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

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).