Using machine learning and an electronic tongue for discriminating saliva samples from oral cavity cancer patients and healthy individuals.

Autor: Braz DC; Mato Grosso do Sul State University (UEMS), 79804-970, Dourados, MS, Brazil; São Carlos Institute of Physics (IFSC), University of São Paulo (USP), 13566-590, São Carlos, SP, Brazil., Neto MP; Federal Institute of São Paulo (IFSP), 14804-296, Araraquara, SP, Brazil; Institute of Mathematics and Computer Sciences (ICMC), University of São Paulo (USP), 13566-590, São Carlos, SP, Brazil., Shimizu FM; São Carlos Institute of Physics (IFSC), University of São Paulo (USP), 13566-590, São Carlos, SP, Brazil; Department of Applied Physics, 'Gleb Wataghin' Institute of Physics (IFGW), University of Campinas (UNICAMP), 13083-859, Campinas, SP, Brazil; Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, 13083-970, Campinas, SP, Brazil., Sá AC; São Carlos Institute of Physics (IFSC), University of São Paulo (USP), 13566-590, São Carlos, SP, Brazil., Lima RS; Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, 13083-970, Campinas, SP, Brazil; Institute of Chemistry, University of Campinas, 13083-970, Campinas, São Paulo, Brazil; Federal University of ABC, 09210-580, Santo André, SP, Brazil; São Carlos Institute of Chemistry, University of São Paulo, 09210-580, São Carlos, SP, Brazil., Gobbi AL; Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, 13083-970, Campinas, SP, Brazil., Melendez ME; Molecular Oncology Research Center, Barretos Cancer Hospital, 14784-400, Barretos, SP, Brazil; Brazilian National Cancer Institute, 20231-091, Rio de Janeiro, RJ, Brazil., Arantes LMRB; Molecular Oncology Research Center, Barretos Cancer Hospital, 14784-400, Barretos, SP, Brazil., Carvalho AL; Molecular Oncology Research Center, Barretos Cancer Hospital, 14784-400, Barretos, SP, Brazil., Paulovich FV; Faculty of Computer Science, Dalhousie University, Halifax, Canada., Oliveira ON Jr; São Carlos Institute of Physics (IFSC), University of São Paulo (USP), 13566-590, São Carlos, SP, Brazil. Electronic address: chu@ifsc.usp.br.
Jazyk: angličtina
Zdroj: Talanta [Talanta] 2022 Jun 01; Vol. 243, pp. 123327. Date of Electronic Publication: 2022 Feb 22.
DOI: 10.1016/j.talanta.2022.123327
Abstrakt: The diagnosis of cancer and other diseases using data from non-specific sensors - such as the electronic tongues (e-tongues) - is challenging owing to the lack of selectivity, in addition to the variability of biological samples. In this study, we demonstrate that impedance data obtained with an e-tongue in saliva samples can be used to diagnose cancer in the mouth. Data taken with a single-response microfluidic e-tongue applied to the saliva of 27 individuals were treated with multidimensional projection techniques and non-supervised and supervised machine learning algorithms. The distinction between healthy individuals and patients with cancer on the floor of mouth or oral cavity could only be made with supervised learning. Accuracy above 80% was obtained for the binary classification (YES or NO for cancer) using a Support Vector Machine (SVM) with radial basis function kernel and Random Forest. In the classification considering the type of cancer, the accuracy dropped to ca. 70%. The accuracy tended to increase when clinical information such as alcohol consumption was used in conjunction with the e-tongue data. With the random forest algorithm, the rules to explain the diagnosis could be identified using the concept of Multidimensional Calibration Space. Since the training of the machine learning algorithms is believed to be more efficient when the data of a larger number of patients are employed, the approach presented here is promising for computer-assisted diagnosis.
(Copyright © 2022 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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