Prediction of lung cancer using volatile biomarkers in breath1
| Autor: | William N. Rom, Robert Kloss, Robert B. Cameron, Roger A. Maxfield, Peter Schmitt, Asif Rashid, Nasser K. Altorki, Muhammad I. Munawar, Michael Phillips, Harvey I. Pass, Joel Greenberg, Renee N. Cataneo, John H.M. Austin |
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| Rok vydání: | 2007 |
| Předmět: |
Breath test
Cancer Research medicine.medical_specialty Pathology education.field_of_study Receiver operating characteristic medicine.diagnostic_test business.industry Multilinear analysis Population General Medicine medicine.disease Gastroenterology Text mining Oncology Internal medicine Area under curve Genetics medicine False positive rate education business Lung cancer |
| Zdroj: | Cancer Biomarkers. 3:95-109 |
| ISSN: | 1875-8592 1574-0153 |
| DOI: | 10.3233/cbm-2007-3204 |
| Popis: | Background: Normal metabolism generates several volatile organic compounds (VOCs) that are excreted in the breath (e.g. alkanes). In patients with lung cancer, induction of high-risk cytochrome p450 genotypes may accelerate catabolism of these VOCs, so that their altered abundance in breath may provide biomarkers of lung cancer. Methods: VOCs in 1.0 L alveolar breath were analyzed in 193 subjects with primary lung cancer and 211 controls with a negative chest CT. Subjects were randomly assigned to a training set or to a prediction set in a 2:1 split. A fuzzy logic model of breath biomarkers of lung cancer was constructed in the training set and then tested in subjects in the prediction set by generating their typicality scores for lung cancer. Results: Mean typicality scores employing a 16 VOC model were significantly higher in lung cancer patients than in the control group ( p< 0.0001 in all TNM stages). The model predicted primary lung cancer with 84.6% sensitivity, 80.0% specificity, and 0.88 area under curve (AUC) of the receiver operating characteristic (ROC) curve. Predictive accuracy was similar in TNM stages 1 through 4, and was not affected by current or former tobacco smoking. The predictive model achieved near-maximal performance with six breath VOCs, and was progressively degraded by random classifiers. Predictions with fuzzy logic were consistently superior to multilinear analysis. If applied to a population with 2% prevalence of lung cancer, a screening breath test would have a negative predictive value of 0.985 and a positive predictive value of 0.163 (true positive rate = 0.277, false positive rate = 0.029). |
| Databáze: | OpenAIRE |
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