非表示:
キーワード:
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要旨:
Exhaled air carries information on human health status. Ion mobility
spectrometers combined with a multi-capillary column (MCC/IMS) is a well-known
technology for detecting volatile organic compounds (VOCs) within human breath.
This technique is relatively inexpensive, robust and easy to use in every day
practice. However, the potential of this methodology depends on successful
application of computational approaches for finding relevant VOCs and
classification of patients into disease-specific profile groups based on the
detected VOCs. We developed an integrated state-of-the-art system using
sophisticated statistical learning techniques for VOC-based feature selection
and supervised classification into patient groups. We analyzed breath data from
84 volunteers, each of them either suffering from chronic obstructive pulmonary
disease (COPD), or both COPD and bronchial carcinoma (COPD + BC), as well as
from 35 healthy volunteers, comprising a control group (CG). We standardized
and integrated several statistical learning methods to provide a broad overview
of their potential for distinguishing the patient groups.
We found that there is strong potential for separating MCC/IMS
chromatograms of healthy controls and COPD patients (best accuracy COPD vs CG:
94). However, further examination of the impact of
bronchial carcinoma on COPD/no-COPD classification performance
is necessary (best accuracy CG vs COPD vs COPD + BC: 79). We
also extracted 20 high-scoring VOCs that allowed differentiating
COPD patients from healthy controls. We conclude that these statistical
learning methods have a generally high accuracy when applied to wellstructured,
medical MCC/IMS data.
