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Harmonization of standard uptake values across different positron emission tomography/computed tomography systems and different reconstruction algorithms: validation in oncology patients

BACKGROUND: EQ.PET is a software package that overcomes the reconstruction-dependent variation of standard uptake values (SUV). In this study, we validated the use of EQ.PET for harmonizing SUVs between different positron emission tomography/computed tomography (PET/CT) systems and reconstruction al...

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Detalles Bibliográficos
Autores principales: Song, Yufei, Meng, Xiangxi, Cao, Zhen, Zhao, Wei, Zhang, Yan, Guo, Rui, Zhou, Xin, Yang, Zhi, Li, Nan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017904/
https://www.ncbi.nlm.nih.gov/pubmed/36920590
http://dx.doi.org/10.1186/s40658-023-00540-z
Descripción
Sumario:BACKGROUND: EQ.PET is a software package that overcomes the reconstruction-dependent variation of standard uptake values (SUV). In this study, we validated the use of EQ.PET for harmonizing SUVs between different positron emission tomography/computed tomography (PET/CT) systems and reconstruction algorithms. METHODS: In this retrospective study, 49 patients with various cancers were scanned on a Biograph mCT (mCT) or Gemini TF 16 (Gemini) after [(18)F]FDG injections. Three groups of patient data were collected: Group 1, patients scanned on mCT or Gemini with data reconstructed using two parameters; Group 2, patients scanned twice on different PET scanners (interval between two scans, 68.9 ± 41.4 days); and Group 3, patients scanned twice using mCT with data reconstructed using different algorithms (interval between two scans, 109.5 ± 60.6 days). The SUVs of the lesions and background were measured, and the tumor-to-background ratios (TBRs) were calculated. In addition, the consistency between the two reconstruction algorithms and confounding factors were evaluated. RESULTS: In Group 1, the consistency of SUV and TBR between different reconstruction algorithms improved when the EQ.PET filter was applied. In Group 2, by comparing ΔSUV, ΔSUV%, ΔTBR, and ΔTBR% with and without the EQ.PET, the results showed significant differences (P < 0.05). In Group 3, Bland–Altman analysis of ΔSUV with EQ.PET showed an improved consistency relative to that without EQ.PET. CONCLUSIONS: EQ.PET is an efficient tool to harmonize SUVs and TBRs across different reconstruction algorithms. Patients could benefit from the harmonized SUV, ΔSUV, and ΔSUV% for therapy responses and follow-up evaluations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-023-00540-z.