Influence of age and gender on the profile of exhaled volatile organic compounds analyzed by an electronic nose

Dragonieri, Silvano; Quaranta, Vitaliano Nicola; Carratù, Pierluigi; Ranieri, Teresa; Resta, Onofrio

doi:10.1590/s1806-37562015000000195

Cited by 28 publications

(17 citation statements)

References 17 publications

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“…The high sensitivity of the e-nose can be explained by the broad spectrum of VOC reactions on multiple sensors at different temperatures and by the use of an ANN, which recognizes patterns more sensitively with every inclusion through a self-learning system. A study by Dragonieri et al 26 suggested that the overall VOC profile does not differ by age group or gender.…”

Section: Discussionmentioning

confidence: 98%

Pilot Study: Detection of Gastric Cancer From Exhaled Air Analyzed With an Electronic Nose in Chinese Patients

Schuermans

Jongen³

et al. 2018

Surg Innov

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The aim of this pilot study is to investigate the ability of an electronic nose (e-nose) to distinguish malignant gastric histology from healthy controls in exhaled breath. In a period of 3 weeks, all preoperative gastric carcinoma (GC) patients (n = 16) in the Beijing Oncology Hospital were asked to participate in the study. The control group (n = 28) consisted of family members screened by endoscopy and healthy volunteers. The e-nose consists of 3 sensors with which volatile organic compounds in the exhaled air react. Real-time analysis takes place within the e-nose, and binary data are exported and interpreted by an artificial neuronal network. This is a self-learning computational system. The inclusion rate of the study was 100%. Baseline characteristics differed significantly only for age: the average age of the patient group was 57 years and that of the healthy control group 37 years (P value = .000). Weight loss was the only significant different symptom (P value = .040). A total of 16 patients and 28 controls were included; 13 proved to be true positive and 20 proved to be true negative. The receiver operating characteristic curve showed a sensitivity of 81% and a specificity of 71%, with an accuracy of 75%. These results give a positive predictive value of 62% and a negative predictive value of 87%. This pilot study shows that the e-nose has the capability of diagnosing GC based on exhaled air, with promising predictive values for a screening purpose.

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Section: Discussionmentioning

confidence: 98%

Pilot Study: Detection of Gastric Cancer From Exhaled Air Analyzed With an Electronic Nose in Chinese Patients

Schuermans

Jongen³

et al. 2018

Surg Innov

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“…Hence, matching of dietary and lifestyle factors by using the spouse as a non-cancer control was difficult to achieve. Furthermore, there is insufficient evidence to suggest that the patient characteristics of age and gender could have affected the breath VOC profiles in this study [29,34]. Future clinical studies with a larger number of participants and incorporating regression models will be needed to accurately investigate the impact of patient characteristics, smoking, stage, tumor sub-site, and HPV on breath VOC profiles in HNSCC.…”

Section: Discussionmentioning

confidence: 99%

The Use of Selected Ion Flow Tube-Mass Spectrometry Technology to Identify Breath Volatile Organic Compounds for the Detection of Head and Neck Squamous Cell Carcinoma: A Pilot Study

et al. 2019

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Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common form of cancer worldwide, with approximately 630,000 new cases diagnosed each year. The development of low-cost and non-invasive tools for the detection of HNSCC using volatile organic compounds (VOCs) in the breath could potentially improve patient care. The aim of this study was to investigate the feasibility of selected ion flow tube mass spectrometry (SIFT-MS) technology to identify breath VOCs for the detection of HNSCC. Materials and Methods: Breath samples were obtained from HNSCC patients (N = 23) and healthy volunteers (N = 21). Exhaled alveolar breath samples were collected into FlexFoil® PLUS (SKC Limited, Dorset, UK) sampling bags from newly diagnosed, histologically confirmed, untreated patients with HNSCC and from non-cancer participants. Breath samples were analyzed by Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) (Syft Technologies, Christchurch, New Zealand) using Selective Ion Mode (SIM) scans that probed for 91 specific VOCs that had been previously reported as breath biomarkers of HNSCC and other malignancies. Results: Of the 91 compounds analyzed, the median concentration of hydrogen cyanide (HCN) was significantly higher in the HNSCC group (2.5 ppb, 1.6–4.4) compared to the non-cancer group (1.1 ppb, 0.9–1.3; Benjamini–Hochberg adjusted p < 0.05). A receiver operating curve (ROC) analysis showed an area under the curve (AUC) of 0.801 (95% CI, 0.65952–0.94296), suggesting moderate accuracy of HCN in distinguishing HNSCC from non-cancer individuals. There were no statistically significant differences in the concentrations of the other compounds of interest that were analyzed. Conclusions: This pilot study demonstrated the feasibility of SIFT-MS technology to identify VOCs for the detection of HNSCC.

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“…We used the following equation to calculate the required sample size [ 26 ]:

where SE is the standard error, C is the percentage of patients classified correctly, and n is the estimated sample size. An SE of 3 was used to limit the standard error to no more than 3%, and the acceptable accuracy ( C ) was 80 based on our aim.…”

Section: Methodsmentioning

confidence: 99%

A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer

Huang

Zeng

Wang

et al. 2018

Sensors

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Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79–1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80–0.99) by the supportive vector machine technique. The combination of the sensor array technique and machine learning can detect lung cancer with high accuracy.

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Influence of age and gender on the profile of exhaled volatile organic compounds analyzed by an electronic nose

Cited by 28 publications

References 17 publications

Pilot Study: Detection of Gastric Cancer From Exhaled Air Analyzed With an Electronic Nose in Chinese Patients

Pilot Study: Detection of Gastric Cancer From Exhaled Air Analyzed With an Electronic Nose in Chinese Patients

The Use of Selected Ion Flow Tube-Mass Spectrometry Technology to Identify Breath Volatile Organic Compounds for the Detection of Head and Neck Squamous Cell Carcinoma: A Pilot Study

A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer

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