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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F21%3APU141090" target="_blank" >RIV/00216305:26620/21:PU141090 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11150/21:10427195 RIV/00216208:11120/21:43921429 RIV/00179906:_____/21:10427195

Result on the web

<a href="https://pubs.rsc.org/en/content/articlelanding/2021/JA/D0JA00469C#!divAbstract" target="_blank" >https://pubs.rsc.org/en/content/articlelanding/2021/JA/D0JA00469C#!divAbstract</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1039/d0ja00469c" target="_blank" >10.1039/d0ja00469c</a>

Result language

angličtina

Original language name

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

Original language description

Nowadays, 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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10406 - Analytical chemistry

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Analytical Atomic Spectrometry

ISSN

0267-9477

e-ISSN

1364-5544

Volume of the periodical

36

Issue of the periodical within the volume

5

Country of publishing house

GB - UNITED KINGDOM

Number of pages

8

Pages from-to

909-916

UT code for WoS article

000639141400001

EID of the result in the Scopus database

2-s2.0-85105783529