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. 2017 Nov 25:23:5620-5629.
doi: 10.12659/msm.904738.

A Prediction Model with a Combination of Variables for Diagnosis of Lung Cancer

Xiangsheng Cai  1 , Lu Chen  2 , Tao Kang  3 , Yongming Tang  3 , Teong Lim  3 , Meng Xu  2 , Hongxiang Hui  1   3   4
Affiliations

A Prediction Model with a Combination of Variables for Diagnosis of Lung Cancer

Xiangsheng Cai et al. Med Sci Monit. .

Abstract

BACKGROUND Multivariate models with a combination of variables can predict disease more accurately than a single variable employed alone. We developed a logistic regression model with a combination of variables and evaluated its ability to predict lung cancer. MATERIAL AND METHODS The exhaled breath from 57 patients with lung cancer and 72 healthy controls without cancer was collected. The VOCs of exhaled breath were examined qualitatively and quantitatively by a novel electronic nose (Z-nose4200 equipment). The VOCs in the 2 groups were compared using the Mann-Whitney U test, and the baseline data were compared between the 2 groups using the chi-square test or ANOVA. Variables from VOCs and baseline data were selected by stepwise logistic regression and subjected to a prediction model for the diagnosis of lung cancer as combined factors. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of this prediction model. RESULTS Nine VOCs in exhaled breath of lung cancer patients differed significantly from those of healthy controls. Four variables - age, hexane, 2,2,4,6,6-pentamethylheptane, and 1,2,6-trimethylnaphthalene - were entered into the prediction model, which could effectively separate the lung cancer samples from the control samples with an accuracy of 82.8%, a sensitivity of 76.0%, and a specificity of 94.0%. CONCLUSIONS The profile of VOCs in exhaled breath contained distinguishable biomarkers in the patients with lung cancers. The prediction model with 4 variables appears to provide a new technique for lung cancer detection.

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Conflict of interest statement

Conflict of Interest

None.

Figures

Figure 1
Figure 1
Schematic diagram of z-NOSE-4200. The GC sensor is based on a 6-port valve and oven, a pre-concentrating trap, a short GC column, and a surface acoustic wave detector.
Figure 2
Figure 2
The output of the Z-nose 4200 after calibration by the standard solution (C6–C14).
Figure 3
Figure 3
The result of exhaled gas detected by Z-nose 4200 after qualitative and quantitative calibration.
Figure 4
Figure 4
Non-parametric Mann-Whitney U test comparing Z values of 23 VOCs in the lung cancer and healthy control groups. Dashed line: significance of Z=±1.96. The VOCs (dimethylmethane [Z=−2.426, P=0.015], ethanol [Z=−2.470, P=0.014], methane [Z=−1.989, P=0.047], hexane [Z=−2.321, P=0.020], 2.2.4.6.6-pentamethylheptane [Z=−4.543, P=0.001], 2,5,5-trimethyl-2,6-heptadien-4-one [Z=−2.926, P=0.003], 1-isopropyl-4-methylbicyclo [3.1.0] hexan-3-ol [Z=−3.904, P=0.001], dodecane [Z=−2.137, P=0.033], and 1,2,6-trimethylnaphthalen [Z=−2.241, P=0.025]). Z values that exceed the dashed line significantly differ between the lung cancer and healthy control groups.
Figure 5
Figure 5
ROC curves of predicted probability and diagnostic values of significant variables. The AUC of the LR model was 0.878, which was higher than AUCs for age (0.774), hexane (0.622), 2.2.4.6.6-pentamethyl heptane (0.726), and 1.2.6-trimethyl naphthalene (0.381).
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