ArticleNuclear medicine communications2020
Computed tomography-based skeletal segmentation for quantitative PET metrics of bone involvement in multiple myeloma.
Article in Nuclear medicine communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images.Radiology. Artificial intelligence · 2025Article
- Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.American journal of nuclear medicine and molecular imaging · 2024Review
- Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Review
- Modern imaging techniques for monitoring patients with multiple myeloma.Medicine and pharmacy reports · 2022Review
- Metabolic Volume Measurements in Multiple Myeloma.Metabolites · 2021Review
- Bone Metastases Are Measurable: The Role of Whole-Body MRI and Positron Emission Tomography.Frontiers in oncology · 2021Review
- 99mTc-sestamibi SPECT/CT and 18F-FDG-PET/CT have similar performance but different imaging patterns in newly diagnosed multiple myeloma.Nuclear medicine communications · 2020Article
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8 authors.
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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 (F-FDG) PET/CT images from patients with multiple myeloma.
methodsWe evaluated 101 whole-body 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 (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 F-FDG PET images than SUVmax. Its value in staging and evaluating therapy response needs to be evaluated.
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