Evidence map›Paper›PMID 34589429›Full record

ArticleFrontiers in oncology2021

Qiufang Liu, Jiaru Li, Bowen Xin, Yuyun Sun, Dagan Feng, Michael J Fulham, Xiuying Wang, Shaoli Song

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05687552 (Role of 18F-FDG PET/CT in Gastric Cancer), which is not on this map. Cited by 21 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 3 pooled it
4.3field-weighted citation impact, top 5% of its field
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.

NCT05687552 unknown statusnot on this mapstarted 2023, after this paper: background citation

Role of 18F-FDG PET/CT in Gastric Cancer

TypeobservationalSponsorSohag UniversityRan2023 to 2024Enrolled50ConditionsGastric CancerArmspet -ct
3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 34 citations in OpenAlex.

  1. Pooled it
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  10. Clinical application ofJapanese journal of radiology · 2024
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  14. Prognostic Value of [Cancers · 2023
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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

8 authors at 3 institutions in 2 countries.

Qiufang LiuDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.
Jiaru LiSchool of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Bowen XinSchool of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Yuyun SunDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.
Dagan FengSchool of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Michael J FulhamSchool of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Xiuying WangSchool of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Shaoli SongDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.
University of Sydney · AUShanghai Medical College of Fudan University · CNFudan University Shanghai Cancer Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe accurate assessment of lymph node metastases (LNMs) and the preoperative nodal (N) stage are critical for the precise treatment of patients with gastric cancer (GC). The diagnostic performance, however, of current imaging procedures used for this assessment is sub-optimal. Our aim was to investigate the value of preoperative

methodsWe retrospectively collected clinical and

resultsThere were 185 patients-127 men, 58 women, with the median age of 62, and an age range of 22-86 years. One CT feature and one PET feature were selected to predict LNMs and achieved the best performance (AUC: 82.2%, accuracy: 85.2%). This radiomic model also detected some LNMs that were missed in CECT (19.6%) and

conclusionWe developed and validated two machine learning models based on the preoperative

Indexed as

gastric cancerlymph nodes metastasesN stagePET/CTradiomics

Identifiers

PMID34589429
PMCPMC8474469
OpenAlexW3199628463

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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.