Evidence map›Paper›PMID 30941463›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2019

Prognostic value of metabolic tumour volume on baseline

Qaid Ahmed Shagera, Gi Jeong Cheon, Youngil Koh, Min Young Yoo, Keon Wook Kang, Dong Soo Lee, E Edmund Kim, Sung-Soo Yoon, June-Key Chung

Abstract read
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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 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

44 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. BMC medical imaging · 2026
    Article
  5. Article
  6. Can we obtain prognostic information from healthy tissue uptake and volume in baselineEuropean journal of nuclear medicine and molecular imaging · 2026
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  18. Predictive Value of CorrectedWorld journal of nuclear medicine · 2024
    Article
  19. Article
  20. Prognostic value ofQuantitative imaging in medicine and surgery · 2024
    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

9 authors.

Qaid Ahmed ShageraDepartment of Nuclear Medicine, Seoul National University, College of Medicine, Daehangro 101, Jongno-gu, Seoul, 03080, South Korea.
Gi Jeong CheonDepartment of Nuclear Medicine, Seoul National University, College of Medicine, Daehangro 101, Jongno-gu, Seoul, 03080, South Korea. larrycheon@gmail.com.ORCID http://orcid.org/0000-0002-1360-5186
Youngil KohCancer Research Institute and Radiation Medicine Institute, Seoul National University, Seoul, South Korea.
Min Young YooDepartment of Nuclear Medicine, Seoul National University, College of Medicine, Daehangro 101, Jongno-gu, Seoul, 03080, South Korea.
Keon Wook KangDepartment of Nuclear Medicine, Seoul National University, College of Medicine, Daehangro 101, Jongno-gu, Seoul, 03080, South Korea.
Dong Soo LeeDepartment of Nuclear Medicine, Seoul National University, College of Medicine, Daehangro 101, Jongno-gu, Seoul, 03080, South Korea.
E Edmund KimDepartment of Nuclear Medicine, Seoul National University, College of Medicine, Daehangro 101, Jongno-gu, Seoul, 03080, South Korea.
Sung-Soo YoonCancer Research Institute and Radiation Medicine Institute, Seoul National University, Seoul, South Korea.
June-Key ChungDepartment of Nuclear Medicine, Seoul National University, College of Medicine, Daehangro 101, Jongno-gu, Seoul, 03080, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe purpose of this study was to determine the prognostic value of metabolic volumetric parameters as a quantitative index on pre-treatment

methodsA total of 103 consecutive patients with DLBCL and baseline FDG PET/CT were retrospectively evaluated. Quantitative metabolic parameters, including total metabolic tumour volume (TMTV) using a standardized uptake value (SUV) of ≥2.5 as the threshold, were estimated. Receiver operating characteristic curve analysis was used to determine the optimal cut-off values for the metabolic parameters. The relationships between study variables and patient survival were tested using Cox regression analysis. Patient survival rates were derived from Kaplan-Meier curves and compared using the log-rank test.

resultsMedian follow-up was 34 months. In patients with a low TMTV (<249 cm

conclusionPretreatment TMTV was an independent predictor of survival in patients with DLBCL. Importantly, TMTV had an additive prognostic value in patients with a high-risk NCCN-IPI. Thus, the combination of baseline TMTV with NCCN-IPI may improve the prognostication and may be helpful guide the decision for intensive therapy and clinical trials, especially in DLBCL patients with a high-risk NCCN-IPI.

Indexed as

Positron Emission Tomography Computed TomographyAdolescentAdultAgedAged, 80 and overDisease-Free SurvivalDisease ProgressionFemaleFluorodeoxyglucose F18HumansKaplan-Meier EstimateLymphoma, Large B-Cell, DiffuseMaleMiddle AgedPrognosisProgression-Free SurvivalFluorodeoxyglucose F18Diffuse large B-cell lymphomaFDG PET/CTMetabolic tumour volumePrognosis

Identifiers

PMID30941463

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Registered trials

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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.