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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F23%3A00078794" target="_blank" >RIV/00159816:_____/23:00078794 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14110/23:00131736 RIV/00216305:26220/23:PU148884

Result on the web

<a href="https://www.nature.com/articles/s41598-023-38542-7" target="_blank" >https://www.nature.com/articles/s41598-023-38542-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-023-38542-7" target="_blank" >10.1038/s41598-023-38542-7</a>

Result language

angličtina

Original language name

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

Original language description

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&apos; 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.

Czech name

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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

30100 - Basic medicine

Project

<a href="/en/project/LTC20027" target="_blank" >LTC20027: Mapping of glioma heterogeneity and infiltration extend by MR imaging biomarkers</a><br>

Continuities

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

Publication year

2023

Confidentiality

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

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

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Volume of the periodical

13

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

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UT code for WoS article

001055239000016

EID of the result in the Scopus database

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