Evidence map›Paper›PMID 29245926›Full record

ArticleOncotarget2017

Prognostic significance of total metabolic tumor volume on

Chin-Chuan Chang, Shih-Feng Cho, Ya-Wen Chuang, Chia-Yang Lin, Shu-Min Chang, Wen-Ling Hsu, Ying-Fong Huang

Abstract read
In one paragraph

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Article
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  20. Texture Analysis Improves the Value of PretreatmentContrast media & molecular imaging · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Chin-Chuan ChangDepartment of Nuclear Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
Shih-Feng ChoDivision of Hematology & Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
Ya-Wen ChuangDepartment of Nuclear Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
Chia-Yang LinDepartment of Nuclear Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
Shu-Min ChangDepartment of Nuclear Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
Wen-Ling HsuDepartment of Nuclear Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
Ying-Fong HuangDepartment of Nuclear Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe purpose of this study was to determine the prognostic significance of metabolic parameters on pre-treatment MATERIALS AND

methodsFrom September 2009 to December 2014, DLBCL patients who had received FDG PET/CT scans for staging were enrolled. The maximal standardized uptake value of tumor (SUVt) was recorded. The metabolic tumor volume (MTV) was the volume of lesion with an elevated SUV greater than 2.5. The total lesion glycolysis (TLG) was the sum of the products of MTV and mean SUV in all measured lesions. Univariate and multivariate analyses were used to assess the prognostic significance of maximal SUVt, total MTV, TLG and other clinical parameters.

resultsThere were 118 patients enrolled in this study. The median follow-up time was 28.7 months. The 5-year progression-free survival (PFS) for patients with higher and lower total MTV was 32.3% and 66.0% respectively (

conclusionsAn elevated total MTV was a predictor for shorter PFS and OS in patients with DLBCL receiving rituximab-containing therapy, independent of IPI.

Indexed as

diffuse large B-cell lymphomaFDG PET/CTMTVprognosisTLG

Identifiers

PMID29245926
PMCPMC5725117

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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.