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Analysis of Exhaled Breath with Analysis of Exhaled Breath with Electronic Nose and Diagnosis of Lung Electronic Nose and Diagnosis of Lung Cancer by Support Vector Machine Cancer by Support Vector Machine Dr.med. Dr.med. Māris Bukovskis Māris Bukovskis 1 2 3 1 2 3 , Dr.biol. Gunta , Dr.biol. Gunta Strazda Strazda 1 2 3 1 2 3 , Dr.med Uldis Kopeika , Dr.med Uldis Kopeika 3 4 3 4 , , Dr.biol.Normunds Jurka Dr.biol.Normunds Jurka 3 , Dr. Ainis Pirtnieks , Dr. Ainis Pirtnieks 4 , , Ph.dr. Līga Balode Ph.dr. Līga Balode 3 , Dr. Jevgenija Aprinceva , Dr. Jevgenija Aprinceva 2 , , Inara Kantane Inara Kantane 5 , Prof. Immanuels Taivans , Prof. Immanuels Taivans 1 2 3 1 2 3 1 1 Center of Lung Diseases, Pauls Stradins Clinical University Hospital, Center of Lung Diseases, Pauls Stradins Clinical University Hospital, 2 2 Faculty of Medicine, University of Latvia, Faculty of Medicine, University of Latvia, 3 3 Institute of Experimental and Clinical Medicine, University of Latvia, Institute of Experimental and Clinical Medicine, University of Latvia, 4 Department of Thoracic Surgery, Pauls Stradins Clinical University Hospital Department of Thoracic Surgery, Pauls Stradins Clinical University Hospital 5 Faculty of Economics and Management, University of Latvia Faculty of Economics and Management, University of Latvia
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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. - PowerPoint PPT Presentation
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Analysis of Exhaled Breath with Electronic Nose and Analysis of Exhaled Breath with Electronic Nose and Diagnosis of Lung Cancer by Support Vector Diagnosis of Lung Cancer by Support Vector
MachineMachineDr.med. Dr.med. Māris BukovskisMāris Bukovskis 1 2 31 2 3, Dr.biol. Gunta Strazda , Dr.biol. Gunta Strazda 1 2 31 2 3, Dr.med , Dr.med Uldis Kopeika Uldis Kopeika 3 43 4, Dr.biol.Normunds Jurka , Dr.biol.Normunds Jurka 33, Dr. Ainis Pirtnieks , Dr. Ainis Pirtnieks 44, , Ph.dr. Līga Balode Ph.dr. Līga Balode 33, Dr. Jevgenija Aprinceva , Dr. Jevgenija Aprinceva 22, Inara Kantane , Inara Kantane 55, ,
Prof. Immanuels Taivans Prof. Immanuels Taivans 1 2 31 2 3
1 1 Center of Lung Diseases, Pauls Stradins Clinical University Hospital, Center of Lung Diseases, Pauls Stradins Clinical University Hospital, 2 2 Faculty of Medicine, University of Latvia, Faculty of Medicine, University of Latvia, 3 3 Institute of Experimental and Clinical Medicine, University of Latvia, Institute of Experimental and Clinical Medicine, University of Latvia, 44 Department of Thoracic Surgery, Pauls Stradins Clinical University Hospital Department of Thoracic Surgery, Pauls Stradins Clinical University Hospital55 Faculty of Economics and Management, University of Latvia Faculty of Economics and Management, University of Latvia
Conflict of interestsConflict of interests
• No conflict of interestsNo conflict of interests
• Study was sponsored by ERAF activity 2.1.1.1.0 Study was sponsored by ERAF activity 2.1.1.1.0 Project Nb. 2010/0303/2DP/2.1.1.1.0/10/APIA/Project Nb. 2010/0303/2DP/2.1.1.1.0/10/APIA/ VIAA/043/VIAA/043/
Lung cancer mortality and diagnostic methodsLung cancer mortality and diagnostic methods
