Prediction of lung cancer using volatile biomarkers in breath1

The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications

BACKGROUND

Cancer is a significant public health problem, causing dozens of millions of deaths annually. New cancer screening programs are urgently needed for early cancer detection, as this approach can improve treatment outcomes and increase patient survival. The search for affordable, noninvasive, and highly accurate cancer detection methods revealed a valuable source of tumor-derived metabolites in the human metabolome through the exploration of volatile organic compounds (VOCs) in noninvasive biofluids.

AIM OF REVIEW

This review discusses volatilomics-based approaches for cancer detection using noninvasive biomatrices (breath, saliva, skin secretions, urine, feces, and earwax). We presented the historical background, the latest approaches, and the required stages for clinical validation of volatilomics-based methods, which are still lacking in terms of making noninvasive methods available and widespread to the population. Furthermore, insights into the usefulness and challenges of volatilomics in clinical implementation steps for each biofluid are highlighted.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We outline the methodologies for using noninvasive biomatrices with up-and-coming clinical applications in cancer diagnostics. Several challenges and advantages associated with the use of each biomatrix are discussed, aiming at encouraging the scientific community to strengthen efforts toward the necessary steps to speed up the clinical translation of volatile-based cancer detection methods, as well as discussing in favor of (i) hybrid applications (i.e., using more than one biomatrix) to describe metabolite modulations that can be "cancer volatile fingerprints" and (ii) in multi-omics approaches integrating genomics, transcriptomics, and proteomics into the volatilomic data, which might be a breakthrough for diagnostic purposes, onco-pathway assessment, and biomarker validations.

Breathomics: may it become an affordable, new tool for early diagnosis of non-small-cell lung cancer? An exploratory study on a cohort of 60 patients

OBJECTIVES

Analysis of breath, specifically the patterns of volatile organic compounds (VOCs), has shown the potential to distinguish between patients with lung cancer (LC) and healthy individuals (HC). However, the current technology relies on complex, expensive and low throughput analytical platforms, which provide an offline response, making it unsuitable for mass screening. A new portable device has been developed to enable fast and on-site LC diagnosis, and its reliability is being tested.

METHODS

Breath samples were collected from patients with histologically proven non-small-cell lung cancer (NSCLC) and healthy controls using Tedlar bags and a Nafion filter attached to a one-way mouthpiece. These samples were then analysed using an automated micro portable gas chromatography device that was developed in-house. The device consisted of a thermal desorption tube, thermal injector, separation column, photoionization detector, as well as other accessories such as pumps, valves and a helium cartridge. The resulting chromatograms were analysed using both chemometrics and machine learning techniques.

RESULTS

Thirty NSCLC patients and 30 HC entered the study. After a training set (20 NSCLC and 20 HC) and a testing set (10 NSCLC and 10 HC), an overall specificity of 83.3%, a sensitivity of 86.7% and an accuracy of 85.0% to identify NSCLC patients were found based on 3 VOCs.

CONCLUSIONS

These results are a significant step towards creating a low-cost, user-friendly and accessible tool for rapid on-site LC screening.

CLINICAL REGISTRATION NUMBER

ClinicalTrials.gov Identifier: NCT06034730.

Exhaled VOC detection in lung cancer screening: a comprehensive meta-analysis

BACKGROUND

Lung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection remains crucial for improving patient outcomes. The accuracy of screening for LC by detecting volatile organic compounds (VOCs) in exhaled breath remains to be determined.

METHODS

Our systematic review, following PRISMA guidelines and analyzing data from 25 studies up to October 1, 2023, evaluates the effectiveness of different techniques in detecting VOCs. We registered the review protocol with PROSPERO and performed a systematic search in PubMed, EMBASE and Web of Science. Reviewers screened the studies' titles/abstracts and full texts, and used QUADAS-2 tool for quality assessment. Then performed meta-analysis by adopting a bivariate model for sensitivity and specificity.

RESULTS

This study explores the potential of VOCs in exhaled breath as biomarkers for LC screening, offering a non-invasive alternative to traditional methods. In all studies, exhaled VOCs discriminated LC from controls. The meta-analysis indicates an integrated sensitivity and specificity of 85% and 86%, respectively, with an AUC of 0.93 for VOC detection. We also conducted a systematic analysis of the source of the substance with the highest frequency of occurrence in the tested compounds. Despite the promising results, variability in study quality and methodological challenges highlight the need for further research.

CONCLUSION

This review emphasizes the potential of VOC analysis as a cost-effective, non-invasive screening tool for early LC detection, which could significantly improve patient management and survival rates.

The role of volatile organic compounds for assessing characteristics and severity of non-cystic fibrosis bronchiectasis: an observational study

Background

Hypoxic conditions and Pseudomonas aeruginosa (P. aeruginosa) infection are significant factors influencing the prognosis and treatment of patients with bronchiectasis. This study aimed to explore the potential for breath analysis to detect hypoxic conditions and P. aeruginosa infection in bronchiectasis patients by analyzing of volatile organic compounds (VOCs) in exhaled breath condensate (EBC).

Methods

EBC samples were collected from stable bronchiectasis patients and analyzed using solid phase microextraction-gas chromatography-mass spectrometry (SPME-GCMS). The association of VOCs with bronchiectasis patients' phenotypes including hypoxic conditions and P. aeruginosa isolation was analyzed, which may relate to the severity of bronchiectasis disease.

Results

Levels of 10-heptadecenoic acid, heptadecanoic acid, longifolene, and decanol in the hypoxia group were higher compared to the normoxia group. Additionally, the levels of 13-octadecenoic acid, octadecenoic acid, phenol, pentadecanoic acid, and myristic acid were increased in P. aeruginosa (+) group compared to the P. aeruginosa (-) group. Subgroup analysis based on the bronchiectasis severity index (BSI)reveled that the levels of 10-heptadecenoic acid, heptadecanoic acid, decanol, 13-octadecenoic acid, myristic acid, and pentadecanoic acid were higher in the severe group compared to the moderate group. Multivariate linear regression showed that 10-heptadecenoic acid and age were independent prognostic factors for bronchiectasis patients with hypoxia. Furthermore, octadecenoic acid, phenol and gender were identified as independent prognostic factors for bronchiectasis patients with P. aeruginosa isolation.

Conclusion

The study provides evidence that specific VOCs in EBC are correlated with the severity of bronchiectasis, and 10-heptadecenoic acid is shown to be a predictive marker for hypoxia condition in bronchiectasis patients.

