Exhaled-breath Testing for Prostate Cancer Based on Volatile Organic Compound Profiling Using an Electronic Nose Device (Aeonose™): A Preliminary Report

An insect-based bioelectronic sensing system combining flexible dual-sided microelectrode array and insect olfactory circuitry for human lung cancer detection

Early detection of lung cancer significantly enhances treatment outcomes, yet current screening methods are limited by accessibility, sensitivity, and cost. This study introduces a bioelectronic sensing platform that integrates the highly sensitive locust olfactory system with a flexible dual-sided microelectrode array (MEA), for robust, noninvasive, and label-free detection of volatile lung cancer biomarkers. Using an innovative folding-annealing fabrication technique and PEDOT:PSS surface functionalization, we developed flexible, dual-sided MEAs with high electrode densities of 463, 687, and 766 channels/mm2 across prototypes, maintaining low impedance (within 4 × 104 Ω). These MEAs demonstrated mechanical flexibility and stability, enabling direct insertion into locust brain tissue without mechanical reinforcement and facilitating precise recording of neural activity in the antennal lobe triggered by lung cancer-related volatile organic compounds (VOCs) from low concentration (1 ppm). Advanced dimensionality reduction techniques applied to the electrophysiological recordings identified distinct neural response patterns to each VOC biomarker and the complex "scent" emitted from various cell lines. Using high-dimensional population neuronal response analysis with a leave-one-trial-out approach, the platform achieved a 100 % classification success rate for unknown VOCs. Additionally, varying concentrations (ppm-ppb) of individual VOC biomarkers were detected and classified with an accuracy of 86 %. The system was further tested for its ability to detect and classify human lung cancer cell lines based on the unique "scent" of cultured cells, including two non-small cell lung cancer (NSCLC) and two small cell lung cancer (SCLC) types. Quantitative assessments demonstrated that the platform achieved a classification accuracy of 85 % across these cell lines. These results substantiate the platform's potential for enhancing clinical diagnostics through the accurate identification of lung cancer stages and cell types. By integrating biological sensory systems with advanced bioelectronics, this study introduces a novel and efficient approach to lung cancer biomarker detection. It provides a non-invasive, brain-based cancer screening method, offering an accessible and innovative solution for early lung cancer diagnosis.

Feasibility of an Electronic Nose to Aid Biochemical Assessment of Adrenal Lesions

OBJECTIVES

Analysis of volatile organic compounds (VOCs) in exhaled breath has emerged as a promising noninvasive diagnostic tool for various diseases. The aim of this study was to evaluate the potential of an electronic nose to differentiate between functional adrenal lesions - pheochromocytoma (PHEO), primary hyperaldosteronism (PHA), and hypercortisolism (CS) - and nonfunctional adrenal lesions.

METHODS

A pilot study was conducted at 2 tertiary hospitals within the Netherlands. Patients with PHEO, PHA, CS, and nonfunctional adrenal lesions underwent breath testing with an electronic nose between May 2021 and June 2024. Each center employed a distinct electronic nose (device A and B). Comparability of data between the devices was assessed in a t-distributed stochastic neighbor embedding plot, and an artificial neural network was trained to classify breath patterns.

RESULTS

Data obtained from the 2 electronic noses were too heterogeneous for pooling and device B had an insufficient sample size for further analysis. Therefore, VOC patterns of 76 functional lesions (27 PHEO, 33 PHA, 16 CS) and 29 nonfunctional adrenal lesions measured exclusively with device A were analyzed. Moderate discriminative performance was observed in the training data: pooled functional lesions (area under the curve [AUC] 0.76), PHEO [AUC 0.76], PHA [AUC 0.72] and CS [AUC 0.58] versus nonfunctional lesions. However, model performance declined significantly when applying the model developed with training data on test data, with wide confidence intervals across all comparisons.

CONCLUSIONS

While slight differences in VOC patterns were detected between functional and nonfunctional adrenal lesions, the electronic nose demonstrated limited discriminative value for clinical practice.

Exhaled breath analysis with the use of an electronic nose to predict response to immune checkpoint inhibitors in patients with metastatic melanoma: melaNose trial

Introduction

Immune checkpoint inhibitors (ICIs) have significantly improved the overall survival for patients with different solid tumors. However, there is an urgent need for predictive biomarkers to identify patients with metastatic melanoma who do not benefit from treatment with ICIs, to prevent unnecessary immune related adverse events (irAEs). Electronic noses (eNoses) showed promising results in the detection of cancer as well as the prediction of response outcome in patients with cancer. In this feasibility study, we aimed to investigate whether the breath pattern measured using eNose can be used as a simple biomarker to predict clinical benefit to first-line treatment with ICIs in patients with metastatic melanoma.

Methods

In this prospective, observational single-center feasibility study, patients with metastatic melanoma performed a breath test using Aeonose™ before start of first-line treatment with ICIs. The detected exhaled breath pattern of volatile organic compounds (VOC) was used for machine learning in a training set to develop a model to identify patients who do not benefit from treatment with ICIs. Lack of clinical benefit was defined as progressive disease according to best tumor response using RECIST v1.1. Primary outcome measures were sensitivity, specificity and accuracy.

Results

The eNose showed a distinct breath pattern between patients with and without clinical benefit from ICIs. To identify patients who do not benefit from first-line ICIs treatment, breath pattern analysis using the eNose resulted in a sensitivity of 88%, specificity of 79%, and accuracy of 85%.

Conclusion

Exhaled breath analysis using eNose can identify patients with metastatic melanoma who will not benefit from first-line treatment with ICIs and guide treatment strategies. When validated in an external cohort, eNose could be a useful tool to select these patients for alternative treatment strategies in clinical practice.

