Lung Cancer Diagnosis System Based on Volatile Organic Compounds (VOCs) Profile Measured in Exhaled Breath

Abstract

Lung cancer is one of the world’s lethal diseases and detecting it at an early stage is crucial and difficult. This paper proposes a computer-aided lung cancer diagnosis system using volatile organic compounds (VOCs) data. A silicon microreactor, which consists of thousands of micropillars coated with an ammonium aminooxy salt, is used to capture the volatile organic compounds (VOCs) in the patients’ exhaled breath by means of oximation reactions. The proposed system ranks the features using the Pearson correlation coefficient and maximum relevance–minimum redundancy (mRMR) techniques. The selected features are fed to nine different classifiers to determine if the lung nodule is malignant or benign. The system is validated using a locally acquired dataset that has 504 patients’ data. The dataset is balanced and has 27 features of volatile organic compounds (VOCs). Multiple experiments were completed, and the best accuracy result is 87%, which was achieved using random forest (RF) either by using all 27 features without selection or by using the first 17 features obtained using maximum relevance–minimum redundancy (mRMR) while using an 80–20 train-test split. The correlation coefficient, maximum relevance–minimum redundancy (mRMR), and random forest (RF) importance agreed that C4H8O (2-Butanone) ranks as the best feature. Using only C4H8O (2-Butanone) for training, the accuracy results using the support vector machine, logistic regression, bagging and neural network classifiers are 86%, which approaches the best result. This shows the potential for these volatile organic compounds (VOCs) to serve as a significant screening tests for the diagnosis of lung cancer.

Keywords

• lung cancer
• volatile organic compounds (VOCs)
• maximum relevance–minimum redundancy (mRMR)
• random forest
• computer-aided diagnosis (CAD)