Calculated indices of volatile organic compounds (VOCs) in exhalation for lung cancer screening and early detection. (April 2021)

• Record Type: Journal Article
• Authors: Chen, Xing; Muhammad, Kanhar Ghulam; Madeeha, Channa; Fu, Wei; Xu, Linxin; Hu, Yanjie; Liu, Jun; Ying, Kejing; Chen, Liying; Yurievna, Gorlova Olga
• Is Part Of: Lung cancer. Volume 154(2021)
• Page Start: 197
• Page End: 205
• Publication Date: 2021-04
• Subjects: VOC Volatile organic compounds -- LC Lung Cancer -- TD-GC–MS Thermal Desorption-Gas Chromatography-Mass Spectrometry -- BPN Benign pulmonary disease -- HC Healthy controls -- NSCLC non-small cell lung carcinoma -- SCLC Small cell lung carcinoma -- ROC Receiver operator characteristics -- AUC Area under curve -- LDCT Low-Dose Computerized Tomography -- EMR Electronic medical record -- CI Confidence interval -- COPD Chronic obstructive pulmonary disease; Breath analysis -- VOC markers -- Lung cancer -- TD-GC–MS; Lungs -- Cancer -- Periodicals; Lung Neoplasms -- Abstracts; Lung Neoplasms -- Periodicals; Poumons -- Cancer -- Périodiques; Lungs -- Cancer; Periodicals; Electronic journals; Electronic journals; 616.99424
• DOI: 10.1016/j.lungcan.2021.02.006
• Languages: English
• ISSNs: 0169-5002

Abstract

Highlights: Breath Analysis is promising no invasive technique. VOC profile Help distinguish early stage lung cancer from advanced stage lung cancer. VOC profile helps distinguish early stage lung cancer from benign pulmonary nodules and healthy controls. Abstract: Background: Breath analysis is a promising noninvasive technique that offers a wide range of opportunities to facilitate early diagnosis of lung cancer (LC). Method: Exhaled breath samples of 352 subjects including 160 with lung cancer (LC), 70 with benign pulmonary nodule (BPN) and 122 healthy controls (HC) were analyzed through thermal desorption coupled with gas chromatography-mass spectrometry (TD-GC–MS) to obtain the metabolic information from volatile organic compounds (VOCs). Statistical classification models were used to find diagnostic clusters of VOCs for the discrimination of HC, BPN and LC patients' early and advanced stages, as well as subtypes of LC. Receiver operator characteristics (ROC) curves with 5-fold validations were used to evaluate the accuracy of these models. Results: The analysis revealed that 20, 19, 19, and 20 VOCs discriminated LC from HC, LC from BPN, histology and LC stages respectively. The calculated diagnostic indices showed a large area under the curve (AUC) to distinguish HC from LC (AUC: 0.987, 95 % confidence interval (CI): 0.976−0.997), BPN from LC (AUC: 0.809, 95 % CI: 0.758−0.860), NSCLC from SCLC (AUC: 0.939, 95 % CI: 0.875−0.995) and Stage III from stage III-IV (AUC: 0.827, 95 % CI: 0.768−0.886). The comparison between the high-risk groups (BPN and HC smokers) and early stages LC resulted in the AUC of 0.756 (95 %CI: 0.681−0.817) for BPN vs. early stage LC and AUC of 0.986 (95 % CI: 0.972−0.994) for HC smoker vs. early stage LC. Conclusion: Volatome of breath of the LC patients was significantly different from that of both BPN patients and HC and showed an ability of distinguishing early from advance stage LC and NSCLC from SCLC. We conclude that the volatome has a potential to help improve early diagnosis of LC.