• Lung cancer causesLung cancer causes 1.3 million deaths annually, 1.3 million deaths annually, more more than the next three most common cancers (colon, breast than the next three most common cancers (colon, breast and prostate)and prostate) combinedcombined
• 58 - 73% of patients with stage I lung cancer survive for 58 - 73% of patients with stage I lung cancer survive for 5 years5 years
• For distant tumors the For distant tumors the 55-year survival rate is only 3.5 -year survival rate is only 3.5 %%
• Available diagnostic methods - nonsensitive, expensive Available diagnostic methods - nonsensitive, expensive or invasiveor invasive
World Health Organization. CancerWorld Health Organization. Cancer Fact Sheet Fact Sheet 2009 2009American Cancer Society.American Cancer Society. Cancer Facts & Figures 201 Cancer Facts & Figures 2012
VOC’s in exhaled breathVOC’s in exhaled breath
Lung cancer sniffer dogsLung cancer sniffer dogsCBC News Aug 17, 2011CBC News Aug 17, 2011
Gordon SM et al. Clin Chem 1985Gordon SM et al. Clin Chem 1985Machado et al. AJRCCM 2005Machado et al. AJRCCM 2005
Chen X et al. Cancer 2007Chen X et al. Cancer 2007
e- e- e-
e- e-e-
Functional principles of electronic noseFunctional principles of electronic nose
S1 S2 S3 S4 S5 S6
• VOCs induce change of the sensorVOCs induce change of the sensorvolume and subsequently change of volume and subsequently change of electric resistanceelectric resistance
• A unique response curve combination, A unique response curve combination, containing the information to allow containing the information to allow discrimination of the different samplesdiscrimination of the different samples
Cyranose 320Cyranose 320
VOCsVOCs
ObjectiveObjective
• The aim of our study was to prove the potential of The aim of our study was to prove the potential of exhaled breath analysisexhaled breath analysis and Support Vector Machine and Support Vector Machine (SVM)(SVM) to discriminate to discriminate patients with: patients with:
1) 1) lung cancer from healthy controls and other lung lung cancer from healthy controls and other lung diseasesdiseases;;
2) 2) lung cancer with or without COPD from patients with lung cancer with or without COPD from patients with only COPD and healthy controlsonly COPD and healthy controls;;
3) early 3) early stage lung cancerstage lung cancer..
MethodsMethods
Sampling of exhaled airSampling of exhaled air
• Inspiration of VOC-filtered air by tidal breathing for 5 Inspiration of VOC-filtered air by tidal breathing for 5 minutes, through T-shaped two-way non-rebreathing minutes, through T-shaped two-way non-rebreathing valve (valve (Hans Rudolph Inc., Shawnee, USAHans Rudolph Inc., Shawnee, USA))
• Inhalation to total lung capacity and full exhalation with Inhalation to total lung capacity and full exhalation with approximate flow rate 0.25 – 0.5 L/s into a polyethylene approximate flow rate 0.25 – 0.5 L/s into a polyethylene terephthalate plastic bag terephthalate plastic bag
• Analysis by electronic nose device (Analysis by electronic nose device (Cyranose 320, Smith Cyranose 320, Smith Detection, USADetection, USA) within 5 minutes after breath sample ) within 5 minutes after breath sample collectioncollection
Dragonieri S et al. J Allergy Clin Immunol 2007Dragonieri S et al. J Allergy Clin Immunol 2007
MethodsMethods
Satistical analysisSatistical analysis
Support vector machine (SVM)Support vector machine (SVM)
