| Title: | Feasibility of a Portable Electronic Nose for Detection of Oral Squamous Cell Carcinoma in Sudan |
| Author(s): | Mohamed N; van de Goor R; El-Sheikh M; Elrayah O; Osman T; Nginamau ES; Johannessen AC; Suleiman A; Costea DE; Kross KW; |
| Address: | "Center for Cancer Biomarkers (CCBIO) and Gade Laboratory for Pathology, Department of Clinical Medicine, University of Bergen, P.O. Box 7800, 5020 Bergen, Norway. Center for International Health (CIH), University of Bergen, P.O. Box 7800, 5020 Bergen, Norway. Department of Oral and Maxillofacial Surgery and Department of Basic Sciences, University of Khartoum, P.O. Box 321, 11111 Khartoum, Sudan. Department of Otolaryngology-Head and Neck Surgery, Bernhoven Hospital, P.O. Box 707, 5400 AS Uden, The Netherlands. Department of Otolaryngology-Head and Neck Surgery, Maastricht University Medical Centre, P. Debyelaan 25, 6229 HX Maastricht, The Netherlands. Department of Pathology, Haukeland University Hospital, Jonas Lies vei 65, N-5020 Bergen, Norway. Policlinique Saint Odilon, 32 Rue Professeur Etienne Sorrel, 03000 Moulins, France" |
| DOI: | 10.3390/healthcare9050534 |
| ISSN/ISBN: | 2227-9032 (Print) 2227-9032 (Electronic) 2227-9032 (Linking) |
| Abstract: | "BACKGROUND: Oral squamous cell carcinoma (OSCC) is increasing at an alarming rate particularly in low-income countries. This urges for research into noninvasive, user-friendly diagnostic tools that can be used in limited-resource settings. This study aims to test and validate the feasibility of e-nose technology for detecting OSCC in the limited-resource settings of the Sudanese population. METHODS: Two e-nose devices (Aeonose, eNose Company, Zutphen, The Netherlands) were used to collect breath samples from OSCC (n = 49) and control (n = 35) patients. Patients were divided into a training group for building an artificial neural network (ANN) model and a blinded control group for model validation. The Statistical Package for the Social Sciences (SPSS) software was used for the analysis of baseline characteristics and regression. Aethena proprietary software was used for data analysis using artificial neural networks based on patterns of volatile organic compounds. RESULTS: A diagnostic accuracy of 81% was observed, with 88% sensitivity and 71% specificity. CONCLUSIONS: This study demonstrates that e-nose is an efficient tool for OSCC detection in limited-resource settings, where it offers a valuable cost-effective strategy to tackle the burden posed by OSCC" |
| Keywords: | cancer diagnosis electronic nose oral screening toombak; |
| Notes: | "PubMed-not-MEDLINEMohamed, Nazar van de Goor, Rens El-Sheikh, Mariam Elrayah, Osman Osman, Tarig Nginamau, Elisabeth Sivy Johannessen, Anne Christine Suleiman, Ahmed Costea, Daniela Elena Kross, Kenneth W eng 22325/Norges Forskningsrad/ 912260/2019/Helse Vest Regionalt Helseforetak/ Switzerland 2021/06/03 Healthcare (Basel). 2021 May 3; 9(5):534. doi: 10.3390/healthcare9050534" |
