The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas

Alis, D.; Bagcilar, O.; Senli, Y.; İŞLER, Cihan; Yergin, M.; Kocer, N.; Islak, C.; Kizilkilic, O.

doi:10.1016/j.crad.2019.12.008

The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas

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Alis D., Bagcilar O., Senli Y. D., İŞLER C., Yergin M., Kocer N., ...More

CLINICAL RADIOLOGY, vol.75, no.5, pp.351-357, 2020 (SCI-Expanded)

Publication Type: Article / Editorial Material
Volume: 75 Issue: 5
Publication Date: 2020
Doi Number: 10.1016/j.crad.2019.12.008
Journal Name: CLINICAL RADIOLOGY
Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, CINAHL, EMBASE, MEDLINE
Page Numbers: pp.351-357
Istanbul University Affiliated: No

Abstract

AIM: To explore the value of quantitative texture analysis of conventional magnetic resonance imaging (MRI) sequences using artificial neural networks (ANN) for the differentiation of high-grade gliomas (HGG) and low-grade gliomas (LGG).