Clinical applications of volatilomic assays

The study of metabolomics is revealing immense potential for diagnosis, therapy monitoring, and understanding of pathogenesis processes. Volatilomics is a subcategory of metabolomics interested in the detection of molecules that are small enough to be released in the gas phase. Volatile compounds produced by cellular processes are released into the blood and lymph, and can reach the external environment through different pathways, such as the blood-air interface in the lung that are detected in breath, or the blood-water interface in the kidney that leads to volatile compounds detected in urine. Besides breath and urine, additional sources of volatile compounds such as saliva, blood, feces, and skin are available. Volatilomics traces its roots back over fifty years to the pioneering investigations in the 1970s. Despite extensive research, the field remains in its infancy, hindered by a lack of standardization despite ample experimental evidence. The proliferation of analytical instrumentations, sample preparations and methods of volatilome sampling still make it difficult to compare results from different studies and to establish a common standard approach to volatilomics. This review aims to provide an overview of volatilomics' diagnostic potential, focusing on two key technical aspects: sampling and analysis. Sampling poses a challenge due to the susceptibility of human samples to contamination and confounding factors from various sources like the environment and lifestyle. The discussion then delves into targeted and untargeted approaches in volatilomics. Some case studies are presented to exemplify the results obtained so far. Finally, the review concludes with a discussion on the necessary steps to fully integrate volatilomics into clinical practice.

Diagnosis of Lung Cancer Through Exhaled Breath: A Comprehensive Study

OBJECTIVES

Exhaled breath analysis is an attractive lung cancer diagnostic tool. However, various factors that are not related to the disease status, comorbidities, and other diseases must be considered to obtain a reliable diagnostic model.

METHODS

Exhaled breath samples from 646 individuals including 273 patients with lung cancer (LC), 90 patients with cancer of other localizations (OC), 150 patients with noncancer lung diseases (NLD), and 133 healthy controls (HC) were analyzed using gas chromatography-mass spectrometry (GC-MS). The samples were collected in Tedlar bags. Volatile organic compounds (VOCs) were preconcentrated on Tenax TA sorbent tubes with subsequent two-stage thermal desorption followed by GC-MS analysis. The influence of age, gender, smoking status, time since last food consumption, and comorbidities on exhaled breath were evaluated. Also, the effect of histology, TNM, tumor localization, treatment status, and the presence of a tumor on VOC profile of patients with lung cancer were assessed. Intergroup statistics were estimated, diagnostic models were created using artificial neural networks (ANNs) and gradient boosted decision trees (GBDTs).

RESULTS

Smoking status and food consumption affect exhaled breath VOC profile: benzene, ethylbenzene, toluene, 1,3-pentadiene 1,4-pentadiene acetonitrile, and some ratios are significantly different in exhaled breath of smokers and nonsmokers; the ratios 2,3-butandione/2-pentanone, 2,3-butandione/dimethylsulfide, and 2-butanone/2-pentanone are affected by time since last food consumption. Exhaled breath of LC is affected by the form of the disease and comorbidities. One-pentanol and 2-butanone were different in exhaled breath of patients with various tumor localization; 2-butanone was different in exhaled breath of patients before and during treatment. Diabetes as a comorbidity affects the pentanal level in exhaled breath; obesity affects the ratios of 2,3-butanedione/dimethylsulfide and 2-butanone/isoprene. Sensitivity and specificity of diagnostic models aimed to discriminate LC and HC, OC, and NLD were 78.7% and 51.0%, 62.2% and 53.4%, and 60.4% and 58.0%, respectively. HC and patients, regardless of the disease, can be classified with sensitivity of 76.6% and specificity of 68.2%.

CONCLUSIONS

The models created to diagnose lung cancer can also classify OC and NLD as patients with lung cancer. Additionally, the influence of comorbidities and factors not related to the disease status must be considered before the creation of diagnostic models to avoid false results.

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.

Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

Advancements in breath-based diagnostics for pancreatic cancer: Current insights and future perspectives

Recent decades have seen a concerning surge in carcinogenic diseases, with cancer cases and deaths on the rise globally. Conventional diagnostic methods are often invasive and time-consuming, highlighting the need for fast, accurate, and painless alternatives. Non-invasive exhaled breath analysis emerges as a promising solution, with over 200 volatile organic compounds (VOCs) detected in exhaled air, showing potential as biomarkers for various diseases, including cancer. Despite the lack of standardized methodologies, advancements in analytical instruments have expanded detection capabilities, reaching 3500 VOCs. Studies have identified specific VOC patterns associated with different cancers, offering hope for non-invasive diagnosis. Techniques such as gas chromatography-mass spectrometry and electronic noses show promise in distinguishing between healthy individuals and cancer patients. However, further research and standardization are needed to realize the full clinical potential of breath-based diagnostics, particularly in the early detection of challenging cancers like pancreatic cancer.

Diagnosis of chondrosarcoma in a noninvasive way using volatile organic compounds in exhaled breath: a pilot study

Aim: Aim of this explorative pilot study was to evaluate the capability of an electronic nose (aeoNose, the eNose Company) to classify healthy individuals and patients with chondrosarcoma, based on their volatile organic compound profiles in exhaled breath.Materials & methods: Fifty-seven patients (25 healthy controls, 24 chondrosarcoma and 8 different benign lesions) were included in the study from 2018 to 2023. An artificial neural network was used as classifier.Results: The developed model had a sensitivity of 75%, and a specificity of 65% with an AUC of 0.66.Conclusion: Results show that there is not enough evidence to include the aeoNose as diagnostic biomarker for chondrosarcoma in daily practice. However, the aeoNose might play an additional role alongside MRI, in questionable chondrosarcoma cases.

VOC-based detection of prostate cancer using an electronic nose and ion mobility spectrometry: A novel urine-based approach

BACKGROUND

Many diseases leave behind specific metabolites which can be detected from breath and urine as volatile organic compounds (VOC). Our group previously described VOC-based methods for the detection of bladder cancer and urinary tract infections. This study investigated whether prostate cancer can be diagnosed from VOCs in urine headspace.

