Evidence map›Paper›PMID 39333995›Full record

ArticleBMC cancer2024

Novel prediction model of early screening lung adenocarcinoma with pulmonary fibrosis based on haematological index.

Haiyang Li, Xing Fu, Mingtao Liu, Jiaxi Chen, Wenhan Cao, Zhiman Liang, Zhangkai J Cheng, Baoqing Sun

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. [Research Progress of Anti-lung Cancer Drug-related Interstitial Lung Disease].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025
    Review
  4. Article
  5. Smart nanoplatforms for early detection and immune modulation in lung cancer.Frontiers in bioengineering and biotechnology · 2025
    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

8 authors.

Haiyang Li *Department of Clinical Laboratory, StateKey Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou, 510120, China. haiyang.li@mrc-bsu.cam.ac.uk.
Xing Fu *Fudan University School of Life Sciences, Fudan University, Shanghai, 200433, China.
Mingtao Liu *Department of Clinical Laboratory, StateKey Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou, 510120, China.
Jiaxi ChenKingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, 511495, China.
Wenhan CaoDepartment of Clinical Laboratory, StateKey Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou, 510120, China.
Zhiman LiangDepartment of Clinical Laboratory, StateKey Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou, 510120, China.
Zhangkai J ChengDepartment of Clinical Laboratory, StateKey Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou, 510120, China. jasontable@gmail.com.
Baoqing SunDepartment of Clinical Laboratory, StateKey Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou, 510120, China. sunbaoqing@vip.163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer (LC), a paramount global life-threatening condition causing significant mortality, is most commonly characterized by its subtype, lung adenocarcinoma (LUAD). Concomitant with LC, pulmonary fibrosis (PF) and interstitial lung disease (ILD) contribute to an intricate landscape of respiratory diseases. Idiopathic pulmonary fibrosis (IPF) in association with LC has been explored. However, other fibrotic interrelations remain underrepresented, especially for LUAD-PF and LUAD-ILD.

methodsWe analysed data with statistical analysis from 7,137 healthy individuals, 7,762 LUAD patients, 7,955 ILD patients, and 2,124 complex PF patients collected over ten years. Furthermore, to identify blood indicators related to lung disease and its complications and compare the relationships between different indicators and lung diseases, we successfully applied the naive Bayes model for a biomarker-based prediction of diagnosis and development into complex PF.

resultsMales predominantly marked their presence in all categories, save for complex PF where females took precedence. Biomarkers, specifically AGR, MLR, NLR, and PLR emerged as pivotal in discerning lung diseases. A machine-learning-driven predictive model underscored the efficacy of these markers in early detection and diagnosis, with NLR exhibiting unparalleled accuracy.

conclusionsOur study elucidates the gender disparities in lung diseases and illuminates the profound potential of serum biomarkers, including AGR, MLR, NLR, and PLR in early lung cancer detection. With NLR as a standout, therefore, this study advances the exploration of indicator changes and predictions in patients with pulmonary disease and fibrosis, thereby improving early diagnosis, treatment, survival rate, and patient prognosis.

Indexed as

Adenocarcinoma of LungEarly Detection of CancerLung NeoplasmsAdultAgedBayes TheoremBiomarkers, TumorFemaleHumansIdiopathic Pulmonary FibrosisLung Diseases, InterstitialMachine LearningMaleMiddle AgedPrognosisPulmonary FibrosisBiomarkers, TumorBayesian modelLung adenocarcinomaMachine learningPulmonary fibrosisSerum biomarkers

Identifiers

PMID39333995
PMCPMC11438419

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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.