Breath profile as composite biomarkers for lung cancer diagnosis

Zou, Yingchang; Wang, Yu; Jiang, Zaile; Zhou, Yuan; Chen, Ying; Hu, Yanjie; Jiang, Guobao; Xie, Duan

doi:10.1016/j.lungcan.2021.01.020

Cited by 20 publications

(16 citation statements)

References 54 publications

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“…However, breathing profiles can be described by many other variables, such as physiological patterns (VO 2max and VC), 13 or even biomarkers present in the air expired. 57 Relative contribution of abdominal versus thoracic breathing is also a well-known specific characteristic of speech breathing control. 58 In a recent study, 57 machine learning was applied to 308 biomarkers of patients' breathing profiles to detect early stages of lung cancer.…”

Section: Discussionmentioning

confidence: 99%

“…57 Relative contribution of abdominal versus thoracic breathing is also a well-known specific characteristic of speech breathing control. 58 In a recent study, 57 machine learning was applied to 308 biomarkers of patients' breathing profiles to detect early stages of lung cancer. These techniques allow to determine the most discriminant markers among the 308 features.…”

Section: Discussionmentioning

confidence: 99%

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Speech breathing: variable but individual over time and according to limb movements

Serre

Dohen

Fuchs

et al. 2021

Annals of the New York Academy of Sciences

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Breathing is variable but also highly individual. Since the 1980s, evidence of a ventilatory personality has been observed in different physiological studies. This original term refers to within-speaker consistency in breathing characteristics across days or even years. Speech breathing is a specific way to control ventilation while supporting speech planning and phonation constraints. It is highly variable between speakers but also for the same speaker, depending on utterance properties, bodily actions, and the context of an interaction. Can we yet still observe consistency over time in speakers' breathing profiles despite these variations? We addressed this question by analyzing the breathing profiles of 25 native speakers of German performing a narrative task on 2 days under different limb movement conditions. The individuality of breathing profiles over conditions and days was assessed by adopting methods used in physiological studies that investigated a ventilatory personality. Our results suggest that speakerspecific breathing profiles in a narrative task are maintained over days and that they stay consistent despite light physical activity. These results are discussed with a focus on better understanding what speech breathing individuality is, how it can be assessed, and the types of research perspectives that this concept opens up.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Speech breathing: variable but individual over time and according to limb movements

Serre

Dohen

Fuchs

et al. 2021

Annals of the New York Academy of Sciences

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“…Data were obtained from a previous case–control study where 236 subjects were asked to participate [ 15 ]. All cases were confirmed with an incident of lung cancer histologically or pathologically, while controls were confirmed with a negative result of LDCT scan.…”

Section: Methodsmentioning

confidence: 99%

“…Sample collection and analysis were performed as previously published [ 15 ]. Briefly, to collect breath samples, subjects were asked to breathe tidally into a self-developed collection device with which VOCs in 1000 mL exhaled breath were captured and concentrated into a Tenax TA stainless steel tube (PerkinElmer, Waltham, MA).…”

Section: Methodsmentioning

confidence: 99%

“…Here, we are going to identify molecular markers of metabolic dysregulation in lung cancer using the GC-MS data obtained from a recent study [ 15 ]. In the mentioned study, we reported a method to differentiate subjects with lung cancer from healthy controls, by means of exhaled breath test with the GC-MS. Then, breath profiles instead of volatile markers were analyzed with machine-learning algorithm and an accuracy of 85% was shown in six-fold cross validations.…”

Section: Introductionmentioning

confidence: 99%

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Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study

Zou

Jiang³

et al. 2022

Annals of Medicine

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Introduction The clinical application of lung cancer detection based on breath test is still challenging due to lack of predictive molecular markers in exhaled breath. This study explored potential lung cancer biomarkers and their related pathways using a typical process for metabolomics investigation. Material and methods Breath samples from 60 lung cancer patients and 176 healthy people were analyzed by GC-MS. The original data were GC-MS peak intensity removing background signal. Differential metabolites were selected after univariate statistical analysis and multivariate statistical analysis based on OPLS-DA and Spearman rank correlation analysis. A multivariate PLS-DA model was established based on differential metabolites for pattern recognition. Subsequently, pathway enrichment analysis was performed on differential metabolites. Results The discriminant capability was assessed by ROC curve of whom the average AUC and average accuracy in 100-fold cross validations were 0.871 and 0.787, respectively. Eight potential biomarkers were involved in a total of 18 metabolic pathways. Among them, 11 metabolic pathways have p -value smaller than .1. Discussion Some pathways among them are related to risk factors or therapies of lung cancer. However, more of them are dysregulated pathways of lung cancer reported in studies based on genome or transcriptome data. Conclusion We believe that it opens the possibility of using metabolomics methods to analyze data of exhaled breath and promotes involvement of knowledge dataset to cover more volatile metabolites. Clinical significance Although a series of related research reported diagnostic models with highly sensitive and specific prediction, the clinical application of lung cancer detection based on breath test is still challenging due to disease heterogeneity and lack of predictive molecular markers in exhaled breath. This study may promote the clinical application of this technique which is suitable for large-scale screening thanks to its low-cost and non-invasiveness. As a result, the mortality of lung cancer may be decreased in future. Key messages In the present study, 11 pathways involving 8 potential biomarkers were discovered to be dysregulated pathways of lung cancer. We found that it is possible to apply metabolomics methods in analysis of data from breath test, which is meaningful to discover convinced volatile markers with definite pathological and histological significance.

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Lung cancer detection by using probabilistic majority voting and optimization techniques

Sünnetci

Alkan

2022

Int J Imaging Syst Tech

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The number of people dying from lung cancer in the world is increasing day by day. Therefore, early diagnosis of lung cancer holds a prominent position for the recovery of patients. Chest computed tomography scan images that are clinical adjuncts are used in the diagnosis of lung cancer. Herein, the proposed method automatically diagnoses lung cancer using machine learning algorithms, probabilistic majority voting, and optimization techniques. The paper uses images from Adenocarcinoma Left Lower Lobe, Large Cell Carcinoma Left Hilum, Normal, and Squamous Cell Carcinoma Left Hilum subjects. 80% and 20% of these images are used in the training and validation set, respectively, and there are 1000 images in the data set. For each image used in the study, image features are extracted using the Bag of Features method (2000 features). Linear Discriminant, Optimizable Support Vector Machine, and Optimizable K-Nearest Neighbor (KNN) classifiers are trained using these features. Afterward, the new image label is estimated in two steps. First, if the two or three classifier outputs are the same, the image label is detected using the majority voting technique. Second, if the classifier outputs are not the same, the image label is determined according to the most successful classifier, Optimizable KNN. To better experience the majority voting technique used in the proposed method, its detailed theoretical framework is given in the study.Additionally, the Graphical User Interface application of the proposed method has been designed to save time and reduce the workload of expert radiologists.From the results, it is seen that the Accuracy (%), Error (%), Sensitivity (%), Specificity (%), Precision (%), F 1 Score (%), Matthews Correlation Coefficient, and Kappa of the proposed method are equal to 99.2837, 0.007163, 99.2571, 99.9139, 99.3486, 99.2816, 0.98738, and 0.94965 respectively.

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Breath profile as composite biomarkers for lung cancer diagnosis

Cited by 20 publications

References 54 publications

Speech breathing: variable but individual over time and according to limb movements

Speech breathing: variable but individual over time and according to limb movements

Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study

Lung cancer detection by using probabilistic majority voting and optimization techniques

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