Analysis of Exhaled Breath with Electronic Nose and

Analysis of Exhaled Breath with Electronic Nose and Diagnosis of Lung Cancer by Support Vector Machine Dr. med. Māris Bukovskis 1 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 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 1

Conflict of interests • No conflict of interests • Study was sponsored by ERAF activity 2. 1. 1. 1. 0 Project Nb. 2010/0303/2 DP/2. 1. 1. 1. 0/10/APIA/ VIAA/043/

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 201

VOC’s in exhaled breath Lung cancer sniffer dogs CBC News Aug 17, 2011 Gordon SM et al. Clin Chem 1985 Machado et al. AJRCCM 2005 Chen X et al. Cancer 2007

Functional principles of electronic nose • VOCs induce change of the sensor volume and subsequently change of electric resistance Cyranose 320 • A unique response curve combination, containing the information to allow discrimination of the different samples VOCs e- S 1 e- S 2 e- S 3 ee- S 4 S 5 e- S 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.

Methods Sampling of exhaled air • 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 total lung capacity and full exhalation with approximate flow rate 0. 25 – 0. 5 L/s into a polyethylene terephthalate plastic bag • Analysis by electronic nose device (Cyranose 320, Smith Detection, USA) within 5 minutes after breath sample collection Dragonieri S et al. J Allergy Clin Immunol 2007

Methods Satistical analysis Support vector machine (SVM) • Continuous predictors: relative maximum (Rmax), area under curve (∑ 0 -60”) and tg α 0 -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

Support Vector Machine

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

Results Cancer vs No cancer Parameters of 32 detectors Rmax, ∑ 0 -60 un tg α 0 -60 Age, Pack-years and ambient tºC Cross-validation 72. 8% Class accuracy 79. 1% Classification summary (Support Vector Machine), Cancer vs No cancer, Training/Test sample 100% SVM: Classification type 1 (C=2. 000), Kernel: Linear Number of support vectors = 219 (170 bounded) Include criteria: v 20='GF' Total Correct Incorrect Correct (%) Incorrect (%) Cancer 165 144 21 87. 3 12. 7 No cancer 170 121 49 71. 2 28. 8 Cancer No cancer Cancer 144 21 87. 3 Sensitivity No cancer 49 121 71. 2 Specificity 74. 6 85. 2 PPV NPV

Results Cancer vs No cancer Parameters of 32 detectors Rmax, ∑ 0 -60 un tg α 0 -60 Age, Pack-years and ambient tºC Cross-validation 69. 7% Class accuracy 75. 5% Classification summary (Support Vector Machine), Cancer vs No cancer, Training sample 75% Test sample 25% SVM: Classification type 1 (C=2. 000), Kernel: Linear Number of support vectors = 219 (170 bounded) Include criteria: v 20='GF' Total Correct Incorrect Correct (%) Incorrect (%) Cancer 45 40 5 88. 9 11. 1 No cancer 39 26 13 66. 7 33. 3 Cancer No cancer Cancer 40 5 88. 9 Sensitivity No cancer 13 26 66. 7 Specificity 75. 5 83. 9 PPV NPV

Results Cancer vs Control Parameters of 32 detectors Rmax, ∑ 0 -60 un tg α 0 -60 Age, Pack-years and ambient tºC Cross-validation 90. 6% Class accuracy 93. 1% Classification summary (Support Vector Machine) Cancer vs Control Training/Test sample 100% SVM: Classification type 1 (C=5. 000), Kernel: Linear Number of support vectors = 84 (39 bounded) Include criteria: v 20='GF' Total Correct Incorrect Correct (%) Incerrect (%) Cancer 166 164 2 98. 8 1. 2 Control 79 64 15 81. 0 19. 0 Cancer Control Cancer 164 2 98. 8 Sensitivity Control 15 64 81. 0 Specificity 91. 6 97. 0 PPV NPV

Results Cancer vs Control Parameters of 32 detectors Rmax, ∑ 0 -60 un tg α 0 -60 Age, Pack-years and ambient tºC Cross-validation 89. 7% Class accuracy 93. 5% Classification summary (Support Vector Machine) Cancer vs Control Training sample 75% Test sample 25% SVM: Classification type 1 (C=5. 000), Kernel: Linear Number of support vectors = 84 (39 saistīti) Include criteria: v 20='GF' Total Correct Incorrect Correct (%) Incorrect (%) Cancer 45 44 1 97. 8 2. 2 Control 16 11 5 68. 8 31. 2 Cancer Control Cancer 44 1 97. 8 Sensitivity Control 5 11 68. 8 Specificity 89. 8 91. 7 PPV NPV

Results Cancer vs Cancer + COPD vs Control Parameters of 32 detectors Rmax, ∑ 0 -60 un tg α 0 -60 Age, Pack-years and Ambient tºC Cross-validation 71. 1% Class accuracy 77. 4% Classification summary (Support Vector Machine), Cancer vs Cancer+COPD vs Control, SVM: Classification type 1 (C=2. 000), Kernel: Linear Number of support vectors = 152 (43 bounded) Include criteria: v 20='GF' Total Correct Incorrect Correct (%) Incorrect (%) Cancer 63 36 27 57. 1 42. 9 Cancer + COPD 79 79 0 100. 0 COPD 15 5 10 33. 3 66. 7 Control 78 62 16 79. 5 20. 5

Results Cancer vs Cancer + COPD vs Control Parameters of 32 detectors Rmax, ∑ 0 -60 un tg α 0 -60 Age, Pack-years and Ambient tºC Cross-validation 71. 1% Class accuracy 77. 4% Classification matrix (Support Vector Machine), Cancer vs Cancer+COPD vs Control, Training/Test sample 100% SVM: Classification type 1 (C=2. 000), Kernel: Linear, Number of support vectors = 152 (43 bounded) Prognosis (rows) x Diagnosis (columns) Cancer + COPD Control Cancer 36 26 0 1 Cancer + COPD 0 79 (!) 0 0 COPD 1 9 5 0 Control 7 9 0 62

Results Parameters of 32 detectors Rmax, ∑ 0 -60 un tg α 0 -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 Classification matrix (Support Vector Machine), Stage 1 -2, 3 and 4 Training/Test group 100% SVM: Classification type 1 (C=1. 000), Kernel: Linear, Number of support vectors = 184 (73 bounded) Prognosis (rows) x Diagnosis (columns) No cancer Stage 1 -2 Stage 3 Stage 4 No cancer 100 0 7 2 Stage 1 -2 11 3 25 1 Stage 3 9 0 40 0 Stage 4 9 7 27 3

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 • An early stage lung cancer from healthy subjects and patients with different lung diseases • Some discrimination pattern between lung cancer, patients with lung cancer and COPD, COPD and control, even in patients with combined diseases

Acknowledgements • To my colleagues and our team Prof. Immanuels Taivans 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

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