The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis

Background

Lung cancer (LC) is the second-highest incidence and the first-highest mortality cancer worldwide. Early screening and precise treatment of LC have been the research hotspots in this field. Artificial intelligence (AI) technology has advantages in many aspects of LC and widely used such as LC early diagnosis, LC differential classification, treatment and prognosis prediction.

Objective

This study aims to analyze and visualize the research history, current status, current hotspots, and development trends of artificial intelligence in the field of lung cancer using bibliometric methods, and predict future research directions and cutting-edge hotspots.

Results

A total of 2931 articles published between 2003 and 2023 were included, contributed by 15,848 authors from 92 countries/regions. Among them, China (40%) with 1173 papers,USA (24.80%) with 727 papers and the India(10.2%) with 299 papers have made outstanding contributions in this field, accounting for 75% of the total publications. The primary research institutions were Shanghai Jiaotong University(n=66),Chinese Academy of Sciences (n=63) and Harvard Medical School (n=52).Professor Qian Wei(n=20) from Northeastern University in China were ranked first in the top 10 authors while Armato SG(n=458 citations) was the most co-cited authors. Frontiers in Oncology(121 publications; IF 2022,4.7; Q2) was the most published journal. while Radiology (3003 citations; IF 2022, 19.7; Q1) was the most co-cited journal. different countries and institutions should further strengthen cooperation between each other. The most common keywords were lung cancer, classification, cancer, machine learning and deep learning. Meanwhile, The most cited papers was Nicolas Coudray et al.2018.NAT MED(1196 Total Citations).

Conclusions

Research related to AI in lung cancer has significant application prospects, and the number of scholars dedicated to AI-related research on lung cancer is continually growing. It is foreseeable that non-invasive diagnosis and precise minimally invasive treatment through deep learning and machine learning will remain a central focus in the future. Simultaneously, there is a need to enhance collaboration not only among various countries and institutions but also between high-quality medical and industrial entities.

DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath

Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used clinical breath samples from 100 patients at a nationally recognized hospital to form the dataset. Data were then processed using a gradient boosting classifier model, and the performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, indicating an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. This has the potential to increase patient adherence to regular monitoring.

Alveolar gradients in breath analysis. A pilot study with comparison of room air and inhaled air by simultaneous measurements using ion mobility spectrometry

Analyzing exhaled breath samples, especially using a highly sensitive method such as MCC/IMS (multi-capillary column/ion mobility spectrometry), may also detect analytes that are derived from exogenous production. In this regard, there is a discussion about the optimal interpretation of exhaled breath, either by considering volatile organic compounds (VOCs) only in exhaled breath or by additionally considering the composition of room air and calculating the alveolar gradients. However, there are no data on whether the composition and concentration of VOCs in room air are identical to those in truly inhaled air directly before analyzing the exhaled breath. The current study aimed to determine whether the VOCs in room air, which are usually used for the calculation of alveolar gradients, are identical to the VOCs in truly inhaled air. For the measurement of inhaled air and room air, two IMS, each coupled with an MCC that provided a pre-separation of the VOCs, were used in parallel. One device was used for sampling room air and the other for sampling inhaled air. Each device was coupled with a newly invented system that cleaned room air and provided a clean carrier gas, whereas formerly synthetic air had to be used as a carrier gas. In this pilot study, a healthy volunteer underwent three subsequent runs of sampling of inhaled air and simultaneous sampling and analysis of room air. Three of the selected 11 peaks (P4-unknown, P5-1-Butanol, and P9-Furan, 2-methyl-) had significantly higher intensities during inspiration than in room air, and four peaks (P1-1-Propanamine, N-(phenylmethylene), P2-2-Nonanone, P3-Benzene, 1,2,4-trimethyl-, and P11-Acetyl valeryl) had higher intensities in room air. Furthermore, four peaks (P6-Benzaldehyde, P7-Pentane, 2-methyl-, P8-Acetone, and P10-2-Propanamine) showed inconsistent differences in peak intensities between inhaled air and room air. To the best of our knowledge, this is the first study to compare simultaneous sampling of room air and inhaled air using MCC/IMS. The simultaneous measurement of inhaled air and room air showed that using room air for the calculation of alveolar gradients in breath analysis resulted in different alveolar gradient values than those obtained by measuring truly inhaled air.

Novel Screening Tools for Lung Cancer

Screening with low-dose computed tomography has been shown to decrease lung cancer mortality. However, the issues of low detection rates and false positive results remain, highlighting the need for adjunctive tools in lung cancer screening. To this end, researchers have investigated easily applicable, minimally invasive tests with high validity. We herein review some of the more promising novel markers utilizing plasma, sputum, and airway samples.

Metabolic biomarkers in lung cancer screening and early diagnosis (Review)

Late diagnosis is one of the major contributing factors to the high mortality rate of lung cancer, which is now the leading cause of cancer-associated mortality worldwide. At present, low-dose CT (LDCT) screening in the high-risk population, in which lung cancer incidence is higher than that of the low-risk population is the predominant diagnostic strategy. Although this has efficiently reduced lung cancer mortality in large randomized trials, LDCT screening has high false-positive rates, resulting in excessive subsequent follow-up procedures and radiation exposure. Complementation of LDCT examination with biofluid-based biomarkers has been documented to increase efficacy, and this type of preliminary screening can potentially reduce potential radioactive damage to low-risk populations and the burden of hospital resources. Several molecular signatures based on components of the biofluid metabolome that can possibly discriminate patients with lung cancer from healthy individuals have been proposed over the past two decades. In the present review, advancements in currently available technologies in metabolomics were reviewed, with particular focus on their possible application in lung cancer screening and early detection.

Smelling the Disease: Diagnostic Potential of Breath Analysis

Breath analysis is a relatively recent field of research with much promise in scientific and clinical studies. Breath contains endogenously produced volatile organic components (VOCs) resulting from metabolites of ingested precursors, gut and air-passage bacteria, environmental contacts, etc. Numerous recent studies have suggested changes in breath composition during the course of many diseases, and breath analysis may lead to the diagnosis of such diseases. Therefore, it is important to identify the disease-specific variations in the concentration of breath to diagnose the diseases. In this review, we explore methods that are used to detect VOCs in laboratory settings, VOC constituents in exhaled air and other body fluids (e.g., sweat, saliva, skin, urine, blood, fecal matter, vaginal secretions, etc.), VOC identification in various diseases, and recently developed electronic (E)-nose-based sensors to detect VOCs. Identifying such VOCs and applying them as disease-specific biomarkers to obtain accurate, reproducible, and fast disease diagnosis could serve as an alternative to traditional invasive diagnosis methods. However, the success of VOC-based identification of diseases is limited to laboratory settings. Large-scale clinical data are warranted for establishing the robustness of disease diagnosis. Also, to identify specific VOCs associated with illness states, extensive clinical trials must be performed using both analytical instruments and electronic noses equipped with stable and precise sensors.

