Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study

Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study

Xin-Yi Gao,1– 3,* Yi-Da Wang,4,* Shi-Man Wu,5,* Wen-Ting Rui,5 De-Ning Ma,1 Yi Duan,4 An-Ni Zhang,1– 3 Zhen-Wei Yao,5 Guang Yang,4 Yan-Ping Yu1– 3 1Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, People’s Republic of China...

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Bibliographic Details
Main Authors: Gao XY, Wang YD, Wu SM, Rui WT, Ma DN, Duan Y, Zhang AN, Yao ZW, Yang G, Yu YP
Format: Article
Language:English
Published: Dove Medical Press 2020-05-01
Series:Cancer Management and Research
Subjects:
glioma recurrence
treatment related effects
t2 flair enhancement
image subtraction
support vector machines
Online Access:https://www.dovepress.com/differentiation-of-treatment-related-effects-from-glioma-recurrence-us-peer-reviewed-article-CMAR

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https://www.dovepress.com/differentiation-of-treatment-related-effects-from-glioma-recurrence-us-peer-reviewed-article-CMAR

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