and compared with the visual analysis. Results Forty-five (44.5%), 32 (31.7%), and 24 (23.8%) PET images were, respectively, classified as negative/mild, moderate, or marked bone involvement. All quantitative parameters were significantly related to the visual assessment of bone involvement. This association was stronger for the SUV mean [odds ratio (OR): 10.52 (95% confidence interval (CI), 5.68–19.48); P < 0.0001] and for the SD SUV [OR: 5.58 (95% CI, 3.31–9.42); P < 0.001) than for the SUV max [OR: 1.01 (95% CI, 1.003–1.022); P = 0.003]. Conclusion CT–based skeletal segmentation allows for automated and therefore reproducible calculation of PET quantitative parameters of bone involvement in patients with multiple myeloma. Using this method, the SUV mean and its respective SD correlated better with the visual analysis of 18 F-FDG PET images than SUVmax. Its value in staging and evaluating therapy response needs to be evaluated.