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 (18F-FDG) PET/CT images from patients with multiple myeloma. Methods We 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. 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 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]. 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 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.

99mTc-sestamibi SPECT/CT and 18F-FDG-PET/CT have similar performance but different imaging patterns in newly diagnosed multiple myeloma

“…This is at least partially explained by the fact that bone marrow is part of the normal biodistribution of FDG [ 28 , 29 ]. Quantitative methods have been proposed to overcome this issue, but still with some complexity for routine use [ 30 , 31 ]. Bone marrow is not part of the normal biodistribution of MIBI [ 28 ] and, as suggested by the present study, it seems to be a good alternative for assessing bone marrow involvement by multiple myeloma.…”

Bone Metastases Are Measurable: The Role of Whole-Body MRI and Positron Emission Tomography

“…Total tumor burden analyses can be combined with specific organ segmentation either based on PET or CT, e.g. for spleen (for lymphoma) and bone, to generate organ-specific tumor burden (181,182). Furthermore, the segmentation leading to total tumor burden or organ-specific tumor burden can also be used as a mask to determine specific radiomic features, which can provide even more information for response evaluation (180).…”

Metabolic Volume Measurements in Multiple Myeloma

“…Takahashi et al [34] retrospectively assessed SUVmax, SUVmean and SD SUV in the whole skeleton of 101 MM patients using CT-based segmentation. They found that both SUVmean and SD SUV agree better than SUVmax with FDG-PET/CT visual analysis.…”

“…However, it can provide a good overview of the overall state of skeletal tumor burden, including slight increases in baseline values (Figure 2). [34,37]. (c) MIP of FDG-PET images after bone segmentation using CT.…”

“…For this patient, the skeletal SUVmean is 0.91, and its Sdsuv is 0.75. The skull is excluded because of the intense overlap of cerebral FDG uptake on the cranium [34,37]. (c) MIP of FDG-PET images after bone segmentation using CT.…”