GC-MS analysis of exhaled gas for fine detection of inflammatory diseases

Exhaled gas analysis is a non-invasive test ideal for continuous monitoring of biological metabolic information. We analyzed the exhaled gas of patients with inflammatory diseases for trace gas components that could serve as biomarkers that enable early detection of inflammatory diseases and assessment of treatment efficacy. Furthermore, we examined the clinical potential of this method. We enrolled 34 patients with inflammatory disease and 69 healthy participants. Volatile components from exhaled gas were collected and analyzed by a gas chromatography-mass spectrometry system, and the data were examined for gender, age, inflammatory markers, and changes in markers before and after treatment. The data were tested for statistical significance through discriminant analysis by Volcano plot, Analysis of variance test, principal component analysis, and cluster analysis comparing healthy and patient groups. There were no significant differences in the trace components of exhaled gas by gender or age. However, we found differences in some components of the exhaled gas between healthy and untreated patients. In addition, after treatment, gas patterns including the patient-specific components changed to a state closer to the inflammation-free status. We identified trace components in the exhaled gas of patients with inflammatory diseases and found that some of these regressed after treatment.

Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose: A Multicenter Validation Study

BACKGROUND

Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies.

RESEARCH QUESTION

This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer?

STUDY DESIGN AND METHODS

In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data.

RESULTS

A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86.

INTERPRETATION

Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer.

CLINICAL TRIAL REGISTRATION

The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025.

Lateral flow assays for detection of disease biomarkers

Early diagnosis saves lives in many diseases. In this sense, monitoring of biomarkers is crucial for the diagnosis of diseases. Lateral flow assays (LFAs) have attracted great attention among paper-based point-of-care testing (POCT) due to their low cost, user-friendliness, and time-saving advantages. Developments in the field of health have led to an increase of interest in these rapid tests. LFAs are used in the diagnosis and monitoring of many diseases, thanks to biomarkers that can be observed in body fluids. This review covers the recent advances dealing with the design and strategies for the development of LFA for the detection of biomarkers used in clinical applications in the last 5 years. We focus on various strategies such as choosing the nanoparticle type, single or multiple test approaches, and equipment for signal transducing for the detection of the most common biomarkers in different diseases such as cancer, cardiovascular, infectious, and others including Parkinson's and Alzheimer's diseases. We expect that this study will contribute to the different approaches in LFA and pave the way for other clinical applications.

Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities

Non-invasive, simple, and fast tests for lung cancer diagnostics are one of the urgent needs for clinical practice. The work describes the results of exhaled breath analysis of 112 lung cancer patients and 120 healthy individuals using gas chromatography-mass spectrometry (GC-MS). Volatile organic compound (VOC) peak areas and their ratios were considered for data analysis. VOC profiles of patients with various histological types, tumor localization, TNM stage, and treatment status were considered. The effect of non-pulmonary comorbidities (chronic heart failure, hypertension, anemia, acute cerebrovascular accident, obesity, diabetes) on exhaled breath composition of lung cancer patients was studied for the first time. Significant correlations between some VOC peak areas and their ratios and these factors were found. Diagnostic models were created using gradient boosted decision trees (GBDT) and artificial neural network (ANN). The performance of developed models was compared. ANN model was the most accurate: 82-88% sensitivity and 80-86% specificity on the test data.

Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields.

The Use of Breath Analysis in the Management of Lung Cancer: Is It Ready for Primetime?

Breath analysis is a promising non-invasive method for the detection and management of lung cancer. Exhaled breath contains a complex mixture of volatile and non-volatile organic compounds that are produced as end-products of metabolism. Several studies have explored the patterns of these compounds and have postulated that a unique breath signature is emitted in the setting of lung cancer. Most studies have evaluated the use of gas chromatography and mass spectrometry to identify these unique breath signatures. With recent advances in the field of analytical chemistry and machine learning gaseous chemical sensing and identification devices have also been created to detect patterns of odorant molecules such as volatile organic compounds. These devices offer hope for a point-of-care test in the future. Several prospective studies have also explored the presence of specific genomic aberrations in the exhaled breath of patients with lung cancer as an alternative method for molecular analysis. Despite its potential, the use of breath analysis has largely been limited to translational research due to methodological issues, the lack of standardization or validation and the paucity of large multi-center studies. It is clear however that it offers a potentially non-invasive alternative to investigations such as tumor biopsy and blood sampling.

Lung Cancer Diagnosis System Based on Volatile Organic Compounds (VOCs) Profile Measured in Exhaled Breath

Lung cancer is one of the world’s lethal diseases and detecting it at an early stage is crucial and difficult. This paper proposes a computer-aided lung cancer diagnosis system using volatile organic compounds (VOCs) data. A silicon microreactor, which consists of thousands of micropillars coated with an ammonium aminooxy salt, is used to capture the volatile organic compounds (VOCs) in the patients’ exhaled breath by means of oximation reactions. The proposed system ranks the features using the Pearson correlation coefficient and maximum relevance–minimum redundancy (mRMR) techniques. The selected features are fed to nine different classifiers to determine if the lung nodule is malignant or benign. The system is validated using a locally acquired dataset that has 504 patients’ data. The dataset is balanced and has 27 features of volatile organic compounds (VOCs). Multiple experiments were completed, and the best accuracy result is 87%, which was achieved using random forest (RF) either by using all 27 features without selection or by using the first 17 features obtained using maximum relevance–minimum redundancy (mRMR) while using an 80–20 train-test split. The correlation coefficient, maximum relevance–minimum redundancy (mRMR), and random forest (RF) importance agreed that C4H8O (2-Butanone) ranks as the best feature. Using only C4H8O (2-Butanone) for training, the accuracy results using the support vector machine, logistic regression, bagging and neural network classifiers are 86%, which approaches the best result. This shows the potential for these volatile organic compounds (VOCs) to serve as a significant screening tests for the diagnosis of lung cancer.

