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

Abstract

PurposeQuantifications 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 (18F-FDG) PET/CT images from patients with multiple myeloma.MethodsWe evaluated 101 whole-body 18F-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 (SUVmax, SUVmean, and SDSUV) were calculated for bone tissue and compared with the visual analysis.ResultsForty-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 SUVmean [odds ratio (OR): 10.52 (95% confidence interval (CI), 5.68–19.48); P < 0.0001] and for the SDSUV [OR: 5.58 (95% CI, 3.31–9.42); P < 0.001) than for the SUVmax [OR: 1.01 (95% CI, 1.003–1.022); P = 0.003].ConclusionCT–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 SUVmean and its respective SD correlated better with the visual analysis of 18F-FDG PET images than SUVmax. Its value in staging and evaluating therapy response needs to be evaluated.