METHODS

For this pilot study, mid-stream urine samples were collected from 56 patients with histologically confirmed prostate cancer. A control group was formed with 53 healthy male volunteers matched for age who had recently undergone a negative screening by prostate-specific antigen (PSA) and digital rectal exam. Headspace measurements were performed with the electronic nose Cyranose 320TM . Statistical comparison was performed using principal component analysis, calculating Mahalanobis distance, and linear discriminant analysis. Further measurements were carried out with ion mobility spectrometry (IMS) to compare detection accuracy and to identify potential individual analytes. Bonferroni correction was applied for multiple testing.

RESULTS

The electronic nose yielded a sensitivity of 77% and specificity of 62%. Mahalanobis distance was 0.964, which is indicative of limited group separation. IMS identified a total of 38 individual analytical peaks, two of which showed significant differences between groups (p < 0.05). To discriminate between tumor and controls, a decision tree with nine steps was generated. This model led to a sensitivity of 98% and specificity of 100%.

CONCLUSIONS

VOC-based detection of prostate cancer seems feasible in principle. While the first results with an electronic nose show some limitations, the approach can compete with other urine-based marker systems. However, it seems less reliable than PSA testing. IMS is more accurate than the electronic nose with promising sensitivity and specificity, which warrants further research. The individual relevant metabolites identified by IMS should further be characterized using gas chromatography/mass spectrometry to facilitate potential targeted rapid testing.

Exhaled breath and urinary volatile organic compounds (VOCs) for cancer diagnoses, and microbial-related VOC metabolic pathway analysis: a systematic review and meta-analysis

BACKGROUND

The gradual evolution of the detection and quantification of volatile organic compounds (VOCs) has been instrumental in cancer diagnosis. The primary objective of this study was to assess the diagnostic potential of exhaled breath and urinary VOCs in cancer detection. As VOCs are indicative of tumor and human metabolism, our work also sought to investigate the metabolic pathways linked to the development of cancerous tumors.

MATERIALS AND METHODS

An electronic search was performed in the PubMed database. Original studies on VOCs within exhaled breath and urine for cancer detection with a control group were included. A meta-analysis was conducted using a bivariate model to assess the sensitivity and specificity of the VOCs for cancer detection. Fagan's nomogram was designed to leverage the findings from our diagnostic analysis for the purpose of estimating the likelihood of cancer in patients. Ultimately, MetOrigin was employed to conduct an analysis of the metabolic pathways associated with VOCs in relation to both human and/or microbiota.

RESULTS

The pooled sensitivity, specificity and the area under the curve for cancer screening utilizing exhaled breath and urinary VOCs were determined to be 0.89, 0.88, and 0.95, respectively. A pretest probability of 51% can be considered as the threshold for diagnosing cancers with VOCs. As the estimated pretest probability of cancer exceeds 51%, it becomes more appropriate to emphasize the 'ruling in' approach. Conversely, when the estimated pretest probability of cancer falls below 51%, it is more suitable to emphasize the 'ruling out' approach. A total of 14, 14, 6, and 7 microbiota-related VOCs were identified in relation to lung, colorectal, breast, and liver cancers, respectively. The enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in the aforementioned tumor types.

CONCLUSIONS

The analysis of exhaled breath and urinary VOCs showed promise for cancer screening. In addition, the enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in four tumor types, namely lung, colorectum, breast and liver. These findings hold significant implications for the prospective clinical application of multiomics correlation in disease management and the exploration of potential therapeutic targets.

Dogs can detect an odor profile associated with Staphylococcus aureus biofilms in cultures and biological samples

Introduction

The study investigated the utilization of odor detection dogs to identify the odor profile of Staphylococcus aureus (S. aureus) biofilms in pure in vitro samples and in in vivo biosamples from animals and humans with S. aureus periprosthetic joint infection (PJI). Biofilms form when bacterial communities aggregate on orthopedic implants leading to recalcitrant infections that are difficult to treat. Identifying PJI biofilm infections is challenging, and traditional microbiological cultures may yield negative results even in the presence of clinical signs.

Methods

Dogs were trained on pure in vitro S. aureus biofilms and tested on lacrimal fluid samples from an in vivo animal model (rabbits) and human patients with confirmed S. aureus PJI.

Results

The results demonstrated that dogs achieved a high degree of sensitivity and specificity in detecting the odor profile associated with S. aureus biofilms in rabbit samples. Preliminary results suggest that dogs can recognize S. aureus volatile organic compounds (VOCs) in human lacrimal fluid samples.

Discussion

Training odor detection dogs on in vitro S. aureus, may provide an alternative to obtaining clinical samples for training and mitigates biosecurity hazards. The findings hold promise for culture-independent diagnostics, enabling early disease detection, and improved antimicrobial stewardship. In conclusion, this research demonstrates that dogs trained on in vitro S. aureus samples can identify the consistent VOC profile of PJI S. aureus biofilm infections. The study opens avenues for further investigations into a retained VOC profile of S. aureus biofilm infection. These advancements could revolutionize infectious disease diagnosis and treatment, leading to better patient outcomes and addressing the global challenge of antimicrobial resistance.

Diagnosis of Carcinogenic Pathologies through Breath Biomarkers: Present and Future Trends

The assessment of volatile breath biomarkers has been targeted with a lot of interest by the scientific and medical communities during the past decades due to their suitability for an accurate, painless, non-invasive, and rapid diagnosis of health states and pathological conditions. This paper reviews the most relevant bibliographic sources aiming to gather the most pertinent volatile organic compounds (VOCs) already identified as putative cancer biomarkers. Here, a total of 265 VOCs and the respective bibliographic sources are addressed regarding their scientifically proven suitability to diagnose a total of six carcinogenic diseases, namely lung, breast, gastric, colorectal, prostate, and squamous cell (oesophageal and laryngeal) cancers. In addition, future trends in the identification of five other forms of cancer, such as bladder, liver, ovarian, pancreatic, and thyroid cancer, through perspective volatile breath biomarkers are equally presented and discussed. All the results already achieved in the detection, identification, and quantification of endogenous metabolites produced by all kinds of normal and abnormal processes in the human body denote a promising and auspicious future for this alternative diagnostic tool, whose future passes by the development and employment of newer and more accurate collection and analysis techniques, and the certification for utilisation in real clinical scenarios.

