Recognition of breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine syndrome elements based on electronic nose combined with machine learning: An observational study in a single center
- VernacularTitle:电子鼻联合机器学习对肺结节良恶性及中医证素呼气图谱辨识的单中心观察性研究
- Author:
Shiyan TAN
1
;
Qiong ZENG
2
;
Hongxia XIANG
3
;
Qian WANG
3
;
Xi FU
4
;
Jiawei HE
1
;
Liting YOU
5
;
Qiong MA
1
;
Fengming YOU
4
;
Yifeng REN
4
Author Information
1. Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, P. R. China
2. Jiangsu Provincial Military Region Xuzhou Fifth Retired Cadre Rest Center, Xuzhou, 221000, Jiangsu, P. R. China
3. Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, P. R. China 2.
4. 1. Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, P. R. China 3.TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, P. R. China
5. Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China
- Publication Type:Journal Article
- Keywords:
Pulmonary nodules;
electronic nose;
machine learning;
Traditional Chinese Medicine (TCM) syndrome elements;
breath odor map
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(02):185-193
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.