Exhaled Breath Volatile Organic Compound Analysis for the Detection of Lung Cancer- A Systematic Review

A rapid and effective diagnostic method is essential for lung cancer since it shows symptoms only at its advanced stage. Research is being carried out in the area of exhaled breath analysis for the diagnosis of various pulmonary diseases including lung cancer. In this method exhaled breath volatile organic compounds (VOC) are analyzed with various techniques such as gas chromatography-mass spectrometry, ion mobility spectrometry, and electronic noses. The VOC analysis is suitable for lung cancer detection since it is non-invasive, fast, and also a low-cost method. In addition, this technique can detect primary stage nodules. This paper presents a systematic review of the various method employed by researchers in the breath analysis field. The articles were selected through various search engines like EMBASE, Google Scholar, Pubmed, and Google. In the initial screening process, 214 research papers were selected using various inclusion and exclusion criteria and finally, 55 articles were selected for the review. The results of the reviewed studies show that detection of lung cancer can be effectively done using the VOC analysis of exhaled breath. The results also show that this method can be used for detecting the different stages and histology of lung cancer. The exhaled breath VOC analysis technique will be popular in the future, bypassing the existing imaging techniques. This systematic review conveys the recent research opportunities, obstacles, difficulties, motivations, and suggestions associated with the breath analysis method for lung cancer detection.

Olfactometric diagnosis of lung cancer by canine scent - A double-blinded study

BACKGROUND

Lung cancer is one of the most often diagnosed tumours in the world with the highest mortality. A major problem and reason for the high mortality from lung cancer is its diagnosis in the late stages. The main goal of preventing lung cancer deaths is early detection in the early stages and accurate diagnosis, which must be followed by targeted treatment. Nevertheless, even top diagnostic techniques do not have the same accuracy and sensitivity as a dog's sense of smell.

METHODS

The study aims to present the results of olfactometric detection of lung cancer using the smell of dogs in unblinded, single-blinded and double-blinded studies. 115 serum samples or breath from patients with lung cancer and 101 samples from healthy people were used for the training. The group consisted of women and men of Indo-European origin, mostly from the Moravian-Silesian region in Czech Republic. Two dogs were selected for the study.

RESULTS

In the case of tumor detection in the form of unblinded tests, Bugs had a sensitivity of 91% and a specificity of 92%. Boolomo had a sensitivity of 89% and a specificity of 81%. For single-blinded tests, Bugs had a sensitivity of 71%. The sensitivity of Boolomo was set at 90%. After meeting the sensitivity limit of 70%, dogs were included in the double-blinded studies. The highest accuracy was set at 68% for Bugs, 83% for Boolomo.

CONCLUSION

When a tumour is diagnosed in the late stages, it is a great burden on both the health and economic systems of the state. Unfortunately, there is still no suitable screening test to detect the tumour at an early stage, so any other method of detection seems desirable. Trained dogs are used in many fields, why not also in medicine and the diagnosis of tumours?

Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) fingerprinting enabled treatment monitoring of pulmonary carcinoma cells in real time

Lung cancer is one of the leading causes of cancer related deaths in the United States. A novel volatile analysis platform is needed to complement current diagnostic techniques and better elucidate chemical signatures of lung cancer and subsequent treatments. A systems biology bottom-up approach using cell culture volatilomics was employed to identify pathological volatile fingerprints of lung cancer in real time. An advanced secondary electrospray ionization (SESI) source, named SuperSESI was used in this study and directly attached to a Thermo Q-Exactive high-resolution mass spectrometer (HRMS). We performed a series of experiments to determine if our optimized SESI-HRMS platform can distinguish between cancer types by sampling their in vitro volatilome profiles. We detected 60 significant volatile organic compound (VOC) features in positive mode that were deemed of cancer cell origin. The cell derived features were used for subsequent analyses to distinguish between our two studied lung cancer types, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Partial least squares-discriminant analysis (PLS-DA) model revealed a good separation of the two cancer types, suggesting unique chemical composition of their headspace profiles. A receiver operating characteristic (ROC) curve using 10 prominent features was used to predict disease type, with an area under the curve (AUC) of 0.811. Cultures were also treated with cisplatin to determine the feasibility of classifying drug treatment from expelled gases. A PLS-DA model revealed independent clustering based on their headspace profiles. An ROC curve using the top features driving separation of PLS-DA model suggested good accuracy with an AUC of 1. It is thus possible to benefit from the advantages of this platform to distinguish the unique volatile fingerprints of cancers to uncover potential biomarkers for cancer type differentiation and treatment monitoring.

Volatolomics in healthcare and its advanced detection technology

Various diseases increasingly challenge the health status and life quality of human beings. Volatolome emitted from patients has been considered as a potential family of markers, volatolomics, for diagnosis/screening. There are two fundamental issues of volatolomics in healthcare. On one hand, the solid relationship between the volatolome and specific diseases needs to be clarified and verified. On the other hand, effective methods should be explored for the precise detection of volatolome. Several comprehensive review articles had been published in this field. However, a timely and systematical summary and elaboration is still desired. In this review article, the research methodology of volatolomics in healthcare is critically considered and given out, at first. Then, the sets of volatolome according to specific diseases through different body sources and the analytical instruments for their identifications are systematically summarized. Thirdly, the advanced electronic nose and photonic nose technologies for volatile organic compounds (VOCs) detection are well introduced. The existed obstacles and future perspectives are deeply thought and discussed. This article could give a good guidance to researchers in this interdisciplinary field, not only understanding the cutting-edge detection technologies for doctors (medicinal background), but also making reference to clarify the choice of aimed VOCs during the sensor research for chemists, materials scientists, electronics engineers, etc.

Simultaneous measurement of inhaled air and exhaled breath by double multicapillary column ion-mobility spectrometry, a new method for breath analysis: results of a feasibility study

The high sensitivity of the methods applied in breath analysis entails a high risk of detecting analytes that do not derive from endogenous production. Consequentially, it appears useful to have knowledge about the composition of inhaled air and to include alveolar gradients into interpretation.

The current study aimed to standardise sampling procedures in breath analysis, especially with multicapillary column ion-mobility spectrometry (MCC-IMS), by applying a simultaneous registration of inhaled air and exhaled breath.

A “double MCC-IMS” device, which for the first time allows simultaneous analysis of inhaled air and exhaled breath, was developed and tested in 18 healthy individuals. For this, two BreathDiscovery instruments were coupled with each other.

Measurements of inhaled air and exhaled breath in 18 healthy individuals (mean age 46±10.9 years; nine men, nine women) identified 35 different volatile organic compounds (VOCs) for further analysis. Not all of these had positive alveolar gradients and could be regarded as endogenous VOCs: 16 VOCs had a positive alveolar gradient in mean; 19 VOCs a negative one. 12 VOCs were positive in >12 of the healthy subjects.