Breath biopsy, a novel technology to identify head and neck squamous cell carcinoma: A systematic review

Head and neck cancers are a heterogeneous group of neoplasms, which together comprise the sixth most common cancer globally. Breath biopsies are a non-invasive clinical investigation that detect volatile organic compounds (VOCs) in exhaled breath. This systematic review examines current applications of breath biopsy for the diagnosis of head and neck squamous cell carcinoma (HNSCC), including data on efficacy and utility, and speculates on the future uses of this non-invasive detection method. Medline, PubMed, Web of Science, Cochrane and Scopus, as well as the grey literature were searched using a search strategy developed to identify relevant studies on the role of breath biopsy in the diagnosis of HNSCC. All included studies were subject to a thorough methodological quality assessment. The initial search generated a total of 1443 articles, 20 of which were eligible for review. A total of 660 HNSCC samples were investigated across the included studies. 3,7-dimethylundecane and benzaldehyde were among several VOCs to be significantly correlated with the presence of HNSCC compared to healthy controls. We show that current breath biopsy methods have high accuracy, specificity and sensitivity for identifying HNSCC. However, further studies are needed given the reported poor quality of the included studies.

Alteration of the Exhaled Volatile Organic Compound Pattern in Colorectal Cancer Patients after Intentional Curative Surgery-A Prospective Pilot Study

As current follow-up modalities for colorectal carcinoma (CRC) have restricted sensitivity, novel diagnostic tools are needed. The presence of CRC changes the endogenous metabolism, resulting in the release of a specific volatile organic compounds (VOC) pattern that can be detected with an electronic nose or AeonoseTM. To evaluate the use of an electronic nose in the follow-up of CRC, we studied the effect of curative surgery on the VOC pattern recognition using AeonoseTM. A prospective cohort study was performed, in which 47 patients diagnosed with CRC were included, all of whom underwent curative surgical resection. Breath testing was performed before and after surgery using the AeonoseTM. A machine learning model was developed by discerning between the 94 pre-and postoperative breath samples. The training model differentiated between the pre-and postoperative CRC breath samples with a sensitivity and specificity of 0.78 (95%CI 0.61-0.90) and 0.73 (95%CI 0.56-0.86), respectively, with an accuracy of 0.76 (95%CI 0.66-0.85), and an area under the curve of 0.79 (95%CI 0.68-0.89). The internal validation of the test set resulted in an accuracy of 0.75 (95%CI 0.51-0.91) and AUC of 0.82 (95%CI 0.61-1). In conclusion, our results suggest that the VOC pattern of CRC patients is altered by curative surgery in a short period, indicating that the exhaled VOCs might be closely related to the presence of CRC. However, to use AeonoseTM as a potential diagnostic tool in the clinical follow-up of CRC patients, the performance of the models needs to be improved through further large-scale prospective research.

Selectivity of Exhaled Breath Biomarkers of Lung Cancer in Relation to Cancer of Other Localizations

Lung cancer is a leading cause of death worldwide, mostly due to diagnostics in the advanced stage. Therefore, the development of a quick, simple, and non-invasive diagnostic tool to identify cancer is essential. However, the creation of a reliable diagnostic tool is possible only in case of selectivity to other diseases, particularly, cancer of other localizations. This paper is devoted to the study of the variability of exhaled breath samples among patients with lung cancer and cancer of other localizations, such as esophageal, breast, colorectal, kidney, stomach, prostate, cervix, and skin. For this, gas chromatography-mass spectrometry (GC-MS) was used. Two classification models were built. The first model separated patients with lung cancer and cancer of other localizations. The second model classified patients with lung, esophageal, breast, colorectal, and kidney cancer. Mann-Whitney U tests and Kruskal-Wallis H tests were applied to identify differences in investigated groups. Discriminant analysis (DA), gradient-boosted decision trees (GBDT), and artificial neural networks (ANN) were applied to create the models. In the case of classifying lung cancer and cancer of other localizations, average sensitivity and specificity were 68% and 69%, respectively. However, the accuracy of classifying groups of patients with lung, esophageal, breast, colorectal, and kidney cancer was poor.

Early Detection of Prostate Cancer: The Role of Scent

Prostate cancer (PCa) represents the cause of the second highest number of cancer-related deaths worldwide, and its clinical presentation can range from slow-growing to rapidly spreading metastatic disease. As the characteristics of most cases of PCa remains incompletely understood, it is crucial to identify new biomarkers that can aid in early detection. Despite the prostate-specific antigen serum (PSA) levels, prostate biopsy, and imaging representing the actual gold-standard for diagnosing PCa, analyzing volatile organic compounds (VOCs) has emerged as a promising new frontier. We and other authors have reported that highly trained dogs can recognize specific VOCs associated with PCa with high accuracy. However, using dogs in clinical practice has several limitations. To exploit the potential of VOCs, an electronic nose (eNose) that mimics the dog olfactory system and can potentially be used in clinical practice was designed. To explore the eNose as an alternative to dogs in diagnosing PCa, we conducted a systematic literature review and meta-analysis of available studies. PRISMA guidelines were used for the identification, screening, eligibility, and selection process. We included six studies that employed trained dogs and found that the pooled diagnostic sensitivity was 0.87 (95% CI 0.86–0.89; I2, 98.6%), the diagnostic specificity was 0.83 (95% CI 0.80–0.85; I2, 98.1%), and the area under the summary receiver operating characteristic curve (sROC) was 0.64 (standard error, 0.25). We also analyzed five studies that used an eNose to diagnose PCa and found that the pooled diagnostic sensitivity was 0.84 (95% CI, 0.80–0.88; I2, 57.1%), the diagnostic specificity was 0.88 (95% CI, 0.84–0.91; I2, 66%), and the area under the sROC was 0.93 (standard error, 0.03). These pooled results suggest that while highly trained dogs have the potentiality to diagnose PCa, the ability is primarily related to olfactory physiology and training methodology. The adoption of advanced analytical techniques, such as eNose, poses a significant challenge in the field of clinical practice due to their growing effectiveness. Nevertheless, the presence of limitations and the requirement for meticulous study design continue to present challenges when employing eNoses for the diagnosis of PCa.

