Evidence map›Paper›PMID 42465031›Full record

ArticleAmerican journal of cancer research2026

Application value of next generation sequencing technology for pathogen detection in patients with pulmonary infection and lung cancer.

Li Xu, Jingwen Liu, Xiaoqing An, Yanhua Wu, Xuezheng Li

Abstract read
In one paragraph

Article in American journal of cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Li XuDepartment of Bronchoscopy, Public Health Clinical Center Affiliated to Shandong University Ji'nan, Shandong, China.
Jingwen LiuDepartment of Clinical Laboratory, Public Health Clinical Center Affiliated to Shandong University Ji'nan, Shandong, China.
Xiaoqing AnDepartment of Bronchoscopy, Public Health Clinical Center Affiliated to Shandong University Ji'nan, Shandong, China.
Yanhua WuCenter for Integrative and Translational Medicine, Public Health Clinical Center Affiliated to Shandong University Ji'nan, Shandong, China.
Xuezheng LiCenter for Integrative and Translational Medicine, Public Health Clinical Center Affiliated to Shandong University Ji'nan, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to evaluate the clinical value of next-generation sequencing (NGS) in diagnosing pulmonary infection pathogens among lung cancer patients. A total of 350 lung cancer patients with pulmonary infection were retrospectively enrolled from 2022 to 2024. Sputum samples were examined by targeted next generation sequencing (tNGS) and CMT (conventional microbiological tests). The diagnostic efficacy of these two methods was compared. The tNGS positive detection rate reached 90.00%, significantly higher than 70.86% of routine tests (P<0.05). The top common pathogens included Mycobacterium tuberculosis, Candida albicans and Pseudomonas aeruginosa. tNGS presented shorter detection time and a markedly higher detection rate of mixed infections (50.86% vs. 18.57%, P<0.001). Patients with abnormal CRP or PCT levels showed distinct tNGS positive rates. The AUC of tNGS was 0.784, indicating better diagnostic accuracy than that of CMT. In conclusion, tNGS featured high positive rate, rapid detection and prominent advantages in identifying mixed infections, which is suitable for clinical etiological detection of pulmonary infection in lung cancer patients.

Indexed as

conventional microbiological testlung cancernext generation sequencingpathogenic diagnosisPulmonary infection

Identifiers

PMID42465031
PMCPMC13373565

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.