For the first time in our understanding, a method is described that enables simultaneous measurement of inhaled air and exhaled breath. This facilitates the calculation of alveolar gradients and selection of endogenous VOCs for exhaled breath analysis. Only a part of VOCs in exhaled breath are truly endogenous VOCs. The observation of different and varying polarities of the alveolar gradients needs further analysis.

Simultaneous analysis of inhaled air and exhaled breath by a newly invented double MCC-IMS device shows that exhaled breath contains confounding exogeneous analytes and only a smaller number of truly endogenous VOCs, which can be used for further analysishttps://bit.ly/3HGVzV5

Exhaled breath analysis using GC-MS and an electronic nose for lung cancer diagnostics

Exhaled breath analysis is an interesting and promising approach for the diagnostics of various diseases. Being non-invasive, convenient and simple, this approach has tremendous potential utility for further translation into clinical practice. In this study, gas chromatography-mass spectrometry (GC-MS) and quartz microbalance sensor-based "electronic nose" were applied for analysis of the exhaled breath of 40 lung cancer patients and 40 healthy individuals. It was found that the electronic nose was unable to distinguish the samples of different groups. However, the application of GC-MS allowed identifying statistically significant differences in compound peak areas and their ratios for investigated groups. Diagnostic models were created using random forest classifier based on peak areas and their ratios with the sensitivity and specificity of peak areas (ratios) of 85.7-96.5% (75.0-93.1%) and 73.3-85.1% (90.0-92.5%) on training data and 63.6-75.0% (72.7-100.0%) and 50.0-69.2% (76.9-84.6%) on test data, respectively. The exhaled breath samples of lung cancer patients and healthy volunteers could be distinguished by GC-MS with the use of individual compounds, but application of their ratios could help to determine specific differences between investigated groups and the level the influence of individual metabolism features alternating from one person to another as well as daily instrument reproducibility deviations. The electronic nose has to be significantly improved to apply it to lung cancer diagnostics of exhaled breath analysis and the influence of water vapour has to be lowered to increase the sensitivity of the sensors to detect lung cancer biomarkers.

Breath Biomarkers in Diagnostic Applications

The detection of chemical compounds in exhaled human breath presents an opportunity to determine physiological state, diagnose disease or assess environmental exposure. Recent advancements in metabolomics research have led to improved capabilities to explore human metabolic profiles in breath. Despite some notable challenges in sampling and analysis, exhaled breath represents a desirable medium for metabolomics applications, foremost due to its non-invasive, convenient and practically limitless availability. Several breath-based tests that target either endogenous or exogenous gas-phase compounds are currently established and are in practical and/or clinical use. This review outlines the concept of breath analysis in the context of these unique tests and their applications. The respective breath biomarkers targeted in each test are discussed in relation to their physiological production in the human body and the development and implementation of the associated tests. The paper concludes with a brief insight into prospective tests and an outlook of the future direction of breath research.

Mass spectrometry based proteome profiling of the exhaled breath condensate for lung cancer biomarkers search

INTRODUCTION

Lung cancer remains the most prevalent cause of cancer mortality worldwide mainly due to insufficient availability of early screening methods for wide-scale application. Exhaled breath condensate (EBC) is currently considered as one of the promising targets for early screening and is particularly attractive due to its absolutely noninvasive collection and possibility for long-term frozen storage. EBC proteome analysis can provide valuable information about the (patho)physiological changes in the respiratory system and may help to identify in time a high risk of lung cancer. Mass spectrometry (MS) profiling of EBC proteome seems to have no alternative in obtaining the most extensive data and characteristic marker panels for screening.

AREAS COVERED

This special report summarizes the data of several proteomic studies of EBC in normal and lung cancer (from 2012 to 2021, PubMed), focuses on the possible reasons for the significant discrepancy in the results, and discusses some aspects for special attention in further studies.

EXPERT OPINION

The significant discrepancy in the results of various studies primarily highlights the need to create standardized protocols for the collection and preparation of EBC for proteomic analysis. The application of quantitative and targeted LC-MS/MS based approaches seems to be the most promising in further EBC proteomic studies.

Study of confounding factors influence on lung cancer diagnostics effectiveness using gas chromatography-mass spectrometry analysis of exhaled breath

Aim: The purpose of this study was to estimate volatile organic compounds (VOCs) ability to distinguish exhaled breath samples of lung cancer patients and healthy volunteers and to assess the effect of smoking status and gender on parameters. Patients & methods: Exhaled breath samples of 40 lung cancer patients and 40 healthy individuals were analyzed using gas chromatography-mass spectrometry. Influence of other factors on the exhaled breath VOCs profile was investigated. Results: Some parameters correlating with the disease status were affected by other factors. Excluding these parameters allows creating a logistic regression diagnostic model with 83% sensitivity and 81% specificity. Conclusion: Influence of other factors on the exhaled breath VOCs profile has to be taken into account to avoid misleading results.

ZEUS (ZIF-based electrochemical ultrasensitive screening) device for isopentane analytics with focus on lung cancer diagnosis

Breath analytics is currently being explored for the development of point-of-care devices in non-invasive disease detection. It is based on the measurement of volatile organic compounds (VOCs) and gases that are produced by the body because of the metabolic pathways. The levels of these metabolites vary due to alteration in the endogenous oxidative stress-related metabolic pathways and can be correlated to understand the underlying disease condition. The levels of exhaled hydrocarbons in human breath can be used to design a rapid, easy to use method for lung cancer detection. This work outlines the development of an electrochemical sensing platform that can be used for the non-invasive diagnosis of lung cancer by monitoring isopentane levels in breath. This electrochemical sensor platform involves the use of [BMIM]BF4@ZIF-8 for sensing the target analyte. This synthesized nanocomposite offers advantages for gas sensing applications as it possesses unique properties such as an electrochemically active Room Temperature Ionic Liquid (RTIL) and a crosslinking Metal Organic Framework (MOF) that provides increased surface area for gas absorption. This is the first report of a hydrocarbon-based sensor platform developed for lung cancer diagnosis. The developed sensor platform displays sensitivity and specificity for the detection of isopentane up to 600 parts-per-billion. We performed structural and morphological characterization of the synthesized nanocomposite using various analytical techniques such as PXRD, FESEM, FTIR, and DLS. We further analyzed the electrochemical activity of the synthesized nanocomposite using a standard glassy carbon electrode. The application of the nanocomposite for isopentane sensing was done using a commercially available carbon screen printed electrode. The results so obtained helped in strengthening our hypothesis and serve as a proof-of-concept for the development of a breathomics-enabled electrochemical strategy. We illustrated the specificity of the developed nanocomposite by cross-reactivity studies. We envision that the detection platform will allow sensitive and specific sensing of isopentane levels such that it can used for point of care applications in noninvasive and early diagnosis of lung cancer, thereby leading to its early treatment and decrease in mortality rate.

