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Updated: Jan 12, 2026
Breath Collection from Children for Disease Biomarker Discovery
Published on: February 14, 2019
AI-driven breath biopsy from a case-control study assists in the early detection of paediatric brain tumours
Shangzhewen Li1, Zhengnan Cen2, Yufan Chen3
1Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China.
Insights
This study identified 12 volatile organic compound (VOC) biomarkers in exhaled breath for paediatric brain tumours (PBT). An AI model using these VOCs and clinical data achieved high accuracy for early PBT diagnosis and risk assessment.
Area of Science:
- Metabolomics
- Artificial Intelligence
- Oncology
Background:
- Paediatric brain tumours (PBT) are a leading cause of childhood cancer mortality.
- Delayed diagnosis significantly hinders effective early intervention for PBT.
- Exhaled volatile metabolites are explored as non-invasive biomarkers for PBT detection.
Purpose of the Study:
- To identify volatile organic compound (VOC) biomarkers for paediatric brain tumours (PBT) using AI.
- To develop a predictive risk assessment model for PBT utilizing identified biomarkers.
- To investigate the biological origins of VOC biomarkers through multi-omics analysis.
Main Methods:
- A case-control study involving 161 PBT patients and 140 controls using untargeted volatile metabolomics on breath samples.
- Machine learning algorithms (SVM, NB, LR, RF) applied to identify a panel of 12 VOC biomarkers.
- Multi-omics analysis incorporating PBMCs transcriptome and gene-VOC interaction networks.
- Development of an ensemble learning model integrating clinical indicators for enhanced risk assessment.
Main Results:
- A panel of 12 VOC biomarkers was identified with a maximum AUC of 0.81 (SVM classifier), demonstrating significant diagnostic potential.
- Multi-omics analysis linked VOC alterations to immune dysregulation, highlighting indole and NOX components (NCF1/2) as key factors.
- An AI-assisted risk assessment model achieved high sensitivity (0.90), specificity (0.86), and accuracy (0.85), outperforming traditional methods by nearly 20%.
Conclusions:
- AI-driven analysis of exhaled volatile metabolites shows promise for identifying PBT biomarkers.
- The VOC biomarker panel, combined with clinical data, provides a valuable non-invasive tool for early PBT diagnosis and risk stratification.
- Breath biopsy emerges as a potentially powerful clinical tool for paediatric brain tumour management.
Background:
Paediatric 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.
Methods:
We 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.
Findings:
Significant 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%.
Interpretation:
This 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.
Funding:
This 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).
