Abstract
Accurate classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) in lung cancer is critical to physicians’ clinical decision-making. Exhaled breath analysis provides a tremendous potential approach in non-invasive diagnosis of lung cancer but was rarely reported for lung cancer subtypes classification. In this paper, we firstly proposed a combined method, integrating K-nearest neighbor classifier (KNN), borderline2-synthetic minority over-sampling technique (borderlin2-SMOTE), and feature reduction methods, to investigate the ability of exhaled breath to distinguish AC from SCC patients. The classification performance of the proposed method was compared with the results of four classification algorithms under different combinations of borderline2-SMOTE and feature reduction methods. The result indicated that the KNN classifier combining borderline2-SMOTE and feature reduction methods was the most promising method to discriminate AC from SCC patients and obtained the highest mean area under the receiver operating characteristic curve (0.63) and mean geometric mean (58.50) when compared to others classifiers. The result revealed that the combined algorithm could improve the classification performance of lung cancer subtypes in breathomics and suggested that combining non-invasive exhaled breath analysis with multivariate analysis is a promising screening method for informing treatment options and facilitating individualized treatment of lung cancer subtypes patients.
Highlights
Accurate classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) in lung cancer is critical to physicians’ clinical decision-making
We have known that the incidence of AC was similar in men and women, while the incidence of SCC was higher in men in our work
The results in this study showed that the K-nearest neighbor classifier (KNN) classifier combining with borderline2-SMOTE and Principal component analysis (PCA) and support vector machine (SVM)-RFE feature reduction method obtained the best performance in distinguishing AC from SCC patients
Summary
Accurate classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) in lung cancer is critical to physicians’ clinical decision-making. Mazzone et al demonstrated the feasibility of classifying the exhaled breath of lung cancer patients with different histological subtypes through a colorimetric sensor array. They obtained excellent classification results using a backward step-down feature selection method before performing a logistic regression analysis. Most of the studies in breathomics analysis used algorithms alone to achieve the goal of distinguishing tumor histological subtypes, and they pay less attention to data pre-processing of the breathomics data to obtain exact information before conducting the regression or classification model. We would pay more attention to data mining and design the algorithm structure for an efficient diagnosis in this field
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