Evidence map›Paper›PMID 40982187›Full record

ArticleAnnals of nuclear medicine2026

The prognostic value of

Kun-Han Lue, Yung-Hsuan Wang, Yu-Hung Chen, Sung-Chao Chu, Chih-Bin Lin, Shu-Hsin Liu

Abstract read
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In one paragraph

Article in Annals of nuclear medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Assessing Respiratory Motion Stability of NovelDiagnostics (Basel, Switzerland) · 2026
    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

6 authors.

Kun-Han LueDepartment of Medical Imaging and Radiological Sciences, Tzu Chi University, Hualien, Taiwan.
Yung-Hsuan WangDepartment of Internal Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.
Yu-Hung ChenDepartment of Medical Imaging and Radiological Sciences, Tzu Chi University, Hualien, Taiwan. jedimasterchen@hotmail.com.ORCID http://orcid.org/0000-0002-9227-0731
Sung-Chao ChuSchool of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan.
Chih-Bin LinDepartment of Internal Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.
Shu-Hsin LiuDepartment of Medical Imaging and Radiological Sciences, Tzu Chi University, Hualien, Taiwan.

Funding

Buddhist Tzu Chi Medical Foundation TCMF-A 107-01-02(114)National Science and Technology Council 113-2314-B-303 -018 -MY2
6 · The paper itself

Abstract

objectiveTo evaluate the prognostic values of

methodsWe retrospectively analyzed 249 patients with lung adenocarcinoma who underwent pre-treatment

resultsTotal sphericity and primary tumor inverse difference normalized were independent predictors of both PFS and OS. The total metabolic tumor volume was another independent predictor of OS. Combined models integrating these imaging biomarkers with clinical variables outperformed the traditional staging system (c-indices for PFS: 0.649 versus 0.550 for analog and 0.668 versus 0.583 for digital PET cohorts; for OS: 0.694 versus 0.562 for analog and 0.728 versus 0.579 for digital PET cohorts). Our models showed consistent predictive values across subgroups based on sex, EGFR mutation subtype, and clinical stage.

conclusionsOur results indicate that models integrating whole-body

Indexed as

Adenocarcinoma of LungFluorodeoxyglucose F18Lung NeoplasmsMolecular Targeted TherapyPositron-Emission TomographyWhole Body ImagingAdultAgedAged, 80 and overErbB ReceptorsFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesEGFR protein, humanErbB ReceptorsFluorodeoxyglucose F1818F-FDG PETEGFRLung adenocarcinomaTarget therapyWhole-body feature

Identifiers

PMID40982187

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.