Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm

Sakumura, Yuichi; Koyama, Yutaro; Tokutake, Hiroaki; Hida, Toyoaki; Sato, Kazuo; Itoh, Toshio; Akamatsu, Takafumi; Shin, Woosuck

doi:10.3390/s17020287

Cited by 98 publications

(55 citation statements)

References 45 publications

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“…Breath gas mainly contains nitrogen and carbon dioxide produced by respiration. It consists of more than 100 gas species with different concentrations, which may provide useful information for early diagnosis of disease [ 69 ]. However, breath test has some drawbacks, such as insufficient accuracy because exhaled air has many crucial volatile organic compounds (VOCs) at very low concentrations and there is no clear protocol for breath sampling [ 70 ].…”

Section: Clinical Lung Screening Modalitiesmentioning

confidence: 99%

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Screening and Biosensor-Based Approaches for Lung Cancer Detection

Wang

2017

Sensors

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Early diagnosis of lung cancer helps to reduce the cancer death rate significantly. Over the years, investigators worldwide have extensively investigated many screening modalities for lung cancer detection, including computerized tomography, chest X-ray, positron emission tomography, sputum cytology, magnetic resonance imaging and biopsy. However, these techniques are not suitable for patients with other pathologies. Developing a rapid and sensitive technique for early diagnosis of lung cancer is urgently needed. Biosensor-based techniques have been recently recommended as a rapid and cost-effective tool for early diagnosis of lung tumor markers. This paper reviews the recent development in screening and biosensor-based techniques for early lung cancer detection.

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Section: Clinical Lung Screening Modalitiesmentioning

confidence: 99%

“…They also performed a subsequent statistical analysis to detect lung cancer by combining various VOCs. Their research findings indicated that the five-element VOC pattern of CHN, methanol, CH3CN, isoprene, and 1-propanol is sufficient for 89% screening accuracy [ 69 ].…”

Section: Clinical Lung Screening Modalitiesmentioning

confidence: 99%

Screening and Biosensor-Based Approaches for Lung Cancer Detection

Wang

2017

Sensors

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“…(26)(27)(28)(29)(30)(31) In this case study, good separation depends on the suitable choice of feature functions after DWT decomposition. The EMG raw data are obtained from the specified experiments for eight different persons.…”

Section: Classification Of Emg Signals Using Svmmentioning

confidence: 99%

Pneumatic Artificial Muscle Actuated Robot for Lower Limb Rehabilitation Triggered by Eelectromyography Signals Using Discrete Wavelet Transformation and Support Vector Machines

2017

Sensors and Materials

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In this study, a real-time electromyography (EMG)-triggered controller for a pneumatic artificial muscle (PAM) actuated lower limb rehabilitation robot is proposed. To make the rehabilitation task controllable by the patient's movement intention, a novel trigger controller is designed according to the EMG signals of the patient's muscle. For predicting his or her movement intention in advance, the EMG signals of the patient's muscle are captured and identified to realize the proposed EMG-triggered control. To guarantee the safety and performance of the proposed system, a patient's movement intention must be identified accurately by EMG feature extraction. First, the discrete wavelet transformation (DWT) technique is used to acquire the feature vectors of the EMG signals. The properties of the different feature spaces are taken into consideration, and the optimal multicomponents of features are chosen according to the experimental results. Second, support vector machines (SVMs) are studied to improve the classification performance. Finally, the MyRIO controller is used to implement a closed-loop control system for the rehabilitation robot with the movement-intention trigger control.

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“…SVM is a supervised model for linear regression analysis on nonlinear data sets. SVM-based classification of samples in the analysis of metabolite profiles from GC-MS and LC-MS has been reported, 18,19 but efficient SVM models optimized for TOF-SIMS data analysis using clinical samples have not yet been defined.…”

Section: Introductionmentioning

confidence: 99%

Screening of important metabolites and KRAS genotypes in colon cancer using secondary ion mass spectrometry

Cho

Choi

Lee

et al. 2020

Bioengineering & Transla Med

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Time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) is an imaging‐based analytical technique that can characterize the surfaces of biomaterials. We used TOF‐SIMS to identify important metabolites and oncogenic KRAS mutation expressed in human colorectal cancer (CRC). We obtained 540 TOF‐SIMS spectra from 180 tissue samples by scanning cryo‐sections and selected discriminatory molecules using the support vector machine (SVM) algorithm. Each TOF‐SIMS spectrum contained nearly 860,000 ion profiles and hundreds of spectra were analyzed; therefore, reducing the dimensionality of the original data was necessary. We performed principal component analysis after preprocessing the spectral data, and the principal components (20) of each spectrum were used as the inputs of the SVM algorithm using the R package. The performance of the algorithm was evaluated using the receiver operating characteristic (ROC) area under the curve (AUC) (0.9297). Spectral peaks (m/z) corresponding to discriminatory molecules used to classify normal and tumor samples were selected according to p‐value and were assigned to arginine, α‐tocopherol, and fragments of glycerophosphocholine. Pathway analysis using these discriminatory molecules showed that they were involved in gastrointestinal disease and organismal abnormalities. In addition, spectra were classified according to the expression of KRAS somatic mutation, with 0.9921 AUC. Taken together, TOF‐SIMS efficiently and simultaneously screened metabolite biomarkers and performed KRAS genotyping. In addition, a machine learning algorithm was provided as a diagnostic tool applied to spectral data acquired from clinical samples prepared as frozen tissue slides, which are commonly used in a variety of biomedical tests.

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Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm

Cited by 98 publications

References 45 publications

Screening and Biosensor-Based Approaches for Lung Cancer Detection

Screening and Biosensor-Based Approaches for Lung Cancer Detection

Pneumatic Artificial Muscle Actuated Robot for Lower Limb Rehabilitation Triggered by Eelectromyography Signals Using Discrete Wavelet Transformation and Support Vector Machines

Screening of important metabolites and KRAS genotypes in colon cancer using secondary ion mass spectrometry

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