Exploring Volatile Organic Compounds in Breath for High-Accuracy Prediction of Lung Cancer

(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants' exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.

Calculated indices of volatile organic compounds (VOCs) in exhalation for lung cancer screening and early detection

BACKGROUND

Breath analysis is a promising noninvasive technique that offers a wide range of opportunities to facilitate early diagnosis of lung cancer (LC).

METHOD

Exhaled breath samples of 352 subjects including 160 with lung cancer (LC), 70 with benign pulmonary nodule (BPN) and 122 healthy controls (HC) were analyzed through thermal desorption coupled with gas chromatography-mass spectrometry (TD-GC-MS) to obtain the metabolic information from volatile organic compounds (VOCs). Statistical classification models were used to find diagnostic clusters of VOCs for the discrimination of HC, BPN and LC patients' early and advanced stages, as well as subtypes of LC. Receiver operator characteristics (ROC) curves with 5-fold validations were used to evaluate the accuracy of these models.

RESULTS

The analysis revealed that 20, 19, 19, and 20 VOCs discriminated LC from HC, LC from BPN, histology and LC stages respectively. The calculated diagnostic indices showed a large area under the curve (AUC) to distinguish HC from LC (AUC: 0.987, 95 % confidence interval (CI): 0.976-0.997), BPN from LC (AUC: 0.809, 95 % CI: 0.758-0.860), NSCLC from SCLC (AUC: 0.939, 95 % CI: 0.875-0.995) and Stage III from stage III-IV (AUC: 0.827, 95 % CI: 0.768-0.886). The comparison between the high-risk groups (BPN and HC smokers) and early stages LC resulted in the AUC of 0.756 (95 %CI: 0.681-0.817) for BPN vs. early stage LC and AUC of 0.986 (95 % CI: 0.972-0.994) for HC smoker vs. early stage LC.

CONCLUSION

Volatome of breath of the LC patients was significantly different from that of both BPN patients and HC and showed an ability of distinguishing early from advance stage LC and NSCLC from SCLC. We conclude that the volatome has a potential to help improve early diagnosis of LC.

Detection of lung cancer with electronic nose using a novel ensemble learning framework

Breath analysis based on electronic nose (e-nose) is a promising new technology for the detection of lung cancer that is non-invasive, simple to operate and cost-effective. Lung cancer screening by e-nose relies on predictive models established using machine learning methods. However, using only a single machine learning method to detect lung cancer has some disadvantages, including low detection accuracy and high false negative rate. To address these problems, groups of individual learning models with excellent performance were selected from classic models, including Support Vector Machine, Decision Tree, Random Forest, Logistic Regression and K-nearest neighbor regression, to build an ensemble learning framework (PCA-SVE). The output result of the PCA-SVE framework was obtained by voting. To test this approach, we analyzed 214 breath samples measured by e-nose with 11 gas sensors of four types using the proposed PCA-SVE framework. Experimental results indicated that the accuracy, sensitivity, and specificity of the proposed framework were 95.75%, 94.78%, and 96.96%, respectively. This framework overcomes the disadvantages of a single model, thereby providing an improved, practical alternative for exhaled breath analysis by e-nose.

Al-Doped MoSe2 Monolayer as a Promising Biosensor for Exhaled Breath Analysis: A DFT Study

Exhaled breath analysis by nanosensors is a workable and rapid manner to diagnose lung cancer in the early stage. In this paper, we proposed Al-doped MoSe2 (Al-MoSe2) as a promising biosensor for sensing three typically exhaled volatile organic compounds (VOCs) of lung cancer, namely, C3H4O, C3H6O, and C5H8, using the density functional theory (DFT) method. Single Al atom is doped on the Se-vacancy site of the MoSe2 surface, which behaves as an electron-donor and enhances the electrical conductivity of the nanosystem. The adsorption and desorption performances, electronic behavior, and the thermostability of the Al-MoSe2 monolayer are conducted to fully understand its physicochemical properties as a sensing material. The results indicate that the Al-MoSe2 monolayer shows admirable sensing performances with C3H4O, C3H6O, and C5H8 with responses of -85.7, -95.6, and -96.3%, respectively. Also, the desirable adsorption performance and the thermostability endow with the Al-MoSe2 monolayer with good sensing and desorbing behaviors for the recycle detection of three VOCs. We are hopeful that the results in this paper could provide some guidance to the experimentalists fulfilling their exploration in the practical application, which can also broaden the exploration of transition-metal dichalcogenides (TMDs) in more fields as well.

Volatile organic compound breath testing detects in-situ squamous cell carcinoma of bronchial and laryngeal regions and shows distinct profiles of each tumour

Volatile organic compound (VOC) breath testing of lung and head and neck squamous cell carcinoma (SCC) has been widely studied, however little is known regarding VOC profiles of in-situ SCC. A prospective study of VOC in patients with histologically proven SCC, either in-situ or advanced, and controls. Breath samples were analysed using the E-nose Cyranose ^®320 and by gas chromatography/mass spectroscopy. Predictive models were developed using bootstrap forest using all 32 sensors. Data from 55 participants was analysed: 42 SCC cases comprising 20 bronchial (10 in-situ, 10 advanced) and 22 laryngeal (12 in-situ, 10 advanced), and 13 controls. There were 32 (76%) male SCC cases with mean age 63.6 (SD = 9.5) compared with 11 (85%) male controls with mean age 61.9 (SD = 10.1). Predictive models for in situ cases had good sensitivity and specificity compared to controls (overall, 95% and 69%; laryngeal, 100% and 85%; bronchial, 77% and 80%). When distinguishing in-situ and advanced tumours, sensitivity and specificity 82% and 75% respectively. For different tumour types (bronchial versus advanced laryngeal) sensitivity and specificity were 100% and 80% respectively. VOCs isolated from in-situ cancers included some previously demonstrated in advanced cancers and some novel VOCs. In-situ bronchial and laryngeal cancer can be detected by VOC analysis. Distinction from normal controls and between the two tumour types could allow screening in high risk groups for these curable lesions.