Breath Testing in the Surgical Setting: Applications, Challenges, and Future Perspectives

BACKGROUND

The potential for exhaled breath to be a valuable diagnostic tool is often overlooked as it can be difficult to imagine how a barely visible sample of breath could hold such a rich source of information about the state of our health. However, technological advances over the last 50 years have enabled us to detect volatile organic compounds (VOCs) present in exhaled breath, and this provides the key to understanding the wealth of information contained within these readily available samples.

SUMMARY

VOCs are produced as a by-product of metabolism; hence, changes in the underlying physiological processes will be reflected in the exact composition of VOCs in exhaled breath. It has been shown that characteristic changes occur in the breath VOC profile associated with certain diseases including cancer, which may enable the non-invasive detection of cancer at primary care level for patients with vague symptoms. The use of breath testing as a diagnostic tool has many advantages. It is non-invasive and quick, and the test is widely accepted by patients and clinicians. However, breath samples provide a snapshot of the VOCs present in a particular patient at a given point in time, so this can be heavily influenced by external factors such as diet, smoking, and the environment. These must all be accounted for when attempting to draw conclusions about disease status. This review focuses on the current applications for breath testing in the field of surgery, as well as discussing the challenges encountered with developing a breath test in a clinical environment. The future of breath testing in the surgical setting is also discussed, including the translation of breath research into clinical practice.

KEY MESSAGES

Analysis of VOCs in exhaled breath can identify the presence of underlying disease including cancer as well as other infectious or inflammatory conditions. Despite the patient factors, environmental factors, storage, and transport considerations that must be accounted for, breath testing demonstrates ideal characteristics for a triage test, being non-invasive, simple, and universally acceptable to patients and clinicians. Many novel biomarkers and diagnostic tests fail to translate into clinical practice because their potential clinical application does not align with the requirements and unmet needs of the healthcare sector. Non-invasive breath testing, however, has the great potential to revolutionise the early detection of diseases, such as cancer, in the surgical setting for patients with vague symptoms.

Noninvasive detection of soft tissue sarcoma using volatile organic compounds in exhaled breath: a pilot study

Aim: The aim of this pilot study was to assess whether an electronic nose can detect patients with soft tissue sarcoma (STS) based on volatile organic compound profiles in exhaled breath. Patients & methods: In this cross-sectional pilot study, patients with primary STS and healthy controls, matched on sex and age, were included for breath analysis. Machine learning techniques were used to develop the best-fitting model. Results: Fifty-nine breath samples were collected (29 STS and 30 control) from March 2018 to March 2022. The final model yielded a c-statistic of 0.85 with a sensitivity of 83% and specificity of 60%. Conclusion: This study suggests that exhaled volatile organic compound analysis could serve as a noninvasive diagnostic biomarker for the detection of STS with a good performance.

SnO2-Based Ultra-Flexible Humidity/Respiratory Sensor for Analysis of Human Breath

Developing ultraflexible sensors using metal oxides is challenging due to the high-temperature annealing step in the fabrication process. Here, we demonstrate the ultraflexible relative humidity (RH) sensor on food plastic wrap by using 808 nm near-infrared (NIR) laser annealing for 1 min at a low temperature (26.2-40.8 °C). The wettability of plastic wraps coated with sol-gel solution is modulated to obtain uniform films. The surface morphology, local temperature, and electrical properties of the SnO₂ resistor under NIR laser irradiation with a power of 16, 33, and 84 W/cm² are investigated. The optimal device can detect wide-range RH from 15% to 70% with small incremental changes (0.1-2.2%). X-ray photoelectron spectroscopy reveals the relation between the surface binding condition and sensing response. Finally, the proposed sensor is attached onto the face mask to analyze the real-time human breath pattern in slow, normal, and fast modes, showing potential in wearable electronics or respiration monitoring.

Harnessing insect olfactory neural circuits for detecting and discriminating human cancers

There is overwhelming evidence that presence of cancer alters cellular metabolic processes, and these changes are manifested in emitted volatile organic compound (VOC) compositions of cancer cells. Here, we take a novel forward engineering approach by developing an insect olfactory neural circuit-based VOC sensor for cancer detection. We obtained oral cancer cell culture VOC-evoked extracellular neural responses from in vivo insect (locust) antennal lobe neurons. We employed biological neural computations of the antennal lobe circuitry for generating spatiotemporal neuronal response templates corresponding to each cell culture VOC mixture, and employed these neuronal templates to distinguish oral cancer cell lines (SAS, Ca9-22, and HSC-3) vs. a non-cancer cell line (HaCaT). Our results demonstrate that three different human oral cancers can be robustly distinguished from each other and from a non-cancer oral cell line. By using high-dimensional population neuronal response analysis and leave-one-trial-out methodology, our approach yielded high classification success for each cell line tested. Our analyses achieved 76-100% success in identifying cell lines by using the population neural response (n = 194) collected for the entire duration of the cell culture study. We also demonstrate this cancer detection technique can distinguish between different types of oral cancers and non-cancer at different time-matched points of growth. This brain-based cancer detection approach is fast as it can differentiate between VOC mixtures within 250 ms of stimulus onset. Our brain-based cancer detection system comprises a novel VOC sensing methodology that incorporates entire biological chemosensory arrays, biological signal transduction, and neuronal computations in a form of a forward-engineered technology for cancer VOC detection.

