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Paper on wound-swaps

Mar 27 2019

The majority of clinical studies has been based on exhaled-breath analysis, but for the current study, a dedicated eNose-device was used, ‘Aetholab’, that enables headspace analysis of 4 vials simultaneously. For this purpose, 4 electronic noses have been integrated. Data analysis methods in this case are similar to the ones used in exhaled-breath analysis.

Our first paper on headspace analysis for human diagnostics was recently published in the journal ‘Clinical Microbiology and Infection’:

‘Differentiation between infected and not infected wounds using an electronic nose’


Pilot Study: Detection of Prostate Cancer

Nov 26 2018

Abstract

Background

Prostate biopsy, an invasive examination, is the gold standard for diagnosing prostate cancer (PCa). There is a need for a novel noninvasive diagnostic tool that achieves a significantly high pretest probability for PCa, reducing unnecessary biopsy numbers. Recent studies have shown that volatile organic compounds (VOCs) in exhaled breath can be used to detect different types of cancers via training of an artificial neural network (ANN).


Objective

To determine whether exhaled-breath analysis using a handheld electronic nosedevice can be used to discriminate between VOC patterns between PCa patients and healthy individuals.


Design, setting, and participants

This prospective pilot study was conducted in the outpatient urology clinic of the Maastricht University Medical Center, the Netherlands. Patients with histologically proven PCa were already included before initial biopsy or during follow-up, with no prior treatment for their PCa. Urological patients with negative biopsies in the past year or patients with prostate enlargement (PE) with low or stable serum prostate-specific antigen were used as controls. Exhaled breath was probed from 85 patients: 32 with PCa and 53 controls (30 having negative biopsies and 23 PE).


Outcome measurements and statistical analysis

Patient characteristics were statistically analyzed using independent sample ttest and Pearson’s chi-square test. Data analysis was performed by Aethena software after data compression using the TUCKER3 algorithm. ANN models were trained and evaluated using the leave-10%-out cross-validation method.


Results and limitations

Our trained ANN showed an accuracy of 0.75, with an area under the curve of 0.79 with sensitivity and specificity of 0.84 (95% confidence interval [CI] 0.66–0.94) and 0.70 (95% CI 0.55–0.81) respectively, comparing PCa with control individuals. The negative predictive value was found to be 0.88. The main limitation is the relatively small sample size.


Conclusions

Our findings imply that the Aeonose allows us to discriminate between patients with untreated, histologically proven primary PCa and control patients based on exhaled-breath analysis.


Patient summary

We explored the possibility of exhaled-breath analysis using an electronic nose, to be used as a noninvasive tool in clinical practice, as a pretest for diagnosing prostate cancer. We found that the electronic nose was able to discriminate between prostate cancer patients and control individuals.


Keywords

Prostatic neoplasms; Electronic nose; Volatile organic compounds; Breath tests

Abstract presented at EBCC

Mar 8 2016

Breast cancer abstractBreast cancer diagnosis and subtyping by analysing exhaled breath: A pilot study using a portable electronic nose (Aeonose®)  Timmer-Bonte1, S. Grosfeld1, N. Seelen-Janssen1, L. Veenendaal2, F. Zijlstra3.


1Alexander Monro Breast Cancer Hospital, Medical Oncology, Bilthoven, Netherlands; 2Alexander Monro Breast Cancer Hospital, Surgery, Bilthoven, Netherlands; 3Alexander Monro Breast Cancer Hospital, Radiology, Bilthoven, Netherlands


Background: Analysing exhaled breath is a rapidly emerging field of medical diagnostics. Growing evidence suggests pathological conditions can cause metabolic changes in the body resulting in deviations in volatile organic compounds (VOC’s) in exhaled breath. The hand-held electronic nose used in this pilot study is convenient to use and has already proven to be capable of distinguishing patients (pts) suffering from lung cancer and head and neck cancer from healthy controls. Advantages include non-invasiveness and fast results. The objective of the current study is to investigate if exhaled-breath patterns from breast cancer (BC) pts can be distinguished from pts with benign breast pathology or healthy controls.


Methods: Female pts referred to our breast cancer hospital for screening or on suspicion of BC, were subjected to standard diagnostic procedures as per protocol (with a minimum of clinical examination, mammography and in case of BIRADS >2 biopsy). All pts were asked written informed consent for performing an additional breath test. Pts had to breathe into the Aeonose® device (made available for this pilot study by The eNose Company, Zutphen, The Netherlands) for a period of 5 minutes. Collected measurement data were compressed using a Tucker3 algorithm followed by neural network analysis. In this way, the electronic nose has been trained to distinguish between healthy and affected pts.


Results: 185 pts were eligible; the majority was referred because of a palpable lesion or abnormal findings on mammography in the Dutch national screening program. In 56 pts BC was confirmed (biopsy or surgery). In the remaining 129 pts no or benign (e.g. cysts or fibroadenoma) breast disease (no BC) was diagnosed. Based on the measurement data of the VOC’s in exhaled breath, a ROC (Receiver Operating Characteristic)- curve has been created showing only a fair sensitivity and specificity of 68% and 64%, respectively. However, BC is a heterogeneous disease. Therefore, explorative analyses were performed. Indeed, no BC (n = 119) compared to either Invasive Ductal Carcinoma (IDC) (n = 38) or to Invasive Lobular Carcinoma (ILC) (n = 11) showed an improved sensitivity and specificity (IDC 71% and 55%; ILC 100% and 81% respectively). All analyses were cross-validated using a leave-one-out method.


Conclusion: The high sensitivity and specificity values found for (the relatively homogeneous) ILC indicates this technology has potential for convenient, quick, and non-invasive screening of (a)symptomatic patients. Based on our experience we expect the ROC-curve to improve for IDC when increasing numbers in several pathological subtypes can be included. Based on these results we intend to perform a larger multi-centre trial as the Aeonose electronic nose could be a convenient and cost-effective screening test for both patient and staff.