11C-acetate positron emission tomography is more precise than 18F-fluorodeoxyglucose positron emission tomography in evaluating tumor burden and predicting disease risk of multiple myeloma
Abstract The optimal method of tumor burden evaluation in newly diagnosed multiple myeloma (NDMM) is yet to be determined. This study aimed to compare the value of 11C-acetate positron-emission tomography (PET)/computed tomography (CT) (AC-PET and 18F-fluorodeoxyglucose PET/CT (FDG-PET) in the asses...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/1f77fa28b9db4d918610f808c0a0606d |
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| Sumario: | Abstract The optimal method of tumor burden evaluation in newly diagnosed multiple myeloma (NDMM) is yet to be determined. This study aimed to compare the value of 11C-acetate positron-emission tomography (PET)/computed tomography (CT) (AC-PET and 18F-fluorodeoxyglucose PET/CT (FDG-PET) in the assessment of tumor burden in NDMM. This study evaluated 64 NDMM patients between February 2015 and July 2018. AC-PET and FDG-PET were used to assess myeloma lesions. The clinical data, imaging results, and their correlations were analyzed. Diffuse bone marrow uptake in AC-PET was significantly correlated with biomarkers for tumor burden, including serum hemoglobin (P = 0.020), M protein (P = 0.054), the percentage of bone marrow plasma cells (P < 0.001), and the Durie–Salmon stage of the disease (P = 0.007). The maximum standard uptake value (SUVmax) of focal lesions and high diffuse bone marrow uptake in AC-PET showed stronger correlations with high-risk disease (P = 0.017, P = 0.013) than those in FDG-PET. Moreover, the presence of diffuse bone marrow uptake, more than ten focal lesions, and an SUVmax of focal lesions of > 6.0 in AC-PET, but not in FDG-PET, predicted a higher probability of disease progression and shorter progression-free survival (P < 0.05). AC-PET outperformed FDG-PET in tumor burden evaluation and disease progression prediction in NDMM. |
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