Development of an Algorithm for Cervical High-Grade Squamous Intraepithelial Lesion Based on Breath Print Analysis

OBJECTIVES

This study was designed to develop an algorithm for the diagnosis of cervical high-grade squamous intraepithelial lesions (HSIL), based on patterns of volatile organic compounds, evaluated using an e-nose.

METHODS

For this pilot study, the study population consisted of a group of 25 patients with histologically confirmed HSIL and a group of 26 controls. Controls consisted of women visiting the outpatient department for gynecological complaints unrelated to cancer. Women had a negative high-risk human papillomavirus and/or normal cytology (negative for intraepithelial lesions of malignancy) of their most recent test performed in the context of participation in routine cervical cancer screening. Breath tests were performed and labeled with the correct diagnosis. Machine-learning techniques were used to develop a model for predicting HSIL. Based on the receiver operating characteristics curve, both sensitivity and specificity were calculated.

RESULTS

Individual classifications of all patients with HSIL and controls, as calculated by the model, showed a sensitivity of 0.88 (95% CI = 0.68-0.97) and specificity of 0.92 (95% CI = 0.73-0.99). The positive predictive value and the negative predictive value were 0.92 (95% CI = 0.72-0.99) and 0.89 (95% CI = 0.70-0.97), respectively. The Cohen κ coefficient was 0.80.

CONCLUSIONS

E-nose can detect distinctive patterns of volatile organic compounds between cervical HSIL patients and controls. Validation of the algorithm in further studies is necessary before possible implementation into daily practice.

Volatilomics: An Emerging and Promising Avenue for the Detection of Potential Prostate Cancer Biomarkers

Simple Summary

The lack of highly specific and sensitive biomarkers for the early detection of prostate cancer (PCa) is a major barrier to its management. Volatilomics emerged as a non-invasive, simple, inexpensive, and easy-to-use approach for cancer screening, characterization of disease progression, and follow-up of the treatment’s success. We provide a brief overview of the potential of volatile organic metabolites (VOMs) for the establishment of PCa biomarkers from non-invasive matrices. Endogenous VOMs have been investigated as potential biomarkers since changes in these VOMs can be characteristic of specific disease processes. Recent studies have shown that the conjugation of the prostate-specific antigen (PSA) screening with other methodologies, such as risk calculators, biomarkers, and imaging tests, can attenuate overdiagnosis and under-detection issues. This means that the combination of volatilomics with other methodologies could be extremely valuable for the differentiation of clinical phenotypes in a group of patients, providing more personalized treatments.

Abstract

Despite the spectacular advances in molecular medicine, including genomics, proteomics, transcriptomics, lipidomics, and personalized medicine, supported by the discovery of the human genome, prostate cancer (PCa) remains the most frequent malignant tumor and a leading cause of oncological death in men. New methods for prognostic, diagnostic, and therapy evaluation are mainly based on the combination of imaging techniques with other methodologies, such as gene or protein profiling, aimed at improving PCa management and surveillance. However, the lack of highly specific and sensitive biomarkers for its early detection is a major hurdle to this goal. Apart from classical biomarkers, the study of endogenous volatile organic metabolites (VOMs) biosynthesized by different metabolic pathways and found in several biofluids is emerging as an innovative, efficient, accessible, and non-invasive approach to establish the volatilomic biosignature of PCa patients, unravelling potential biomarkers. This review provides a brief overview of the challenges of PCa screening methods and emergent biomarkers. We also focus on the potential of volatilomics for the establishment of PCa biomarkers from non-invasive matrices.

Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath: A Systematic Review and Meta-analysis

IMPORTANCE

There has been a growing interest in the use of electronic noses (e-noses) in detecting volatile organic compounds in exhaled breath for the diagnosis of cancer. However, no systematic evaluation has been performed of the overall diagnostic accuracy and methodologic challenges of using e-noses for cancer detection in exhaled breath.

OBJECTIVE

To provide an overview of the diagnostic accuracy and methodologic challenges of using e-noses for the detection of cancer.

DATA SOURCES

An electronic search was performed in the PubMed and Embase databases (January 1, 2000, to July 1, 2021).

STUDY SELECTION

Inclusion criteria were the following: (1) use of e-nose technology, (2) detection of cancer, and (3) analysis of exhaled breath. Exclusion criteria were (1) studies published before 2000; (2) studies not performed in humans; (3) studies not performed in adults; (4) studies that only analyzed biofluids; and (5) studies that exclusively used gas chromatography-mass spectrometry to analyze exhaled breath samples.

DATA EXTRACTION AND SYNTHESIS

PRISMA guidelines were used for the identification, screening, eligibility, and selection process. Quality assessment was performed using Quality Assessment of Diagnostic Accuracy Studies 2. Generalized mixed-effects bivariate meta-analysis was performed.

MAIN OUTCOMES AND MEASURES

Main outcomes were sensitivity, specificity, and mean area under the receiver operating characteristic curve.

RESULTS

This review identified 52 articles with a total of 3677 patients with cancer. All studies were feasibility studies. The sensitivity of e-noses ranged from 48.3% to 95.8% and the specificity from 10.0% to 100.0%. Pooled analysis resulted in a mean (SE) area under the receiver operating characteristic curve of 94% (95% CI, 92%-96%), a sensitivity of 90% (95% CI, 88%-92%), and a specificity of 87% (95% CI, 81%-92%). Considerable heterogeneity existed among the studies because of differences in the selection of patients, endogenous and exogenous factors, and collection of exhaled breath.

CONCLUSIONS AND RELEVANCE

Results of this review indicate that e-noses have a high diagnostic accuracy for the detection of cancer in exhaled breath. However, most studies were feasibility studies with small sample sizes, a lack of standardization, and a high risk of bias. The lack of standardization and reproducibility of e-nose research should be addressed in future research.

