{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T20:49:33Z","timestamp":1775076573432,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T00:00:00Z","timestamp":1575331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81671850"],"award-info":[{"award-number":["81671850"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The electronic nose (e-nose) system is a newly developing detection technology for its advantages of non-invasiveness, simple operation, and low cost. However, lung cancer screening through e-nose requires effective pattern recognition frameworks. Existing frameworks rely heavily on hand-crafted features and have relatively low diagnostic sensitivity. To handle these problems, gated recurrent unit based autoencoder (GRU-AE) is adopted to automatically extract features from temporal and high-dimensional e-nose data. Moreover, we propose a novel margin and sensitivity based ordering ensemble pruning (MSEP) model for effective classification. The proposed heuristic model aims to reduce missed diagnosis rate of lung cancer patients while maintaining a high rate of overall identification. In the experiments, five state-of-the-art classification models and two popular dimensionality reduction methods were involved for comparison to demonstrate the validity of the proposed GRU-AE-MSEP framework, through 214 collected breath samples measured by e-nose. Experimental results indicated that the proposed intelligent framework achieved high sensitivity of 94.22%, accuracy of 93.55%, and specificity of 92.80%, thereby providing a new practical means for wide disease screening by e-nose in medical scenarios.<\/jats:p>","DOI":"10.3390\/s19235333","type":"journal-article","created":{"date-parts":[[2019,12,4]],"date-time":"2019-12-04T04:30:35Z","timestamp":1575433835000},"page":"5333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Novel Framework with High Diagnostic Sensitivity for Lung Cancer Detection by Electronic Nose"],"prefix":"10.3390","volume":"19","author":[{"given":"Binchun","family":"Lu","sequence":"first","affiliation":[{"name":"Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400030, China"}]},{"given":"Lidan","family":"Fu","sequence":"additional","affiliation":[{"name":"Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400030, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0428-1677","authenticated-orcid":false,"given":"Bo","family":"Nie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Biotechnology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China"}]},{"given":"Zhiyun","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment &amp; System Security and New Technology, Chongqing University, Chongqing 400030, China"}]},{"given":"Hongying","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Biotechnology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA A Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.jtho.2015.09.009","article-title":"The IASLC lung cancer staging project: Proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer","volume":"11","author":"Goldstraw","year":"2016","journal-title":"J. Thorac. Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.jpba.2018.10.017","article-title":"Early detection of lung cancer biomarkers through biosensor technology: A review","volume":"164","author":"Roointan","year":"2019","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"807","DOI":"10.6004\/jnccn.2018.0062","article-title":"NCCN guidelines insights: Non\u2013small cell lung cancer, version 5.2018","volume":"16","author":"Ettinger","year":"2018","journal-title":"J. Natl. Compr. Cancer Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e211S","DOI":"10.1378\/chest.12-2355","article-title":"Methods for staging non-small cell lung cancer: Diagnosis and management of lung cancer: American College of Chest Physicians evidence-based clinical practice guidelines","volume":"143","author":"Silvestri","year":"2013","journal-title":"Chest"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1038\/nnano.2009.235","article-title":"Diagnosing lung cancer in exhaled breath using gold nanoparticles","volume":"4","author":"Peng","year":"2009","journal-title":"Nat. Nanotechnol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mazzone, P.J., Obuchowski, N., Phillips, M., Risius, B., Bazerbashi, B., and Meziane, M. (2013). Lung cancer screening with computer aided detection chest radiography: Design and results of a randomized, controlled trial. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0059650"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"38","DOI":"10.3322\/canjclin.51.1.38","article-title":"American Cancer Society guidelines for the early detection of cancer: update of early detection guidelines for prostate, colorectal, and endometrial cancers: Also: Update 2001\u2014testing for early lung cancer detection","volume":"51","author":"Smith","year":"2001","journal-title":"CA A Cancer J. Clin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.trac.2018.08.020","article-title":"Sample preparation and recent trends in volatolomics for diagnosing gastrointestinal diseases","volume":"108","author":"Majchrzak","year":"2018","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"026007","DOI":"10.1088\/1752-7155\/2\/2\/026007","article-title":"Impact of sampling procedures on the results of breath analysis","volume":"2","author":"Miekisch","year":"2008","journal-title":"J. Breath Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1016\/j.snb.2017.08.057","article-title":"Analysis of volatile organic compounds in exhaled breath for lung cancer diagnosis using a sensor system","volume":"255","author":"Chang","year":"2018","journal-title":"Sens. Actuators B Chem."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.ijms.2004.08.010","article-title":"Applications of breath gas analysis in medicine","volume":"239","author":"Amann","year":"2004","journal-title":"Int. J. Mass Spectrom."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/S1387-3806(03)00212-4","article-title":"Quantification of acetonitrile in exhaled breath and urinary headspace using selected ion flow tube mass spectrometry","volume":"228","author":"Abbott","year":"2003","journal-title":"Int. J. Mass Spectrom."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"024001","DOI":"10.1088\/1752-7163\/aafc77","article-title":"Electronic-nose: A non-invasive technology for breath analysis of diabetes and lung cancer patients","volume":"13","author":"Behera","year":"2019","journal-title":"J. Breath Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gasparri, R., Sedda, G., and Spaggiari, L. (2018). The Electronic Nose\u2019s Emerging Role in Respiratory Medicine. Sensors, 18.","DOI":"10.3390\/s18093029"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.cca.2016.05.013","article-title":"VOC breath biomarkers in lung cancer","volume":"459","author":"Saalberg","year":"2016","journal-title":"Clin. Chim. Acta"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1016\/S0956-5663(03)00086-1","article-title":"Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors","volume":"18","author":"Natale","year":"2003","journal-title":"Biosens. Bioelectron."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Grassi, S., Benedetti, S., Opizzio, M., di Nardo, E., and Buratti, S. (2019). Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense). Sensors, 19.","DOI":"10.3390\/s19143225"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hannon, A., and Li, J. (2019). Solid State Electronic Sensors for Detection of Carbon Dioxide. Sensors, 19.","DOI":"10.3390\/s19183848"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"197","DOI":"10.2174\/0929867324666171004164636","article-title":"Electronic noses in medical diagnostics","volume":"26","author":"Wojnowski","year":"2019","journal-title":"Curr. Med. Chem."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.lungcan.2009.11.003","article-title":"An investigation on electronic nose diagnosis of lung cancer","volume":"68","author":"Pennazza","year":"2010","journal-title":"Lung Cancer"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.juro.2014.01.113","article-title":"Detection of prostate cancer by an electronic nose: A proof of principle study","volume":"192","author":"Roine","year":"2014","journal-title":"J. Urol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/S0956-5663(02)00078-7","article-title":"Use of an electronic nose system for diagnoses of urinary tract infections","volume":"17","author":"Pavlou","year":"2002","journal-title":"Biosens. Bioelectron."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Roine, A., Saviauk, T., Kumpulainen, P., Karjalainen, M., Tuokko, A., Aittoniemi, J., Vuento, R., Lekkala, J., Lehtim\u00e4ki, T., and Tammela, T.L. (2014). Rapid and accurate detection of urinary pathogens by mobile IMS-based electronic nose: A proof-of-principle study. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0114279"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"333","DOI":"10.3109\/03091902.2012.690015","article-title":"Evaluation of gut bacterial populations using an electronic e-nose and field asymmetric ion mobility spectrometry: Further insights into \u2019fermentonomics\u2019","volume":"36","author":"Arasaradnam","year":"2012","journal-title":"J. Med Eng. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"38643","DOI":"10.18632\/oncotarget.5938","article-title":"Detection of cancer through exhaled breath: A systematic review","volume":"6","author":"Krilaviciute","year":"2015","journal-title":"Oncotarget"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1164\/rccm.200906-0939OC","article-title":"Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma","volume":"180","author":"Fens","year":"2009","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_28","first-page":"330","article-title":"Exhaled Breath Profiles Before, During and After Exacerbation of COPD: A Prospective Follow-Up Study","volume":"16","author":"Brinkman","year":"2019","journal-title":"COPD J. Chron. Obstr. Pulm. Dis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.jaci.2007.05.043","article-title":"An electronic nose in the discrimination of patients with asthma and controls","volume":"120","author":"Dragonieri","year":"2007","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1378\/chest.09-1836","article-title":"Diagnostic performance of an electronic nose, fractional exhaled nitric oxide, and lung function testing in asthma","volume":"137","author":"Montuschi","year":"2010","journal-title":"Chest"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liao, Y.H., Wang, Z.C., Zhang, F.G., Abbod, M.F., Shih, C.H., and Shieh, J.S. (2019). Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit. Sensors, 19.","DOI":"10.3390\/s19081866"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1164\/rccm.200409-1184OC","article-title":"Detection of lung cancer by sensor array analyses of exhaled breath","volume":"171","author":"Machado","year":"2005","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.snb.2014.05.025","article-title":"Feature extraction of wound infection data for electronic nose based on a novel weighted KPCA","volume":"201","author":"Jia","year":"2014","journal-title":"Sens. Actuators B Chem."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, P., Jia, P., Qiao, S., and Duan, S. (2017). Self-taught learning based on sparse autoencoder for e-nose in wound infection detection. Sensors, 17.","DOI":"10.3390\/s17102279"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hou, B., Yang, J., Wang, P., and Yan, R. (2019). LSTM Based Auto-Encoder Model for ECG Arrhythmias Classification. IEEE Trans. Instrum. Meas.","DOI":"10.1109\/TIM.2019.2910342"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cowton, J., Kyriazakis, I., Pl\u00f6tz, T., and Bacardit, J. (2018). A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors, 18.","DOI":"10.3390\/s18082521"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"296","DOI":"10.2174\/157489310794072508","article-title":"A review of ensemble methods in bioinformatics","volume":"5","author":"Yang","year":"2010","journal-title":"Curr. Bioinform."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Ma, Y. (2012). Ensemble Machine Learning: Methods and Applications, Springer.","DOI":"10.1007\/978-1-4419-9326-7"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Okun, O. (2009). Applications of Supervised and Unsupervised Ensemble Methods, Springer.","DOI":"10.1007\/978-3-642-03999-7"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000, January 21\u201323). Ensemble methods in machine learning. Proceedings of the International Workshop on Multiple Classifier Systems, Cagliari, Italy.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0004-3702(02)00190-X","article-title":"Ensembling neural networks: Many could be better than all","volume":"137","author":"Zhou","year":"2002","journal-title":"Artif. Intell."},{"key":"ref_44","unstructured":"Johnson, D.S., and Garey, M.R. (1979). Computers and Intractability: A Guide to the Theory of NP-Completeness, WH Freeman."},{"key":"ref_45","unstructured":"Partalas, I., Tsoumakas, G., and Vlahavas, I.P. (2008, January 21\u201325). Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection. Proceedings of the ECAI 2008\u201418th European Conference on Artificial Intelligence, Patras, Greece."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.patrec.2013.01.003","article-title":"Margin-based ordered aggregation for ensemble pruning","volume":"34","author":"Guo","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neucom.2017.06.052","article-title":"Margin & diversity based ordering ensemble pruning","volume":"275","author":"Guo","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_48","unstructured":"Lazarevic, A., and Obradovic, Z. (2001, January 15\u201319). Effective pruning of neural network classifier ensembles. Proceedings of the IJCNN\u201901. International Joint Conference on Neural Networks. Proceedings (Cat. No. 01CH37222), Washington, DC, USA."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Tian, Y., Zhang, J., Chen, L., Geng, Y., and Wang, X. (2019). Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition. Sensors, 19.","DOI":"10.3390\/s19163468"},{"key":"ref_50","first-page":"503","article-title":"Heterogeneous Ensemble Pruning based on Bee Algorithm for Mammogram Classification","volume":"458","author":"Qasem","year":"2008","journal-title":"Cancer"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1038\/s41598-017-02154-9","article-title":"Lung cancer screening based on type-different sensor arrays","volume":"7","author":"Li","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_52","first-page":"1651","article-title":"Boosting the margin: A new explanation for the effectiveness of voting methods","volume":"26","author":"Schapire","year":"1998","journal-title":"Ann. Stat."