Evidence map›Paper›PMID 33598849›Full record

ArticleEJNMMI physics2021

Evaluation of SUVlean consistency in FDG and PSMA PET/MR with Dixon-, James-, and Janma-based lean body mass correction.

Jun Zhao, Qiaoyi Xue, Xing Chen, Zhiwen You, Zhe Wang, Jianmin Yuan, Hui Liu, Lingzhi Hu

Abstract read
In one paragraph

Article in EJNMMI physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. The tumour sink effect onCancer imaging : the official publication of the International Cancer Imaging Society · 2025
    Observational
  3. Article
  4. Influences on PET Quantification and Interpretation.Diagnostics (Basel, Switzerland) · 2022
    Review
  5. Dosing Therapeutic Radiopharmaceuticals in Obese Patients.International journal of molecular sciences · 2022
    Review
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jun Zhao *Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China. petcenter@126.com.ORCID http://orcid.org/0000-0002-9887-5512
Qiaoyi Xue *Central Research Institute, United Imaging Healthcare Group, Shanghai, China.
Xing ChenDepartment of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Zhiwen YouDepartment of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Zhe WangCentral Research Institute, United Imaging Healthcare Group, Shanghai, China.
Jianmin YuanCentral Research Institute, United Imaging Healthcare Group, Shanghai, China.
Hui LiuCentral Research Institute, United Imaging Healthcare Group, Shanghai, China.
Lingzhi HuCentral Research Institute, United Imaging Healthcare Group, Shanghai, China.

Funding

Key Specialty Construction Project of Pudong Health and Family Planning Commission of Shanghai PWZzk2017-24Project of Science and Technology Commission of Shanghai Municipality 19DZ1930703
6 · The paper itself

Abstract

purposeTo systematically evaluate the consistency of various standardized uptake value (SUV) lean body mass (LBM) normalization methods in a clinical positron emission tomography/magnetic resonance imaging (PET/MR) setting.

methodsSUV of brain, liver, prostate, parotid, blood, and muscle were measured in 90

resultsCompared to DXA, the Dixon approach presented the least bias in LBM/weight% than James and Janma models (bias 0.4±7.3%, - 8.0±9.4%, and - 3.3±8.3% respectively). SUV normalized by body weight (SUVbw) was positively correlated with body mass index (BMI) for both FDG (e.g., liver: r = 0.45, p < 0.001) and PSMA scans (r = 0.20, p = 0.31), while SUV normalized by lean body mass (SUVlean) revealed a decreased dependency on BMI (r = 0.22, 0.08, 0.14, p = 0.04, 0.46, 0.18 for Dixon, James, and Janma models, respectively). The liver SUVbw of obese/overweight patients was significantly larger (p < 0.001) than that of normal patients, whereas the bias was mostly eliminated in SUVlean. One-way ANOVA showed significant difference (p < 0.001) between SUVlean in major organs measured using Dixon method vs James and Janma models.

conclusionSignificant systematic variation was found using different approaches to calculate SUVlean. A consistent correction method should be applied for serial PET/MR scans. The Dixon method provides the most accurate measure of LBM, yielding the least bias of all approaches when compared to DXA.

Indexed as

Lean body massPET/MRSUV

Identifiers

PMID33598849
PMCPMC7889776

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.