Applying the electronic nose for pre-operative SARS-CoV-2 screening

BACKGROUND

Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis.

METHODS

Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between - 1 and + 1, indicating the infection probability.

RESULTS

219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96.

CONCLUSIONS

The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

Non-Invasive Detection and Staging of Colorectal Cancer Using a Portable Electronic Nose

Electronic noses (e-nose) offer potential for the detection of cancer in its early stages. The ability to analyse samples in real time, at a low cost, applying easy-to-use and portable equipment, gives e-noses advantages over other technologies, such as Gas Chromatography-Mass Spectrometry (GC-MS). For diseases such as cancer with a high mortality, a technology that can provide fast results for use in routine clinical applications is important. Colorectal cancer (CRC) is among the highest occurring cancers and has high mortality rates, if diagnosed late. In our study, we investigated the use of portable electronic nose (PEN3), with further analysis using GC-TOF-MS, for the analysis of gases and volatile organic compounds (VOCs) to profile the urinary metabolome of colorectal cancer. We also compared the different cancer stages with non-cancers using the PEN3 and GC-TOF-MS. Results obtained from PEN3, and GC-TOF-MS demonstrated high accuracy for the separation of CRC and non-cancer. PEN3 separated CRC from non-cancerous group with 0.81 AUC (Area Under the Curve). We used data from GC-TOF-MS to obtain a VOC profile for CRC, which identified 23 potential biomarker VOCs for CRC. Thus, the PEN3 and GC-TOF-MS were found to successfully separate the cancer group from the non-cancer group.

Exhaled-Breath Testing Using an Electronic Nose during Spinal Cord Stimulation in Patients with Failed Back Surgery Syndrome: An Experimental Pilot Study

The increased awareness of discrepancies between self-reporting outcome measurements and objective outcome measurements within the field of neuromodulation has accelerated the search towards more objective measurements. The aim of this study was to evaluate whether an electronic nose can differentiate between chronic pain patients in whom Spinal Cord Stimulation (SCS) was activated versus deactivated. Twenty-seven patients with Failed Back Surgery Syndrome (FBSS) participated in this prospective pilot study. Volatile organic compounds in exhaled breath were measured with electronic nose technology (Aeonose™) during SCS on and off states. Random forest was used with a leave-10%-out cross-validation method to determine accuracy of discriminating between SCS on and off states. Our random forest showed an accuracy of 0.56, with an area under the curve of 0.62, a sensitivity of 62% (95% CI: 41–79%) and a specificity of 50% (95% CI: 30–70%). Pain intensity scores were significantly different between both SCS states. Our findings indicate that we cannot discriminate between SCS off and on states based on exhaled breath with the Aeonose™ in patients with FBSS. In clinical practice, these findings imply that with a noninvasive electronic nose, exhaled breath cannot be used as an additional marker of the effect of neuromodulation.

Socio-economic demands and challenges for non-invasive disease diagnosis through a portable breathalyzer by the incorporation of 2D nanosheets and SMO nanocomposites

Breath analysis for non-invasive clinical diagnostics and treatment progression has penetrated the research community owing to the technological developments in novel sensing nanomaterials. The trace level selective detection of volatile organic compounds (VOCs) in breath facilitates the study of physiological disorder and real-time health monitoring. This review focuses on advancements in chemiresistive gas sensor technology for biomarker detection associated with different diseases. Emphasis is placed on selective biomarker detection by semiconducting metal oxide (SMO) nanostructures, 2-dimensional nanomaterials (2DMs) and nanocomposites through various optimization strategies and sensing mechanisms. Their synergistic properties for incorporation in a portable breathalyzer have been elucidated. Furthermore, the socio-economic demands of a breathalyzer in terms of recent establishment of startups globally and challenges of a breathalyzer are critically reviewed. This initiative is aimed at highlighting the challenges and scope for improvement to realize a high performance chemiresistive gas sensor for non-invasive disease diagnosis.

Breath analysis for non-invasive clinical diagnostics and treatment progression has penetrated the research community owing to the technological developments in novel sensing nanomaterials.

Breath analysis for the detection of digestive tract malignancies: systematic review

Background

In recent decades there has been growing interest in the use of volatile organic compounds (VOCs) in exhaled breath as biomarkers for the diagnosis of multiple variants of cancer. This review aimed to evaluate the diagnostic accuracy and current status of VOC analysis in exhaled breath for the detection of cancer in the digestive tract.

Methods

PubMed and the Cochrane Library database were searched for VOC analysis studies, in which exhaled air was used to detect gastro-oesophageal, liver, pancreatic, and intestinal cancer in humans, Quality assessment was performed using the QUADAS-2 criteria. Data on diagnostic performance, VOCs with discriminative power, and methodological information were extracted from the included articles.

Results

Twenty-three articles were included (gastro-oesophageal cancer n = 14, liver cancer n = 1, pancreatic cancer n = 2, colorectal cancer n = 6). Methodological issues included different modalities of patient preparation and sampling and platform used. The sensitivity and specificity of VOC analysis ranged from 66.7 to 100 per cent and from 48.1 to 97.9 per cent respectively. Owing to heterogeneity of the studies, no pooling of the results could be performed. Of the VOCs found, 32 were identified in more than one study. Nineteen were reported as cancer type-specific, whereas 13 were found in different cancer types. Overall, decanal, nonanal, and acetone were the most frequently identified.

Conclusion

The literature on VOC analysis has documented a lack of standardization in study designs. Heterogeneity between the studies and insufficient validation of the results make interpretation of the outcomes challenging. To reach clinical applicability, future studies on breath analysis should provide an accurate description of the methodology and validate their findings.

