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Overcoming methodological barriers in electronic nose clinical studies, a simulation data-based approach.
Milou L M van Riswijk1, Bastiaan F M van Tintelen2, Ruben H Lucas2
1Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, The Netherlands.
Journal of Breath Research
|April 30, 2025
Summary
Disease prevalence significantly impacts electronic nose (e-nose) study sample sizes. Lower prevalence requires substantially more data for reliable neoplasia screening, highlighting the need for careful study design.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Oncology
Background:
- Non-invasive screening for neoplasia using volatile organic compounds (VOCs) analyzed by electronic nose (e-nose) technology shows promise.
- Limited guidance exists for clinical study design, particularly concerning machine learning's impact on sample size requirements.
Purpose of the Study:
- To evaluate how neoplasia prevalence, data augmentation, and the number of e-nose devices influence sample size needs for accurate prediction models.
- To provide data-driven insights for optimizing e-nose study designs in clinical settings.
Main Methods:
- Simulated e-nose breath test data based on real-world data.
- Analysis of varying neoplasia prevalence (5%-50%) and data augmentation effects on model performance.
- Development of prediction models using single value decomposition, random forest, and convolutional neural networks.
Main Results:
- Lower neoplasia prevalence drastically increases sample size requirements; 5% prevalence necessitates up to five times more data than 50% prevalence for stable model performance.
- Model performance varied across different e-nose devices, with multi-device integration requiring larger sample sizes.
- Approximately 400 data points per device at 50% prevalence and 2100 data points at 5% prevalence were needed for stable performance.
Conclusions:
- Neoplasia prevalence and the number of e-nose devices are critical factors determining sample size requirements.
- Minimizing device variability and ensuring adequate case/control samples per device are essential for reliable predictive performance in e-nose studies.
