Evidence map›Paper›PMID 38800393›Full record

SynthesisFrontiers in oncology2024

Prognostic impact of interstitial lung abnormalities in lung cancer: a systematic review and meta-analysis.

Xian-Liang Tang, Yin-Bo Sun, Xiao-Tong Guo, Sheng-Zhao Yang, Wen-Ping Zhang

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in oncology, 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. Article
  2. Review
  3. Review
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

5 authors.

Xian-Liang TangDepartment of Thoracic Surgery, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China.
Yin-Bo SunDepartment of Thoracic Surgery, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China.
Xiao-Tong GuoDepartment of Rehabilitation, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China.
Sheng-Zhao YangDepartment of Thoracic Surgery, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China.
Wen-Ping ZhangDepartment of Thoracic Surgery, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Newly identified as a radiological concept, interstitial lung abnormalities (ILA) is emerging as a prognostic factor for lung cancer. Yet, debates persist regarding the prognostic significance of ILA in lung cancer. Our inaugural meta-analysis aimed to investigate the correlation between ILA and lung cancer outcomes, offering additional insights for clinicians in predicting patient prognosis. Methods: Articles meeting the criteria were found through PubMed, the Cochrane Library, EMBASE, and Web of Science by February 29, 2024. The outcomes evaluated were the survival rates such as overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and cancer-specific survival (CSS). Results: A total of 12 articles with 4416 patients were included in this meta-analysis. The pooled results showed that lung cancer patients with interstitial lung abnormalities had an inferior OS (n=11; HR=2.22; 95% CI=1.68-2.95; P<0.001; I Conclusion: ILA was a availability factors of prognosis in patients with lung cancers. These findings highlight the importance of early pulmonary fibrosis, namely ILA for prognosis in patients with lung cancer, and provide a partial rationale for future clinical work.

Indexed as

evidence-based medicineinterstitial lung abnormalitylung cancermeta-analysisprognosis

Identifiers

PMID38800393
PMCPMC11116699

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.