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_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

TAN Shiyan ¹ , ZENG Qiong ² , XIANG Hongxia ¹ , WANG Qian ¹ , FU Xi ^1,3 , HE Jiawei ¹ , YOU Liting ⁴ , MA Qiong ¹ ,  YOU Fengming ^1,3 ,  REN Yifeng ^1,3

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. TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, P. R. China;
4. Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding?author：

YOU Fengming, Email: yfmdoc@163.com; REN Yifeng, Email: ryftcm.dr@yahoo.com

Keywords：

Pulmonary nodules; electronic nose; machine learning; Traditional Chinese Medicine (TCM) syndrome elements; breath odor map

DOI：

10.7507/1007-4848.202407045

Video：

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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.

Citation： TAN Shiyan, ZENG Qiong, XIANG Hongxia, WANG Qian, FU Xi, HE Jiawei, YOU Liting, MA Qiong, YOU Fengming, REN Yifeng. 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. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(2): 185-193. doi: 10.7507/1007-4848.202407045 Copy

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1. 中國醫藥教育協會肺癌醫學教育委員會, 中國胸外科肺癌聯盟, 中國抗癌協會腫瘤消融治療專業委員會, 等. 多發磨玻璃結節樣肺癌多學科診療中國專家共識(2024年版). 中華內科雜志, 2024, 63(2): 153-169.Chinese Medical Education Association Lung Cancer Medical Education Committee, China Lung Cancer Coalition in Thoracic Surgery, Chinese Anti-Cancer Association Tumor Ablation Therapy Professional Committee, et al. Chinese expert consensus on multidisciplinary diagnosis and treatment of multiple ground-glass nodular lung cancer (2024 version). Chin J Intern Med, 2024, 63(2): 153-169.
2. 徐國厚, 黃海峽, 陳斌, 等. 單體檢中心23 695例體檢者首次胸部低劑量CT篩查結果及肺結節相關易感因素的研究. 復旦學報(醫學版), 2020, 47(5): 654-659, 668.Xu GH, Huang HX, Chen B, et al. A study on the first chest low-dose CT screening and susceptible factors of pulmonary nodules in 23 695 physical examinees in a medical examination center. Fudan Univ J Med Sci, 2020, 47(5): 654-659, 668.
3. 劉瑞, 李玥. 基于肺癌高風險人群篩查的肺結節中醫診療與管理專家共識. 中醫雜志, 2023, 64(17): 1824-1832.Liu R, Li Y. Expert consensus on diagnosis, treatment and management of lung nodules with traditional chinese medicine based on screening of high-risk people of lung cancer. J Trad Chin Med, 2023, 64(17): 1824-1832.
4. 滿君, 張曉梅, 閆宏. 四逆散合升降散治療三焦郁滯肺結節45例臨床觀察. 中華中醫藥雜志, 2020, 35(8): 4275-4277.Man J, Zhang XM, Yan H. Clinical observation of Sini Powder combined with Shengjiang Powder on 45 cases of pulmonary nodule with depression and stagnation of triple energizer. Chin J Trad Chin Med Pharm, 2020, 35(8): 4275-4277.
5. 車麗坤, 張曉梅, 李寶重, 等. 從狀態論治中危磨玻璃肺結節患者的前瞻性隨機對照臨床觀察. 中醫雜志, 2023, 64(20): 2109-2115.Che LK, Zhang XM, Li BZ, et al. Treatment of patients with mediate-risk pure ground glass pulmonary nodules based on the state theory: A prospective randomize-controlled clinical observation. J Trad Chin Med, 2023, 64(20): 2109-2115.
6. 譚可欣, 鄭佳彬, 張旭, 等. 中醫藥在肺結節全程管理中的優勢及展望. 中醫雜志, 2022, 63(14): 1388-1393.Tan KX, Zheng JB, Zhang X, et al. The advantages and the future prospects of traditional Chinese medicine in the whole process management of pulmonary nodules. J Trad Chin Med, 2022, 63(14): 1388-1393.
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Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

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

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