Gas chromatography-mass spectrometry untargeted profiling of non-Hodgkin's lymphoma urinary metabolite markers

Non-Hodgkin's lymphoma (NHL) is a cancer of the lymphatic system where the lymphoid and hematopoietic tissues are infiltrated by malignant neoplasms of B, T, and natural killer lymphocytes. Effective and less invasive methods for NHL screening are urgently needed. Herein, we report an untargeted gas chromatography-mass spectrometry (GC-MS) method to investigate metabolic changes in non-volatile derivatized compounds from urine samples of NHL patients (N = 15) and compare them to healthy controls (N = 34). Uni- and multivariate data analysis showed 18 endogenous metabolites, including amino acids and their metabolites, sugars, small organic acids, and vitamins, as statistically significant for group differentiation. A receiver operating characteristic curve (ROC) generated from a support vector machine (SVM) algorithm-based model achieved 0.998 of predictive accuracy, displaying the potential and relevance of GC-MS-detected urinary non-volatile compounds for predictive purposes. Furthermore, a specific panel of key metabolites was also evaluated, showing similar results. All in all, our results indicate that this robust GC-MS method is an effective screening tool for NHL diagnosis and it is able to highlight different pathways of the disease. Graphical Abstract.

Volatile organic compounds in human matrices as lung cancer biomarkers: a systematic review

Volatile organic compounds (VOCs) have shown potential as non-invasive breath biomarkers for lung cancer, but their unclear biological origin currently limits clinical applications. This systematic review explores headspace analysis of VOCs in patient-derived body fluids and lung cancer cell lines to pinpoint lung cancer-specific VOCs and uncover their biological origin. A search was performed in the databases MEDLINE and Web of Science. Twenty-two articles were included in this systematic review. Since there is no standardised approach to analyse VOCs, a plethora of techniques and matrices/cell lines were explored, which is reflected in the various VOCs identified. However, comparing VOCs in the headspace of urine, blood and pleural effusions from patients and lung cancer cell lines showed some overlapping VOCs, indicating their potential use in lung cancer diagnosis. This review demonstrates that VOCs are promising biomarkers for lung cancer. However, due to lack of inter-matrix consensus, standardised prospective trials will have to be conducted to validate clinically relevant biomarkers.

Investigation of different approaches for exhaled breath and tumor tissue analyses to identify lung cancer biomarkers

Development of early noninvasive methods for lung cancer diagnosis is among the most promising technologies, especially using exhaled breath as an object of analysis. Simple sample collection combined with easy and quick sample preparation, as well as the long-term stability of the samples, make it an ideal choice for routine analysis. The conditions of exhaled breath analysis by preconcentrating volatile organic compounds (VOCs) in sorbent tubes, two-stage thermal desorption and gas-chromatographic determination with flame-ionization detection have been optimized. These conditions were applied to estimate differences in exhaled breath VOC profiles of lung cancer patients and healthy volunteers. The combination of statistical methods was used to evaluate the ability of VOCs and their ratios to classify lung cancer patients and healthy volunteers. The performance of diagnostic models on the test data set was greater than 90 % for both VOC peak areas and their ratios. Some of the exhaled breath samples were analyzed using gas chromatography coupled with mass spectrometry (GC-MS) to identify VOCs present in exhaled breath at lower concentration levels. To confirm the endogenous origin of VOCs found in exhaled breath, GC-MS analysis of tumor tissues was conducted. Some of the VOCs identified in exhaled breath were found in tumor tissues, but their frequency of occurrence was significantly lower than in the case of exhaled breath.

Emerging non-invasive detection methodologies for lung cancer

The potential for non-invasive lung cancer (LC) diagnosis based on molecular, cellular and volatile biomarkers has been attracting increasing attention, with the development of advanced techniques and methodologies. It is standard practice to tailor the treatments of LC for certain specific genetic alterations, including the epidermal growth factor receptor, anaplastic lymphoma kinase and BRAF genes. Despite these advances, little is known about the internal mechanisms of different types of biomarkers and the involvement of their related biochemical pathways during the development of LC. The development of faster and more effective techniques is essential for the identification of different biomarkers. The present review summarizes some of the latest methods used for detecting molecular, cellular and volatile biomarkers in LC and their potential use in clinical diagnosis and targeted therapy.

Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters

Introduction

Exhaled-breath analysis of volatile organic compounds could detect lung cancer earlier, possibly leading to improved outcomes. Combining exhaled-breath data with clinical parameters may improve lung cancer diagnosis.

Methods

Based on data from a previous multi-centre study, this article reports additional analyses. 138 subjects with non-small cell lung cancer (NSCLC) and 143 controls without NSCLC breathed into the Aeonose. The diagnostic accuracy, presented as area under the receiver operating characteristic curve (AUC-ROC), of the Aeonose itself was compared with 1) performing a multivariate logistic regression analysis of the distinct clinical parameters obtained, and 2) using this clinical information beforehand in the training process of the artificial neural network (ANN) for the breath analysis.

Results

NSCLC patients (mean±sd age 67.1±9.1 years, 58% male) were compared with controls (62.1±7.0 years, 40.6% male). The AUC-ROC of the classification value of the Aeonose itself was 0.75 (95% CI 0.69–0.81). Adding age, number of pack-years and presence of COPD to this value in a multivariate regression analysis resulted in an improved performance with an AUC-ROC of 0.86 (95% CI 0.81–0.90). Adding these clinical variables beforehand to the ANN for classifying the breath print also led to an improved performance with an AUC-ROC of 0.84 (95% CI 0.79–0.89).

Conclusions

Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose, either post hoc in a multivariate regression analysis or a priori to the ANN, significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer.

Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose significantly improves the diagnostic accuracy to detect the presence or absence of lung cancerhttp://bit.ly/38ps6fH

Non-invasive Biodiversified Sensors: A Modernized Screening Technology for Cancer

Background:

Biological sensors revolutionize the method of diagnoses of diseases from early to final stages using the biomarkers present in the body. Biosensors are advantageous due to the involvement of minimal sample collection with improved specificity and sensitivity for the detection of biomarkers.

Methods:

Conventional biopsies restrict problems like patient non-compliance, cross-infection and high cost and to overcome these issues biological samples like saliva, sweat, urine, tears and sputum progress into clinical and diagnostic research for the development of non-invasive biosensors. This article covers various non-invasive measurements of biological samples, optical-based, mass-based, wearable and smartphone-based biosensors for the detection of cancer.

Results:

The demand for non-invasive, rapid and economic analysis techniques escalated due to the modernization of the introduction of self-diagnostics and miniature forms of devices. Biosensors have high sensitivity and specificity for whole cells, microorganisms, enzymes, antibodies, and genetic materials.

