| SOLÁR, Peter, Hana VALEKOVÁ, Petr MARCON, Jan MIKULKA, Martin BARÁK, Michal HENDRYCH, Matyas STRANSKY, Katerina SIRUCKOVA, Martin KOSTIAL, Klára HOLÍKOVÁ, Jindrich BRYCHTA and Radim JANČÁLEK. Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics. Nature Scientific Reports. BERLIN: NATURE RESEARCH, 2023, vol. 13, No 1, p. 1-11. ISSN 2045-2322. Available from: https://dx.doi.org/10.1038/s41598-023-38542-7. |
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@article{2313837,
author = {Solár, Peter and Valeková, Hana and Marcon, Petr and Mikulka, Jan and Barák, Martin and Hendrych, Michal and Stransky, Matyas and Siruckova, Katerina and Kostial, Martin and Holíková, Klára and Brychta, Jindrich and Jančálek, Radim},
article_location = {BERLIN},
article_number = {1},
doi = {http://dx.doi.org/10.1038/s41598-023-38542-7},
keywords = {brain lesions; ADC; machine learning},
language = {eng},
issn = {2045-2322},
journal = {Nature Scientific Reports},
title = {Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics},
url = {https://www.nature.com/articles/s41598-023-38542-7},
volume = {13},
year = {2023}
}
TY - JOUR
ID - 2313837
AU - Solár, Peter - Valeková, Hana - Marcon, Petr - Mikulka, Jan - Barák, Martin - Hendrych, Michal - Stransky, Matyas - Siruckova, Katerina - Kostial, Martin - Holíková, Klára - Brychta, Jindrich - Jančálek, Radim
PY - 2023
TI - Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics
JF - Nature Scientific Reports
VL - 13
IS - 1
SP - 1-11
EP - 1-11
PB - NATURE RESEARCH
SN - 20452322
KW - brain lesions
KW - ADC
KW - machine learning
UR - https://www.nature.com/articles/s41598-023-38542-7
N2 - 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.
ER -
SOLÁR, Peter, Hana VALEKOVÁ, Petr MARCON, Jan MIKULKA, Martin BARÁK, Michal HENDRYCH, Matyas STRANSKY, Katerina SIRUCKOVA, Martin KOSTIAL, Klára HOLÍKOVÁ, Jindrich BRYCHTA and Radim JANČÁLEK. Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics. \textit{Nature Scientific Reports}. BERLIN: NATURE RESEARCH, 2023, vol.~13, No~1, p.~1-11. ISSN~2045-2322. Available from: https://dx.doi.org/10.1038/s41598-023-38542-7.
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