Evidence map›Paper›PMID 41205293›Full record

ArticleEBioMedicine2025

AI-driven breath biopsy from a case-control study assists in the early detection of paediatric brain tumours.

Shangzhewen Li, Zhengnan Cen, Yufan Chen, Yuerun Huang, Shanshan Dong, Yang Zhao, Xiang Li

Abstract read
PubMed Full text at PMC Publisher
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Article in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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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

7 authors.

Shangzhewen LiShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China.
Zhengnan CenShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China; Collaborative Innovation Centre for Statistical Data Engineering, Technology and Application, School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018, China; Zhejiang Key Laboratory of Solid Waste Pollution Control and Resource Utilization, School of Environmental Sciences and Engineering, Zhejiang Gongshang University, Hangzhou, 310018, China.
Yufan ChenDepartment of Paediatric Neurosurgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yuerun HuangShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China.
Shanshan DongShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China.
Yang ZhaoDepartment of Paediatric Neurosurgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: zhaoyang@xinhuamed.com.cn.
Xiang LiShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China; Institute of Eco-Chongming (IEC), Shanghai, China. Electronic address: lixiang@fudan.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPaediatric brain tumours (PBT) are among the deadliest childhood cancers, with delayed diagnosis often limiting early intervention. This study explores exhaled volatile metabolites as potential biomarkers for PBT, employing AI to identify diagnostic markers and develop a predictive risk assessment model.

methodsWe conducted a case-control study using untargeted volatile metabolomics on exhaled breath samples from 161 patients with primary paediatric brain tumours (PBT) and 140 non-tumour controls, analysed via a custom-built analysis platform. Machine learning, combined with univariate analysis, identified volatile organic compound (VOC) biomarkers linked to PBT. Diagnostic performance was evaluated using four supervised learning classifiers: Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and Random Forest (RF), and model robustness was validated in an independent internal cohort of 32 participants. Incorporating PBMCs transcriptome for multi-omics analysis to construct a gene-VOC interaction network for investigating the biological origins of VOC biomarkers. An ensemble learning model was developed to enhance risk assessment by integrating clinical indicators.

findingsSignificant differences in exhaled volatile metabolomics were observed between PBT patients and controls. A panel of 12 VOC biomarkers was identified, achieving a best performed AUC of 0.81 (95% CI: 0.72-0.91) on SVM classifier, with all AUC >0.70 for all classifiers in the discovery cohort, highlighting strong diagnostic potential. Multi-omics analysis revealed VOC alterations linked to immune dysregulation, with indole and NOX components NCF1/2 emerging as key regulatory factors. An AI-assisted risk assessment model, incorporating immune-inflammatory clinical indicators, achieved high sensitivity (0.90, 95% CI: 0.77-0.90), specificity (0.86, 95% CI: 0.76-0.90), and accuracy (0.85, 95% CI: 0.80-0.89), outperforming traditional diagnostic models by nearly 20%.

interpretationThis study underscores the potential of AI-driven analysis of exhaled volatile metabolites for identifying biomarkers of paediatric brain tumours. The VOC biomarker panel, integrated with clinical indicators, offers a valuable biomarker resource for non-invasive early diagnosis and risk stratification in PBT, highlighting the potential of breath biopsy as a promising clinical tool.

fundingThis work was supported by the National Natural Science Foundation of China (No. 22476023 and No. 22276038), the Fundamental Research Funds for the Central Universities (No. KLSB2023KF-06), AI for Science Foundation of Fudan University (No. FudanX24Al026), Agilent Research Gift (No. 4956) and the Foundation of Xinhua Hospital (No. GD202501).

Indexed as

Brain NeoplasmsEarly Detection of CancerAdolescentBiomarkers, TumorBiopsyBreath TestsCase-Control StudiesChildChild, PreschoolExhalationFemaleHumansInfantMachine LearningMaleMetabolomicsBiomarkers, TumorVolatile Organic CompoundsArtificial intelligenceBreath biomarkerExhaled volatilomicsNon-invasive detectionPaediatric brain tumoursTranscriptome analysis

Identifiers

PMID41205293
PMCPMC12790591

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.