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Cancers (Basel)

Title:		Exploring Volatile Organic Compounds in Breath for High-Accuracy Prediction of Lung Cancer
Author(s):		Tsou PH; Lin ZL; Pan YC; Yang HC; Chang CJ; Liang SK; Wen YF; Chang CH; Chang LY; Yu KL; Liu CJ; Keng LT; Lee MR; Ko JC; Huang GH; Li YK;
Address:		"Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan. Institute of Statistics, National Yang Ming Chiao Tung University, Hsin-Chu 30010, Taiwan. Center for Emergent Functional Matter Science, National Yang Ming Chiao Tung University, Hsin-Chu 30010, Taiwan. Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsin-Chu 30010, Taiwan"
Journal Title:		Cancers (Basel)
Year:		2021
Volume:		20210321
Issue:		6
Page Number:		-
DOI:		10.3390/cancers13061431
ISSN/ISBN:		2072-6694 (Print) 2072-6694 (Electronic) 2072-6694 (Linking)
Abstract:		"(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants' exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction"
Keywords:		Sift-ms XGBoost breath analysis lung cancer machine learning volatile organic compounds;
Notes:		"PubMed-not-MEDLINETsou, Ping-Hsien Lin, Zong-Lin Pan, Yu-Chiang Yang, Hui-Chen Chang, Chien-Jen Liang, Sheng-Kai Wen, Yueh-Feng Chang, Chia-Hao Chang, Lih-Yu Yu, Kai-Lun Liu, Chia-Jung Keng, Li-Ta Lee, Meng-Rui Ko, Jen-Chung Huang, Guan-Hua Li, Yaw-Kuen eng Switzerland 2021/04/04 Cancers (Basel). 2021 Mar 21; 13(6):1431. doi: 10.3390/cancers13061431"