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

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Publikace nespadá pod Filozofickou fakultu, ale pod Lékařskou fakultu. Oficiální stránka publikace je na webu muni.cz.
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SOLÁR Peter VALEKOVÁ Hana MARCON Petr MIKULKA Jan BARÁK Martin HENDRYCH Michal STRANSKY Matyas SIRUCKOVA Katerina KOSTIAL Martin HOLÍKOVÁ Klára BRYCHTA Jindrich JANČÁLEK Radim

Rok publikování 2023
Druh Článek v odborném periodiku
Časopis / Zdroj Nature Scientific Reports
Fakulta / Pracoviště MU

Lékařská fakulta

Citace
www https://www.nature.com/articles/s41598-023-38542-7
Doi http://dx.doi.org/10.1038/s41598-023-38542-7
Klíčová slova brain lesions; ADC; machine learning
Popis 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.
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