• Continuous predictors: relative maximum (RContinuous predictors: relative maximum (Rmaxmax), area ), area
under curve (∑under curve (∑0-60”0-60”) and tg ) and tg αα0-60”0-60” for each curve of 32 for each curve of 32
sensorssensors
• Additional predictor factors: Additional predictor factors: age, age, smoking status smoking status (smoker, non-smoker, ex-smoker), (smoker, non-smoker, ex-smoker), smoking historysmoking history ((pack-yearspack-years)) and and ambient ambient temperature temperature ttº Cº C at the at the moment of measurementmoment of measurement
Support Vector MachineSupport Vector Machine
ResultsResults
Morphologically confirmed lung cancerMorphologically confirmed lung cancerOther diseases: COPD, pneumonia, tbc, PATE, benign tumors etc.Other diseases: COPD, pneumonia, tbc, PATE, benign tumors etc.Control – healthy volunteers, postinflammatory pneumofibrosisControl – healthy volunteers, postinflammatory pneumofibrosis
ResultsResults
Classification summary Classification summary (Support Vector Machine), (Support Vector Machine), Cancer vs No cancerCancer vs No cancer, ,
Training/Test sample 100% Training/Test sample 100% SVM: SVM: Classification type Classification type 1 (C=2.000), 1 (C=2.000), KernelKernel: : Linear Linear
Number of support vectors Number of support vectors = 219 (170 = 219 (170 boundedbounded) ) Include criteriaInclude criteria: v20='GF': v20='GF'
TotalTotal CorrectCorrect IncorrectIncorrect Correct Correct (%)(%) Incorrect Incorrect (%)(%)
CancerCancer 165165 144144 2121 87.387.3 12.712.7
No cancerNo cancer 170170 121121 4949 71.271.2 28.828.8
Cancer vs No cancerCancer vs No cancer
Parameters of 32 detectors RParameters of 32 detectors Rmaxmax, ∑, ∑0-600-60 un tg un tg αα0-600-60 Age, Pack-years and ambient tºC Age, Pack-years and ambient tºC
Cross-validation 72.8% Class accuracy 79.1%Cross-validation 72.8% Class accuracy 79.1%
CancerCancer No cancerNo cancer
CancerCancer 144144 4949 74.674.6 PPVPPV
No cancerNo cancer 2121 121121 85.285.2 NPVNPV
87.387.3 71.271.2
SensitivitySensitivity SpecificitySpecificity
ResultsResults
Classification summary Classification summary (Support Vector Machine), (Support Vector Machine), Cancer vs No cancerCancer vs No cancer,, Training Training
sample 75% Test sample 25%sample 75% Test sample 25% SVM: SVM: Classification type Classification type 1 (C=2.000), 1 (C=2.000), KernelKernel: : LinearLinear
Number of support vectors Number of support vectors = 219 (170 = 219 (170 boundedbounded) ) Include criteriaInclude criteria: v20='GF': v20='GF'
TotalTotal CorrectCorrect IncorrectIncorrect Correct Correct (%)(%) Incorrect Incorrect (%)(%)
CancerCancer 4545 4040 55 88.988.9 11.111.1
No cancerNo cancer 3939 2626 1313 66.766.7 33.333.3
Cancer vs No cancerCancer vs No cancer
Parameters of 32 detectors RParameters of 32 detectors Rmaxmax, ∑, ∑0-600-60 un tg un tg αα0-600-60 Age, Pack-years and ambient tºC Age, Pack-years and ambient tºC
Cross-validation 69.7% Class accuracy 75.5%Cross-validation 69.7% Class accuracy 75.5%
CancerCancer No cancerNo cancer
CancerCancer 4040 1313 75.575.5 PPVPPV
No cancerNo cancer 55 2626 83.983.9 NPVNPV
88.988.9 66.766.7
SensitivitySensitivity SpecificitySpecificity
ResultsResults
Classification summary Classification summary (Support Vector Machine) (Support Vector Machine) Cancer vs Control Training/Test Cancer vs Control Training/Test
