Publication detail

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

KISS, K. ŠINDELÁŘOVÁ, A. KRBAL, L. STEJSKAL, V. MRÁZOVÁ, K. VRÁBEL, J. KAŠKA, M. MODLITBOVÁ, P. POŘÍZKA, P. KAISER, J.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

Laser-Induced Breakdown Spectroscopy, human skin cancer, malignant melanomy

Authors

KISS, K.; ŠINDELÁŘOVÁ, A.; KRBAL, L.; STEJSKAL, V.; MRÁZOVÁ, K.; VRÁBEL, J.; KAŠKA, M.; MODLITBOVÁ, P.; POŘÍZKA, P.; KAISER, J.

Released

1. 5. 2021

Publisher

Royal Society of Chemistry

Location

CAMBRIDGE

ISBN

1364-5544

Periodical

Journal of Analytical Atomic Spectrometry

Year of study

36

Number

5

State

United Kingdom of Great Britain and Northern Ireland

Pages from

909

Pages to

916

Pages count

8

URL

https://pubs.rsc.org/en/content/articlelanding/2021/JA/D0JA00469C#!divAbstract

Full text in the Digital Library

http://hdl.handle.net/11012/200991

BibTex

@article{BUT171885,
  author="Kateřina {Kiss} and Anna {Šindelářová} and Lukáš {Krbal} and Václav {Stejskal} and Kristýna {Mrázová} and Jakub {Vrábel} and Milan {Kaška} and Pavlína {Modlitbová} and Pavel {Pořízka} and Jozef {Kaiser}",
  title="Imaging margins of skin tumors using laser-induced breakdown spectroscopy and machine learning",
  journal="Journal of Analytical Atomic Spectrometry",
  year="2021",
  volume="36",
  number="5",
  pages="909--916",
  doi="10.1039/d0ja00469c",
  issn="1364-5544",
  url="https://pubs.rsc.org/en/content/articlelanding/2021/JA/D0JA00469C#!divAbstract"
}