Integrated statistical learning of metabolic ion mobility spectrometry profiles for pulmonary disease identification

Hauschild, Anne-Christin; Baumbach, Jan

doi:10.4238/2012.july.10.17

Cited by 41 publications

(51 citation statements)

References 13 publications

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“…An early diagnosis of COPD would be an advantage. Multiple research groups demonstrated that VOC profiles could accurately differentiate COPD patients from healthy (non-) smokers [25,29,31,33]. In contrast, others found a limited performance of VOCs profiles to differentiate COPD patients from (former) smokers [15,26].…”

Section: Resultsmentioning

confidence: 99%

Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review

et al. 2012

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There is an increasing interest in the potential of exhaled biomarkers, such as volatile organic compounds (VOCs), to improve accurate diagnoses and management decisions in pulmonary diseases. The objective of this manuscript is to systematically review the current knowledge on exhaled VOCs with respect to their potential clinical use in asthma, lung cancer, chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), and respiratory tract infections. A systematic literature search was performed in PubMed, EMBASE, Cochrane database, and reference lists of retrieved studies. Controlled, clinical, English-language studies exploring the diagnostic and monitoring value of VOCs in asthma, COPD, CF, lung cancer and respiratory tract infections were included. Data on study design, setting, participant characteristics, VOCs techniques, and outcome measures were extracted. Seventy-three studies were included, counting in total 3,952 patients and 2,973 healthy controls. The collection and analysis of exhaled VOCs is non-invasive and could be easily applied in the broad range of patients, including subjects with severe disease and children. Various research groups demonstrated that VOCs profiles could accurately distinguish patients with a pulmonary disease from healthy controls. Pulmonary diseases seem to be characterized by a disease specific breath-print, as distinct profiles were found in patients with dissimilar diseases. The heterogeneity of studies challenged the inter-laboratory comparability. In conclusion, profiles of VOCs are potentially able to accurately diagnose various pulmonary diseases. Despite these promising findings, multiple challenges such as further standardization and validation of the diverse techniques need to be mastered before VOCs can be applied into clinical practice.

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Section: Resultsmentioning

confidence: 99%

Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review

et al. 2012

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“…20 Chronic obstructive pulmonary disease (COPD) is an inflammatory condition characterized by oxidative stress and particular VOCs from lungs ( Figure 3). 6 Hauschild et al 4 analysed breath data by IMS-MCC from 84 volunteers, either healthy or suffering from COPD or bronchial carcinoma and extracted 28-scoring VOCs that allowed differentiating COPD patients. On the other hand, Basanta et al 21 used differential mobility spectrometry (DMS) to discriminate between individuals with and without COPD.…”

Section: Applications Of Breath Diagnosis In Medicinementioning

confidence: 99%

Ion mobility spectrometry: the diagnostic tool of third millennium medicine

Romero¹,

Fernandez‐Maestre²

2018

Rev. Assoc. Med. Bras.

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Ion mobility spectrometry (IMS) is a fast, low cost, portable, and sensitive technique that separates ions in a drift tube under the influence of an electric field according to their size and shape. IMS represents a non-invasive and reliable instrumental alternative for the diagnosis of different diseases through the analysis of volatile metabolites in biological samples. IMS has applications in medicine in the study of volatile compounds for the non-invasive diagnose of bronchial carcinoma, chronic obstructive pulmonary disease, and other diseases analysing breath, urine, blood, faeces, and other biological samples. This technique has been used to study complex mixtures such as proteomes, metabolomes, complete organisms like bacteria and viruses, monitor anaesthetic agents, determine drugs, pharmaceuticals, and volatile compounds in human body fluids, and others. Pharmaceutical applications include analysis of over-thecounter-drugs, quality assessment, and cleaning verification. Medical practice needs non-invasive, robust, secure, fast, real-time, and low-cost methods with high sensitivity and compact size instruments to diagnose different diseases and IMS is the diagnostic tool that meets all these requirements of the Medicine of the future.

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“…Like the study of Westhoff et al [66], the advanced pre-processing and peak location methods provided by the VisualNow software were used to build the features for COPD and bronchial carcinoma prediction. To get a broad overview of the potential of the data and the different classification techniques, six different sophisticated statistical learning methods have been applied: Decision trees, naive Bayes, neural networks, random forest and linear as well as radial SVM [79]. Similar to the previous studies of Baumbach et al [78] and Westhoff et al [66], the set of samples was small (119 volunteers), which leads to a very noisy estimation of the predictive performance.…”

Section: Statistical Learningmentioning

confidence: 99%

“…In fact, most of the measurements of COPD patients falsely predicted suffering from both COPD and bronchial carcinoma, which might be reducible to the characteristic of COPD as a common and important independent risk factor for lung cancer. Both studies, Westhoff et al 2011 [66] as well as Hauschild et al [79], identified a set of ten most informative features, whereby five of these twenty features overlapped in the inverse drift time as well as retention time, which means they represent the same VOCs. …”

Section: Statistical Learningmentioning

confidence: 99%

Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art

et al. 2012

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Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain.

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Integrated statistical learning of metabolic ion mobility spectrometry profiles for pulmonary disease identification

Cited by 41 publications

References 13 publications

Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review

Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review

Ion mobility spectrometry: the diagnostic tool of third millennium medicine

Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art

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