sample 100% sample 100% SVM: SVM: Classification type Classification type 1 (C=5.000), Kernel: 1 (C=5.000), Kernel: LinearLinear Number of support Number of support
vectors vectors = 84 (39 = 84 (39 boundedbounded) ) Include criteriaInclude criteria: v20='GF': v20='GF'
TotalTotal CorrectCorrect IncorrectIncorrect Correct Correct (%)(%) Incerrect Incerrect (%)(%)
CancerCancer 166166 164164 22 98.898.8 1.21.2
ControlControl 7979 6464 1515 81.081.0 19.019.0
Cancer vs ControlCancer vs Control
Parameters of 32 detectors RParameters of 32 detectors Rmaxmax, ∑, ∑0-600-60 un tg un tg αα0-600-60 Age, Pack-years and ambient tºC Age, Pack-years and ambient tºC
Cross-validation 90.6% Class accuracy 93.1%Cross-validation 90.6% Class accuracy 93.1%
CancerCancer ControlControl
CancerCancer 164164 1515 91.691.6 PPVPPV
ControlControl 22 6464 97.097.0 NPVNPV
98.898.8 81.081.0
SensitivitySensitivity SpecificitySpecificity
ResultsResults
Classification summary Classification summary (Support Vector Machine) (Support Vector Machine) Cancer vs Control Training sample Cancer vs Control Training sample
75% Test sample 25% 75% Test sample 25% SVM: SVM: Classification type Classification type 1 (C=5.000), Kernel: 1 (C=5.000), Kernel: LinearLinear Number of Number of
support vectors support vectors = 84 (39 = 84 (39 saistītisaistīti) ) Include criteriaInclude criteria: v20='GF': v20='GF'
TotalTotal CorrectCorrect IncorrectIncorrect Correct Correct (%)(%) Incorrect Incorrect (%)(%)
CancerCancer 4545 4444 11 97.897.8 2.22.2
ControlControl 1616 1111 55 68.868.8 31.231.2
Cancer vs ControlCancer vs Control
Parameters of 32 detectors RParameters of 32 detectors Rmaxmax, ∑, ∑0-600-60 un tg un tg αα0-600-60 Age, Pack-years and ambient tºC Age, Pack-years and ambient tºC
Cross-validation 89.7% Class accuracy 93.5%Cross-validation 89.7% Class accuracy 93.5%
CancerCancer ControlControl
CancerCancer 4444 55 89.889.8 PPVPPV
ControlControl 11 1111 91.791.7 NPVNPV
97.897.8 68.868.8
SensitivitySensitivity SpecificitySpecificity
ResultsResults
Cancer vs Cancer + COPD vs COPD vs ControlCancer vs Cancer + COPD vs COPD vs Control
Parameters of 32 detectors RParameters of 32 detectors Rmaxmax, ∑, ∑0-600-60 un tg un tg αα0-600-60 Age, Pack-years and Ambient tºC Age, Pack-years and Ambient tºC
Cross-validation 71.1% Class accuracy 77.4%Cross-validation 71.1% Class accuracy 77.4%
Classification summary Classification summary (Support Vector Machine), (Support Vector Machine), Cancer vs Cancer+COPD vs COPD vs Cancer vs Cancer+COPD vs COPD vs
ControlControl, , SVM: SVM: Classification type Classification type 1 (C=2.000), Kernel: 1 (C=2.000), Kernel: LinearLinear Number of support vectors Number of support vectors = =
152 (43 152 (43 bounded)bounded) Include criteriaInclude criteria: v20='GF': v20='GF'
TotalTotal CorrectCorrect IncorrectIncorrect Correct Correct (%)(%) Incorrect Incorrect (%)(%)
CancerCancer 6363 3636 2727 57.157.1 42.942.9
Cancer + COPDCancer + COPD 7799 7979 00 100.0100.0 0.00.0
COPDCOPD 1515 55 1010 33.333.3 66.766.7
ControlControl 7878 6262 1616 79.579.5 20.520.5
ResultsResults
Cancer vs Cancer + COPD vs COPD vs ControlCancer vs Cancer + COPD vs COPD vs Control
Parameters of 32 detectors RParameters of 32 detectors Rmaxmax, ∑, ∑0-600-60 un tg un tg αα0-600-60 Age, Pack-years and Ambient tºC Age, Pack-years and Ambient tºC
