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25 November 2017 : Clinical Research  

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

Xiangsheng Cai1ABCDE, Lu Chen2ABCD, Tao Kang3ACDF, Yongming Tang3DEF, Teong Lim3ABDEF, Meng Xu2AEF, Hongxiang Hui134AEFG*

DOI: 10.12659/MSM.904738

Med Sci Monit 2017; 23:5620-5629

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.

Keywords: Early Detection of Cancer, tumor microenvironment

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750