Prediction of lung cancer using volatile biomarkers in breath1

Abstract

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).

Urinary Volatile Compounds as Biomarkers for Lung Cancer

“…14,15,[20][21][22][23][24][25][26][27][28][29][30][31][32] Most of these biomarkers are alkanes, alkane, and benzene derivatives. Phillips et al reported that comparing exhaled breath of lung cancer patients and healthy subjects, branched hydrocarbons and alkanes were more abundunt in the lung cancer patients.…”

Solid phase microextraction, mass spectrometry and metabolomic approaches for detection of potential urinary cancer biomarkers—A powerful strategy for breast cancer diagnosis

“…Currently available diagnostic techniques for breast cancer detection include direct examination of the cytomorphology of exfoliated cells, and the molecular analysis of tumor biomarkers in nipple aspirate fluid (NAF) or in ductal lavage [10]. However, these methods are time-consuming, painful for patients and require skilled medical staff.…”

Breath sensors for lung cancer diagnosis

“…However, distinction of the cancer stages is equally important as the detection of cancer for a prompt therapeutic intervention. In one study, VOCs' profile was similar in all the stages of lung cancer and not changing after the tumor resection (Phillips et al, 2007). This was supported with the data of a larger scale multicentre study that was carried out later (Phillips et al, 2007).…”

“…As a result, VOCs' profile become similar in all stages of lung cancer and does not change after the tumor resection. This was later supported by the data from their larger scale multicenter study (Phillips et al, 2007) that resulted in scoring of the exhaled VOCs' profile of the patients with resected lung cancer as cancer (Horváth et al, 2009). The early detection of any disease and especially cancer is the hurdle for the early disease intervention that can eventually determine the success of the treatment.…”