| Title: | Exhaled-breath Testing for Prostate Cancer Based on Volatile Organic Compound Profiling Using an Electronic Nose Device (Aeonose): A Preliminary Report |
| Author(s): | Waltman CG; Marcelissen TAT; van Roermund JGH; |
| Address: | "Department of Urology, Maastricht University Medical Centre, Maastricht, The Netherlands. Department of Urology, Maastricht University Medical Centre, Maastricht, The Netherlands. Electronic address: joep.van.roermund@mumc.nl" |
| DOI: | 10.1016/j.euf.2018.11.006 |
| ISSN/ISBN: | 2405-4569 (Electronic) 2405-4569 (Linking) |
| 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 nose device 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 t test 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: | Aged Breath Tests/*instrumentation/methods *Electronic Nose Humans Male Middle Aged Pilot Projects Prospective Studies Prostatic Neoplasms/*diagnosis Volatile Organic Compounds/*analysis Breath tests Electronic nose Prostatic neoplasms Volatile organic co; |
| Notes: | "MedlineWaltman, Claire G Marcelissen, Tom A T van Roermund, Joep G H eng Netherlands 2018/11/30 Eur Urol Focus. 2020 Nov 15; 6(6):1220-1225. doi: 10.1016/j.euf.2018.11.006. Epub 2018 Nov 24" |