Electronic Nose as a Novel Method for Diagnosing Cancer: A Systematic Review

Cancer is fast becoming the most important cause of death worldwide, its mortality being mostly caused by late or wrong diagnosis. Novel strategies have been developed to identify early signs of cancer in a minimally obtrusive way, including the Electronic Nose (E-Nose) technology, user-friendly, cost- and time-saving alternative to classical approaches. This systematic review, conducted under the PRISMA guidelines, identified 60 articles directly dealing with the E-Nose application in cancer research published up to 31 January 2020. Among these works, the vast majority reported successful E-Nose use for diagnosing Lung Cancer, showing promising results especially when employing the Aeonose tool, discriminating subjects with Lung Cancer from controls in more than 80% of individuals, in most studies. In order to tailor the main limitations of the proposed approach, including the application of the protocol to advanced stage of cancer, sample heterogeneity and massive confounders, future studies should be conducted on early stage patients, and on larger cohorts, as to better characterize the specific breathprint associated with the various subtypes of cancer. This would ultimately lead to a better and faster diagnosis and to earlier treatment, possibly reducing the burden associated to such conditions.

Gas Sensors Based on Chemi-Resistive Hybrid Functional Nanomaterials

Chemi-resistive sensors based on hybrid functional materials are promising candidates for gas sensing with high responsivity, good selectivity, fast response/recovery, great stability/repeatability, room-working temperature, low cost, and easy-to-fabricate, for versatile applications. This progress report reviews the advantages and advances of these sensing structures compared with the single constituent, according to five main sensing forms: manipulating/constructing heterojunctions, catalytic reaction, charge transfer, charge carrier transport, molecular binding/sieving, and their combinations. Promises and challenges of the advances of each form are presented and discussed. Critical thinking and ideas regarding the orientation of the development of hybrid material-based gas sensor in the future are discussed.

Volatile organic compounds in breath can serve as a non-invasive diagnostic biomarker for the detection of advanced adenomas and colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the third most common cancer diagnosis in the Western world.

AIM

To evaluate exhaled volatile organic compounds (VOCs) as a non-invasive biomarker for the detection of CRC and precursor lesions using an electronic nose.

METHODS

In this multicentre study adult colonoscopy patients, without inflammatory bowel disease or (previous) malignancy, were invited for breath analysis. Two-thirds of the breath tests were randomly assigned to develop training models which were used to predict the diagnosis of the remaining patients (external validation). In the end, all data were used to develop final-disease models to further improve the discriminatory power of the algorithms.

RESULTS

Five hundred and eleven breath samples were collected. Sixty-four patients were excluded due to an inadequate breath test (n = 51), incomplete colonoscopy (n = 8) or colitis (n = 5). Classification was based on the most advanced lesion found; CRC (n = 70), advanced adenomas (AAs) (n = 117), non-advanced adenoma (n = 117), hyperplastic polyp (n = 15), normal colonoscopy (n = 125). Training models for CRC and AAs had an area under the curve (AUC) of 0.76 and 0.71 and blind validation resulted in an AUC of 0.74 and 0.61 respectively. Final models for CRC and AAs yielded an AUC of 0.84 (sensitivity 95% and specificity 64%) and 0.73 (sensitivity and specificity 79% and 59%) respectively.

CONCLUSIONS

This study suggests that exhaled VOCs could potentially serve as a non-invasive biomarker for the detection of CRC and AAs. Future studies including more patients could further improve the discriminatory potential of VOC analysis for the detection of (pre-)malignant colorectal lesions. (https://clinicaltrials.gov Identifier NCT03488537).

Exhaled breath analysis in the diagnosis of head and neck cancer

Head and neck cancer (HNC) comprises a heterogeneous group of upper aerodigestive tract malignant neoplasms, the most frequent of which is squamous cell carcinoma. HNC forms the eighth most common cancer type and the incidence is increasing. However, survival has improved only moderately during the past decades. Currently, early diagnosis remains the mainstay for improving treatment outcomes in this patient population. Unfortunately, screening methods to allow early detection of HNC are not yet established. Therefore, many cases are still diagnosed at advanced stage, compromising outcomes. Exhaled breath analysis (EBA) is a diagnostic tool that has been recently introduced for many cancers. Breath analysis is non-invasive, cost-effective, time-saving, and can potentially be applied for cancer screening. Here, we provide a summary of the accumulated evidence on the feasibility of EBA in the diagnosis of HNC.

Diagnosing complex regional pain syndrome using an electronic nose, a pilot study

OBJECTIVE

Complex regional pain syndrome (CRPS) is a complication after surgery or trauma and is characterized by a continuing regional pain in a distal extremity. The pain is disproportionate in severity and duration in relation to the preceding trauma. Currently, the diagnosis is based on the patients' signs and symptoms. There is no objective clinically applicable test available to confirm the diagnosis of CRPS, however this could contribute to a more reliable and valid diagnosis. Since the treatment of CRPS differs from that of other types of pain this could thereby lead to earlier and (more) appropriate treatment and possibly to lower medical costs. The Aeonose™ is a diagnostic test device which detects volatile organic profiles in exhaled air. Exhaled breath analysis using an electronic nose has been successfully applied to differentiate between sick and healthy persons for various indications. This study was a feasibility study in which we investigated whether the Aeonose™ is able to measure a difference in the volatome of CRPS patients compared to the volatome of healthy controls.

DESIGN

Prospective observational study.

SETTING

University Center for Pain Medicine.

SUBJECTS

Adult patients diagnosed with CRPS according to the latest IASP criteria (n = 36) and matched healthy controls (n = 36).

METHODS

Breath profiles were sampled by breathing in and out through the Aeonose™. Data were compressed using a Tucker3-like solution and subsequently used for training an artificial neural network together with the classification 'CRPS: Yes' or 'CRPS: No'. Cross-validation was applied using the leave-10%-out method.

RESULTS

Data of the 72 participants were analyzed, resulting in a sensitivity of 83% (95% CI 67%-93%), specificity of 78% (95% CI 60%-89%), and an overall accuracy of 81%.

CONCLUSIONS

This study suggests that the Aeonose™ can possibly distinguish patients with CRPS from healthy controls based on analysis of their volatome (MEC-2014-149).