Imaging margins of skin tumors using laser-induced breakdown spectroscopy and machine learning

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dc.contributor.authorKiss, Kateřinacs
dc.contributor.authorŠindelářová, Annacs
dc.contributor.authorKrbal, Lukášcs
dc.contributor.authorStejskal, Václavcs
dc.contributor.authorMrázová, Kristýnacs
dc.contributor.authorVrábel, Jakubcs
dc.contributor.authorKaška, Milancs
dc.contributor.authorModlitbová, Pavlínacs
dc.contributor.authorPořízka, Pavelcs
dc.contributor.authorKaiser, Jozefcs
dc.coverage.issue5cs
dc.coverage.volume36cs
dc.date.accessioned2021-08-13T10:52:55Z
dc.date.available2021-08-13T10:52:55Z
dc.date.issued2021-05-01cs
dc.description.abstractNowadays, laser-based techniques play a significant role in medicine, mainly in the ophthalmology, dermatology, and surgical fields. So far, they have presented mostly therapeutic applications, although they have considerable potential for diagnostic approaches. In our study, we focused on the application of laser-based spectroscopy in skin cancer assessment. Recently, lengthy and demanding pathological investigation has been improved with modern techniques of machine learning and analytical chemistry where elemental analysis provides further insight into the investigated phenomenon. This article deals with the complementarity of Laser-Induced Breakdown Spectroscopy (LIBS) with standard histopathology. This includes discussion on sample preparation and feasibility to perform 3D imaging of a tumor. Typical skin tumors were selected for LIBS analysis, namely cutaneous malignant melanoma, squamous cell carcinoma and the most common skin tumor basal cell carcinoma, and a benign tumor was represented by hemangioma. The imaging of biotic elements (Mg, Ca, Na, and K) provides the elemental distribution within the tissue. The elemental images were correlated with the tumor progression and its margins, as well as with the difference between healthy and tumorous tissues and the results were compared with other studies covering this topic of interest. Finally, self-organizing maps were trained and used with a k-means algorithm to cluster various matrices within the tumorous tissue and to demonstrate the potential of machine learning for processing of LIBS data.en
dc.formattextcs
dc.format.extent909-916cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationJournal of Analytical Atomic Spectrometry. 2021, vol. 36, issue 5, p. 909-916.en
dc.identifier.doi10.1039/d0ja00469ccs
dc.identifier.issn1364-5544cs
dc.identifier.other171885cs
dc.identifier.urihttp://hdl.handle.net/11012/200991
dc.language.isoencs
dc.publisherRoyal Society of Chemistrycs
dc.relation.ispartofJournal of Analytical Atomic Spectrometrycs
dc.relation.urihttps://pubs.rsc.org/en/content/articlelanding/2021/JA/D0JA00469C#!divAbstractcs
dc.rightsCreative Commons Attribution-NonCommercial 3.0 Unportedcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1364-5544/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/cs
dc.subjectLaser-Induced Breakdown Spectroscopyen
dc.subjecthuman skin canceren
dc.subjectmalignant melanomyen
dc.titleImaging margins of skin tumors using laser-induced breakdown spectroscopy and machine learningen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-171885en
sync.item.dbtypeVAVen
sync.item.insts2022.01.06 00:54:38en
sync.item.modts2022.01.06 00:15:57en
thesis.grantorVysoké učení technické v Brně. Ústav soudního inženýrství. Ústav soudního inženýrstvícs
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Pokročilé instrumentace a metody pro charakterizace materiálůcs
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav fyzikálního inženýrstvícs
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