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Analysis of Exhaled B reath with Electronic N ose and Diagnosis of Lung C ancer by Support V ector M achine.

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  1. Analysis ofExhaledBreathwithElectronicNoseandDiagnosisofLungCancerbySupportVectorMachine Dr.med. Māris Bukovskis1 2 3, Dr.biol. Gunta Strazda 1 2 3, Dr.med Uldis Kopeika 3 4, Dr.biol.Normunds Jurka 3, Dr. Ainis Pirtnieks 4, Ph.dr. Līga Balode 3, Dr. Jevgenija Aprinceva 2, Inara Kantane 5, Prof. Immanuels Taivans 1 2 3 1 Center of Lung Diseases, Pauls Stradins Clinical University Hospital, 2 Faculty of Medicine, University of Latvia, 3 Institute of Experimental and Clinical Medicine, University of Latvia, 4 Department of Thoracic Surgery, Pauls Stradins Clinical University Hospital 5 Faculty of Economics and Management, University of Latvia

  2. Conflict of interests • No conflict of interests • Study was sponsored by ERAF activity 2.1.1.1.0 Project Nb. 2010/0303/2DP/2.1.1.1.0/10/APIA/VIAA/043/

  3. Lung cancer mortality and diagnostic methods • Lung cancer causes 1.3 million deaths annually, more than the next three most common cancers (colon, breast and prostate)combined • 58 - 73% of patients with stage I lung cancer survive for 5 years • For distant tumors the 5-year survival rate is only 3.5 % • Available diagnostic methods - nonsensitive, expensive or invasive World Health Organization. Cancer Fact Sheet 2009 American Cancer Society. Cancer Facts & Figures 2012

  4. VOC’s in exhaled breath Lung cancer sniffer dogs CBC News Aug 17, 2011 Gordon SM etal. ClinChem 1985 Machadoetal. AJRCCM 2005 Chen X etal. Cancer 2007

  5. e- e- e- e- e- e- Functional principles of electronic nose • VOCs induce change of the sensor • volumeandsubsequentlychangeof • electric resistance • A unique response curve combination, • containing the information to allow • discrimination of the different samples Cyranose 320 VOCs S1 S2 S3 S4 S5 S6

  6. Objective • The aim of our study was to prove the potential of exhaled breath analysis and Support Vector Machine (SVM) to discriminate patients with: 1) lung cancer from healthy controls and other lung diseases; 2) lung cancer with or without COPD from patients with only COPD and healthy controls; 3) early stage lung cancer.

  7. Methods Samplingofexhaledair • Inspiration of VOC-filtered air by tidal breathing for 5 minutes, through T-shaped two-way non-rebreathing valve (Hans Rudolph Inc., Shawnee, USA) • Inhalation to totallungcapacityandfullexhalationwithapproximateflowrate 0.25 – 0.5 L/s into a polyethyleneterephthalateplasticbag • Analysisbyelectronicnosedevice (Cyranose 320, SmithDetection, USA) within 5 minutesafterbreathsamplecollection Dragonieri S etal. J AllergyClinImmunol 2007

  8. Methods Satistical analysis Support vector machine (SVM) • Continuouspredictors: relative maximum (Rmax), area under curve (∑0-60”) and tg α0-60” for each curve of 32 sensors • Additional predictor factors: age, smoking status (smoker, non-smoker, ex-smoker), smoking history (pack-years) and ambient temperature tº C at the moment of measurement

  9. Support Vector Machine

  10. Results Morphologically confirmed lung cancer Other diseases: COPD, pneumonia, tbc, PATE, benign tumors etc. Control – healthy volunteers, postinflammatory pneumofibrosis

  11. Results Cancer vs No cancer Parameters of 32 detectors Rmax, ∑0-60 un tg α0-60 Age, Pack-years and ambient tºC Cross-validation 72.8% Class accuracy 79.1%

  12. Results Cancer vs No cancer Parameters of 32 detectors Rmax, ∑0-60 un tg α0-60 Age, Pack-years and ambient tºC Cross-validation 69.7% Class accuracy 75.5%

  13. Results Cancer vs Control Parameters of 32 detectors Rmax, ∑0-60 un tg α0-60 Age, Pack-years and ambient tºC Cross-validation 90.6% Class accuracy 93.1%

  14. Results Cancer vs Control Parameters of 32 detectors Rmax, ∑0-60 un tg α0-60 Age, Pack-years and ambient tºC Cross-validation 89.7% Class accuracy 93.5%

  15. Results Cancer vs Cancer + COPD vs COPD vs Control Parameters of 32 detectors Rmax, ∑0-60 un tg α0-60 Age, Pack-years and Ambient tºC Cross-validation 71.1% Class accuracy 77.4%

  16. Results Cancer vs Cancer + COPD vs COPD vs Control Parameters of 32 detectors Rmax, ∑0-60 un tg α0-60 Age, Pack-years and Ambient tºC Cross-validation 71.1% Class accuracy 77.4%

  17. Results Parameters of 32 detectors Rmax, ∑0-60 un tg α0-60 Age, Pack-years and Ambient tºC Patients with post-obstructive pneumonia in cancer group and bacterial, TB and infarct pneumonia in no cancer group were excluded from analysis

  18. Conclusions Exhaled breath analysis by electronic nose using support vector pattern recognition method is able to discriminate: • Lung cancer from healthy subjects and patients with different lung diseases • Anearly stage lung cancer from healthy subjects and patients with different lung diseases • Somediscrimination pattern between lung cancer, patients with lung cancer and COPD, COPD and control, even in patients with combined diseases

  19. Acknowledgements • To mycolleaguesandourteam Prof. ImmanuelsTaivans Dr.biol. Gunta Strazda Dr. Ainis Pirtnieks Dr.med. Uldis Kopeika Dr.biol. Normunds Jurka Ph.dr. Liga Balode Doctoral student Agnese Kislina Mrs. Inara Kantane

  20. Thank You for Your Attention! How to sniff out the disease?

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