Evidence map›Paper›PMID 38322946›Full record

ArticleHeliyon2024

Optimizing the prognostic capacity of baseline

Ying-Ming Zhu, Pan Peng, Xin Liu, Shu-Nan Qi, Shu-Lian Wang, Hui Fang, Yong-Wen Song, Yue-Ping Liu, Jing Jin, Ning Li and 10 more

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

20 authors.

Ying-Ming ZhuDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Pan PengDepartment of Nuclear Medicine, National Cancer Center/Cancer Hospital, CAMS and PUMC, Beijing, China.
Xin LiuDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Shu-Nan QiDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Shu-Lian WangDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Hui FangDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Yong-Wen SongDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Yue-Ping LiuDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Jing JinDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Ning LiDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Ning-Ning LuDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Hao JingDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Yuan TangDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Bo ChenDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Wen-Wen ZhangDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Yi-Rui ZhaiDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Yong YangDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Bin LiangDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
Rong ZhengDepartment of Nuclear Medicine, National Cancer Center/Cancer Hospital, CAMS and PUMC, Beijing, China.
Ye-Xiong LiDepartment of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To investigate the prognostic capacity of baseline Materials and methods: Metabolic tumor volume (MTV)-based parameters were defined using RTs (41 % or 25 % of maximum standardized uptake value [SUVmax]), ATs (SUV 2.5, 3.0, 4.0, or mean liver uptake) in 133 patients. Metabolic parameters were classified into avidity-related parameters (SUVmax, mean SUV [SUVmean], standard deviation of SUV [SUVsd]), volume-related parameters (RT-MTV), and avidity- and volume-related parameters (total lesion glycolysis [TLG] and AT-MTV). The prognostic capacity of the metabolic parameters and the effects of different threshold types (RT vs. AT) were evaluated. Results: All metabolic parameters were moderately associated with prognosis. However, the area under the receiver operating characteristic curve of MTV and TLG was slightly higher than that of avidity-related parameters for predicting 5-year progression-free survival (PFS) (0.614-0.705 vs. 0.563-0.609) and overall survival (OS) (0.670-0.748 vs. 0.562-0.593). Correlations of MTV and avidity-related parameters differed between RTs (r < 0.06, Conclusion: The incorporation of volume and avidity significantly improved the prognostic capacity of PET in ENKTCL. Composite parameters that encompassed both avidity and volume were recommended.

Indexed as

Absolute thresholdFDG PET/CTMetabolic parametersNK/T-cell lymphomaPrognosisRelative threshold

Identifiers

PMID38322946
PMCPMC10844272

What OpenQuestion holds

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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.