Conclusion:

Biosensors provide a reliable early diagnosis of cancer, which results in faster therapeutic outcomes with in-depth fundamental understanding of the disease progression.

NOS.E: A New Fast Response Electronic Nose Health Monitoring System

We present a practical electronic nose (e-nose) sys-tem, NOS.E, for the rapid detection and identification of human health conditions. By detecting the changes in the composition of an individual's respiratory gases, which have been shown to be linked to changes in metabolism, e-nose systems can be used to characterize the physical health condition. We demonstrated our system's viability with a simple data set consists of breath collected under three different scenarios from one volunteer. Our preliminary results show the popular classifier SVM can discriminate NOS.E's responses under the three scenarios with high performance. In future work, we will aim to gather a more varied data set to test NOS.E's abilities.

Exhaled breath condensate biomarkers for lung cancer

Lung cancer is the main cause of cancer incidence and mortality worldwide and the identification of clinically useful biomarkers for lung cancer detection at both early and metastatic stage is a pressing medical need. Although many improvements have been made in the treatment and in the early screening of this cancer, most diagnosis are made at a late stage, when a lot of genetic and epigenetic changes have occurred. A promising source of biomarkers reflective of the pathogenesis of lung cancer is exhaled breath condensate (EBC), a biological fluid and a natural matrix of the respiratory tract. Molecules such as DNAs, RNAs, proteins, metabolites and volatile compounds are present in EBC, and their presence/absence or their variation in concentrations can be used as biomarkers. The aims of this review are to briefly describe exhaled breath composition, firstly, and then to document some of the EBC candidate biomarkers for lung cancer by dividing them according to their origin (genome, transcriptome, epigenome, metabolome, proteome and microbiota) in order to demonstrate the potential use of EBC as a helpful tool in cancer diagnostics, molecular profiling, therapy monitoring and screening of high risk individuals.

A volatile biomarker in breath predicts lung cancer and pulmonary nodules

BACKGROUND

previous studies have reported volatile organic compounds (VOCs) in the breath as apparent biomarkers of lung cancer. We tested the hypothesis that a robust breath VOC biomarker of lung cancer should also predict pulmonary nodules in chest CT images.

METHODS

Biomarker discovery study (unblinded): 301 subjects were screened for lung cancer with low dose chest CT (LDCT), and donated duplicate samples of alveolar breath for analysis with gas chromatography mass spectrometry (GC MS). Monte Carlo analysis of breath chromatograms revealed a mass ion as a biomarker that identified biopsy-proven lung cancer as well as suspicious pulmonary nodules on LDCT. The biomarker was termed Mass Abnormalities in Gaseous Ions with Imaging Correlates (MAGIIC). The chemical structure of MAGIIC was tentatively identified from the NIST library of mass spectra; the best-fit compounds included C4 and C5 alkane derivatives that were consistent with metabolic products of oxidative stress. Blinded validation of MAGIIC: the abundance of the MAGIIC biomarker was determined in a different group of 161 subjects undergoing screening with LDCT. They donated duplicate alveolar breath VOC samples that were analyzed at two independent laboratories. The study was blinded and monitored with Good Clinical Practice. The abundance of MAGIIC in breath predicted biopsy-proven lung cancer with 84% accuracy, sensitivity = 75.4% and specificity = 85.0%. MAGIIC also predicted pulmonary nodules in LDCT with 80.5% accuracy, sensitivity = 80.1% and specificity = 75.0%. Breath MAGIIC abundance was not significantly affected by tobacco smoking history.

CONCLUSIONS

in a blinded study, breath VOC MAGIIC accurately predicted lung cancer confirmed on a tissue biopsy, as well as suspicious pulmonary nodules observed on LDCT. MAGIIC may have been a product of oxidative stress and it could potentially be employed as an ancillary to LDCT to predict the likelihood that a pulmonary nodule is malignant.

Critical Review of Volatile Organic Compound Analysis in Breath and In Vitro Cell Culture for Detection of Lung Cancer

Breath analysis is a promising technique for lung cancer screening. Despite the rapid development of breathomics in the last four decades, no consistent, robust, and validated volatile organic compound (VOC) signature for lung cancer has been identified. This review summarizes the identified VOC biomarkers from both exhaled breath analysis and in vitro cultured lung cell lines. Both clinical and in vitro studies have produced inconsistent, and even contradictory, results. Methodological issues that lead to these inconsistencies are reviewed and discussed in detail. Recommendations on addressing specific issues for more accurate biomarker studies have also been made.

Exhaled breath testing - A tool for the clinician and researcher

Exhaled breath is a robust matrix of biomarkers divided between three fractions - gaseous breath, volatile breath, and breath condensate. Breath is collected non-invasively through bags (for gaseous breath), cold condensation chambers (breath condensate), and adsorbent traps (volatile breath). Due to the incredibly dilute nature of breath matrices, breath biomarker analysis requires precise analytical techniques, highly sensitive technology and often challenges the limit of detection of even the most advanced assays. Interest and advances in breath collection, analysis, and use have increased in recent years largely due to advances in analytical technology. Approved and validated breath tests are available as tools for researchers and clinicians. Novel development is ongoing. This article reviews the current applications for exhaled breath biomarkers.

Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC × GC-TOFMS

Lung cancer is the deadliest cancer in developed countries. To reduce its mortality rate, it is important to enhance our capability to detect it at earlier stages by developing early diagnostic methods. In that context, the analysis of exhaled breath is an interesting approach because of the simplicity of the medical act and its non-invasiveness. Thermal desorption comprehensive two-dimensional gas chromatography time of flight mass spectrometry (TD-GC × GC-TOFMS) has been used to characterize and compare the volatile content of human breath of lung cancer patients and healthy volunteers. On the sampling side, the contaminations induced by the bags membrane and further environmental migration of VOCs during and after the sampling have also been investigated. Over a realistic period of 6 h, the concentration of contaminants inside the bag can increase from 2 to 3 folds based on simulated breath samples. On the data processing side, Fisher ratio (FR) and random forest (RF) approaches were applied and compared in regards to their ability to reduce the data dimensionality and to extract the significant information. Both approaches allow to efficiently smooth the background signal and extract significant features (27 for FR and 17 for RF). Principal component analysis (PCA) was used to evaluate the clustering capacity of the different models. For both approaches, a separation along PC-1 was obtained with a variance score around 35%. The combined model provides a partial separation with a PC-1 score of 52%. This proof-of-concept study further confirms the potential of breath analysis for cancer detection but also underlines the importance of quality control over the full analytical procedure, including the processing of the data.