Cross-validation 71.1% Class accuracy 77.4%Cross-validation 71.1% Class accuracy 77.4%
Classification matrix Classification matrix (Support Vector Machine), (Support Vector Machine), Cancer vs Cancer+COPD vs COPD vs Cancer vs Cancer+COPD vs COPD vs
ControlControl, , Training/Test sample 100% Training/Test sample 100% SVM: SVM: Classification typeClassification type1 (C=2.000), Kernel: 1 (C=2.000), Kernel: LinearLinear, ,
Number of support vectors Number of support vectors = 152 (43 = 152 (43 bounded) Prognosis bounded) Prognosis (r(rowsows) x ) x DiagnosisDiagnosis ( (columnscolumns))
CancerCancer Cancer + COPDCancer + COPD COPDCOPD ControlControl
CancerCancer 3636 2626 00 11
Cancer + COPDCancer + COPD 00 79 (!)79 (!) 00 00
COPDCOPD 11 99 55 00
ControlControl 77 99 00 6262
ResultsResults
Parameters of 32 detectors RParameters of 32 detectors Rmaxmax, ∑, ∑0-600-60 un tg un tg αα0-600-60 Age, Pack-years and Ambient tºC Age, Pack-years and Ambient tºC
Patients with post-obstructive pneumonia in cancer group and bacterial, Patients with post-obstructive pneumonia in cancer group and bacterial, TB and infarct pneumonia in no cancer group were excluded from analysisTB and infarct pneumonia in no cancer group were excluded from analysis
Classification matrix Classification matrix (Support Vector Machine), (Support Vector Machine), Stage 1-2, 3 and 4Stage 1-2, 3 and 4 Training/Test Training/Test
group 100% group 100% SVM: SVM: Classification type Classification type 1 (C=1.000), Kernel: 1 (C=1.000), Kernel: LinearLinear, , Number of support Number of support
vectors vectors = 184 (73 = 184 (73 boundedbounded) ) PrognosisPrognosis (r (rowsows) x ) x DiagnosisDiagnosis ( (columns)columns)
No cancerNo cancer Stage 1-2Stage 1-2 Stage 3Stage 3 Stage 4Stage 4
No cancerNo cancer 100100 00 77 22
Stage 1-2Stage 1-2 1111 33 2525 11
Stage 3Stage 3 99 00 4040 00
Stage 4Stage 4 99 77 2727 33
ConclusionsConclusions
Exhaled breath analysis by electronic nose using support Exhaled breath analysis by electronic nose using support vector pattern recognition method vector pattern recognition method is able to discriminate:is able to discriminate:
• LLung cancer from healthy subjects and patients with ung cancer from healthy subjects and patients with different lung diseasesdifferent lung diseases
• An An early stage lung cancer from healthy subjects and early stage lung cancer from healthy subjects and patients with different lung diseasespatients with different lung diseases
• Some Some discrimination pattern between lung cancer,discrimination pattern between lung cancer, patients with lung cancer and COPD,patients with lung cancer and COPD, COPD and control, COPD and control, even in patients with combined diseaseeven in patients with combined diseasess
AcknowledgementsAcknowledgements
• To my colleagues and our teamTo my colleagues and our team
Prof. Immanuels TaivansProf. Immanuels Taivans
Dr.biol. Gunta StrazdaDr.biol. Gunta Strazda
Dr. Ainis PirtnieksDr. Ainis Pirtnieks
Dr.med. Uldis KopeikaDr.med. Uldis Kopeika
Dr.biol. Normunds JurkaDr.biol. Normunds Jurka
Ph.dr. Liga BalodePh.dr. Liga Balode
Doctoral student Agnese KislinaDoctoral student Agnese Kislina
Mrs. Inara KantaneMrs. Inara Kantane
Thank You for Your Attention!Thank You for Your Attention!
How to sniff out the disease?How to sniff out the disease?
