ORIGINAL RESEARCH article
Front. Med.
Sec. Pulmonary Medicine
Exploratory Analysis of Exhaled Volatile Organic Compounds for Binary Discrimination Between Lung Cancer, Pneumonia, and Healthy Controls Using Machine Learning
Jing Wang 1
haitian li 1
Jianshen Yue 2
Yamei Song 1
Ning Wang 1
Wei Guo 1
Zhigang Cai 3
1. Harrison International Peace Hospital, Hengshui, China
2. Cangzhou People's Hospital, Cangzhou, China
3. The Second Hospital of Hebei Medical University, shijiazhuang, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Abstract
These results provide preliminary evidence that breath analysis could address the critical clinical challenge of differentiating radiographically similar conditions non-invasively. The presented methodology and dataset establish a foundational framework for characterizing disease-specific metabolic signatures. However, the findings remain hypothesis-generating. Definitive evaluation of clinical utility necessitates subsequent studies employing multiclass modeling, validation in independent and prospective cohorts, and direct assessment of diagnostic impact in real-world triage scenarios.
Summary
Keywords
Exhaled breath analysis, exploratory study, lung cancer, machine learning, Pneumonia, Volatile Organic Compounds
Received
07 November 2025
Accepted
04 February 2026
Copyright
© 2026 Wang, li, Yue, Song, Wang, Guo and Cai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhigang Cai
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.