Differentiating Brain Metastasis and High-Grade Glioma Using Multi-b Value Diffusion MRI and Tumor Volumetry
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F25%3A00082971" target="_blank" >RIV/65269705:_____/25:00082971 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14110/25:00144753
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1111/jon.70103" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1111/jon.70103</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/jon.70103" target="_blank" >10.1111/jon.70103</a>
Alternative languages
Result language
angličtina
Original language name
Differentiating Brain Metastasis and High-Grade Glioma Using Multi-b Value Diffusion MRI and Tumor Volumetry
Original language description
Background and Purpose: To evaluate feasibility of multi-b value diffusion magnetic resonance imaging (MRI) and volumetry in differentiating between brain metastases and high-grade gliomas (HGGs) while producing a differentiation tool.Methods: Preoperative brain MRI consisting of both morphological and multi-b value diffusion sequences of patients with HGGs and brain metastases was prospectively performed. Three-dimensional masks of enhancing and non-enhancing tumor and surrounding edema were semiautomatically segmented. Multiple diffusion parameters were subsequently derived together with volumes of the particular tissues. Histogram analysis of the diffusion parameters was performed, and the parameters' diagnostic power to differentiate between the subgroups was evaluated by receiver operating characteristic analysis and least absolute shrinkage and selection operator (LASSO) regression method.Results: A training dataset included 97 consecutive patients (67 HGGs, 30 metastases), whereas 17 patients (9 HGGs and 8 metastases) comprised a validation group. Overall, 66 histogram diffusion parameters and tissue volumes were found to differ significantly between metastasis and HGG subgroups. LASSO regression identified 17 of these as best predictors. A decision tree using four parameters achieved sensitivity of 90% and 87.5% and specificity of 97% and 77.8% for the training and validation subgroups, respectively.Conclusion: Multi-b diffusion MRI and tumor volumetry may be valuable diagnostic tools for differentiating HGG from brain metastasis.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30224 - Radiology, nuclear medicine and medical imaging
Result continuities
Project
<a href="/en/project/NU21-08-00359" target="_blank" >NU21-08-00359: Classification of brain tumors using advanced techniques of multimodal diffusion MRI data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Neuroimaging
ISSN
1051-2284
e-ISSN
1552-6569
Volume of the periodical
35
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
"e70103"
UT code for WoS article
001652265600016
EID of the result in the Scopus database
2-s2.0-105021638925