The International Journal of Tuberculosis and Lung Disease · 2012 · 91 citations · 15 references
Public HealthDiagnostic SciencesDiagnostic MicrobiologyMass Spectrometric AnalysisPulmonologyBioanalytical Method ValidationMycobacterial DiagnosticsPulmonary AssessmentPulmonary TuberculosisTuberculosis DiagnosisTuberculosis Diagnostic TestingBreath Analysis MethodologyGas Chromatography-mass SpectrometryMycobacterium Tuberculosis InfectionPulmonary Tuberculosis DiagnosisSupport Vector ClassificationSupport Vector Machine ClassificationTuberculosis DiagnosticsTuberculosis Screening
SETTING: Cape Town, South Africa. OBJECTIVES: We investigated the potential of breath analysis by gas chromatography-mass spectrometry (GC-MS) to discriminate between samples collected prospectively from patients with suspected tuberculosis (TB). DESIGN: Samples were obtained in a TB-endemic setting in South Africa, where 28% of culture-proven TB patients had Ziehl-Neelsen (ZN) negative sputum smear. A training set of breath samples from 50 sputum culture-proven TB patients and 50 culture-negative non-TB patients was analysed using GC-MS. We used support vector machine analysis for classification of the patient samples into TB and non-TB. RESULTS: A classification model with seven compounds had a sensitivity of 72%, a specificity of 86% and an accuracy of 79% compared with culture. The classification model was validated with breath samples from a different set of 21 TB and 50 non-TB patients from the same area, giving a sensitivity of 62%, a specificity of 84% and an accuracy of 77%. CONCLUSION: This study shows that GC-MS breath analysis is able to differentiate between TB and non-TB breath samples even among patients with a negative ZN sputum smear but a positive culture for Mycobacterium tuberculosis. We conclude that breath analysis by GC-MS merits further research.
15
Gene Selection for Cancer Classification using Support Vector Machines
Isabelle Guyon, Jason Weston, S. Barnhill et al. · Machine Learning · 2002 · 9.8K citations · Full text
Data mining in bioinformatics using Weka
Eibe Frank, Mark Hall, Len Trigg et al. · Bioinformatics · 2004 · 903 citations · Full text
Volatile biomarkers of pulmonary tuberculosis in the breath
Michael Phillips, Renee N. Cataneo, Rany Condos et al. · Tuberculosis · 2006 · 345 citations
Breath biomarkers of active pulmonary tuberculosis
Michael Phillips, Victoria Basa-Dalay, Graham Bothamley et al. · Tuberculosis · 2010 · 260 citations