Publication detail

Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics

SOLÁR, P. VALEKOVÁ, H. MARCOŇ, P. MIKULKA, J. BARÁK, M. HENDRYCH, M. STRÁNSKÝ, M. SIRŮČKOVÁ, K. KOSTIAL, M. HOLÍKOVÁ, K. BRYCHTA, J. JANČÁLEK, R.

Original Title

Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics

Type

journal article in Web of Science

Language

English

Original Abstract

Diffusion-weighted imaging (DWI) and its numerical expression via apparent diffusion coefficient (ADC) values are commonly utilized in non-invasive assessment of various brain pathologies. Although numerous studies have confirmed that ADC values could be pathognomic for various ring-enhancing lesions (RELs), their true potential is yet to be exploited in full. The article was designed to introduce an image analysis method allowing REL recognition independently of either absolute ADC values or specifically defined regions of interest within the evaluated image. For this purpose, the line of interest (LOI) was marked on each ADC map to cross all of the RELs’ compartments. Using a machine learning approach, we analyzed the LOI between two representatives of the RELs, namely, brain abscess and glioblastoma (GBM). The diagnostic ability of the selected parameters as predictors for the machine learning algorithms was assessed using two models, the k-NN model and the SVM model with a Gaussian kernel. With the k-NN machine learning method, 80% of the abscesses and 100% of the GBM were classified correctly at high accuracy. Similar results were obtained via the SVM method. The proposed assessment of the LOI offers a new approach for evaluating ADC maps obtained from different RELs and contributing to the standardization of the ADC map assessment.

Keywords

DWI, ADC, brain lesions, segmentation, classification, artifical intelligence

Authors

SOLÁR, P.; VALEKOVÁ, H.; MARCOŇ, P.; MIKULKA, J.; BARÁK, M.; HENDRYCH, M.; STRÁNSKÝ, M.; SIRŮČKOVÁ, K.; KOSTIAL, M.; HOLÍKOVÁ, K.; BRYCHTA, J.; JANČÁLEK, R.

Released

15. 7. 2023

Publisher

Springer Nature

ISBN

2045-2322

Periodical

Scientific Reports

Year of study

13

Number

1

State

United Kingdom of Great Britain and Northern Ireland

Pages count

11

URL

https://doi.org/10.1038/s41598-023-38542-7

Full text in the Digital Library

http://hdl.handle.net/11012/244998

BibTex

@article{BUT184472,
  author="Peter {Solár} and Hana {Valeková} and Petr {Marcoň} and Jan {Mikulka} and Martin {Barák} and Michal {Hendrych} and Matyáš {Stránský} and Kateřina {Novotná} and Martin {Kostial} and Klára {Holíková} and Jindřich {Brychta} and Radim {Jančálek}",
  title="Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics",
  journal="Scientific Reports",
  year="2023",
  volume="13",
  number="1",
  pages="11",
  doi="10.1038/s41598-023-38542-7",
  issn="2045-2322",
  url="https://doi.org/10.1038/s41598-023-38542-7"
}