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artint.2013.07.002","article-title":"On the doubt about margin explanation of boosting","volume":"203","author":"Gao","year":"2013","journal-title":"Artif. Intell."},{"key":"ref_54","unstructured":"Wang, L., Sugiyama, M., Yang, C., Zhou, Z.H., and Feng, J. (2008, January 9\u201312). On the margin explanation of boosting algorithms. Proceedings of the 21st Annual Conference on Learning Theory (COLT), Helsinki, Finland."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.asoc.2017.04.058","article-title":"Considering diversity and accuracy simultaneously for ensemble pruning","volume":"58","author":"Dai","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s10489-013-0440-x","article-title":"Intelligent churn prediction in telecom: Employing mRMR feature selection and RotBoost based ensemble classification","volume":"39","author":"Idris","year":"2013","journal-title":"Appl. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"9232","DOI":"10.3390\/s101009232","article-title":"An intelligent architecture based on field programmable gate arrays designed to detect moving objects by using principal component analysis","volume":"10","author":"Bravo","year":"2010","journal-title":"Sensors"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhao, X., Chen, Y., and Zhao, W. (2018). Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array. Sensors, 18.","DOI":"10.3390\/s18103264"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Martinez-Vernon, A.S., Covington, J.A., Arasaradnam, R.P., Esfahani, S., O\u2019connell, N., Kyrou, I., and Savage, R.S. (2018). An improved machine learning pipeline for urinary volatiles disease detection: Diagnosing diabetes. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0204425"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.chemolab.2016.07.004","article-title":"Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring","volume":"157","author":"Huerta","year":"2016","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.snb.2012.01.074","article-title":"Chemical gas sensor drift compensation using classifier ensembles","volume":"166","author":"Vergara","year":"2012","journal-title":"Sens. Actuators B Chem."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.chemolab.2013.10.012","article-title":"On the calibration of sensor arrays for pattern recognition using the minimal number of experiments","volume":"130","author":"Fonollosa","year":"2014","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ziyatdinov, A., and Perera-Lluna, A. (2014). Data simulation in machine olfaction with the R package chemosensors. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0088839"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Jian, Y., Huang, D., Yan, J., Lu, K., Huang, Y., Wen, T., Zeng, T., Zhong, S., and Xie, Q. (2017). A novel extreme learning machine classification model for e-Nose application based on the multiple kernel approach. Sensors, 17.","DOI":"10.3390\/s17061434"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.lungcan.2008.08.008","article-title":"An electronic nose in the discrimination of patients with non-small cell lung cancer and COPD","volume":"64","author":"Dragonieri","year":"2009","journal-title":"Lung Cancer"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1097\/JTO.0b013e318233d80f","article-title":"Exhaled Breath Analysis with a Colorimetric Sensor Array for the Identification and Characterization of Lung Cancer","volume":"7","author":"Mazzone","year":"2012","journal-title":"J. Thorac. Oncol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1136\/thx.2006.072892","article-title":"Diagnosis of lung cancer by the analysis of exhaled breath with a colorimetric sensor array","volume":"62","author":"Mazzone","year":"2007","journal-title":"Thorax"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.jtho.2018.01.024","article-title":"Training and validating a portable electronic nose for lung cancer screening","volume":"13","author":"Dingemans","year":"2018","journal-title":"J. Thorac. Oncol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5333\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:39:52Z","timestamp":1760189992000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5333"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,3]]},"references-count":69,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19235333"],"URL":"https:\/\/doi.org\/10.3390\/s19235333","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,3]]}}}