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

PURPOSE: We propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence. MATERIALS AND METHODS:...

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Detalles Bibliográficos
Autores principales: Gao, Xin-Yi, Wang, Yi-Da, Wu, Shi-Man, Rui, Wen-Ting, Ma, De-Ning, Duan, Yi, Zhang, An-Ni, Yao, Zhen-Wei, Yang, Guang, Yu, Yan-Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7213892/
https://www.ncbi.nlm.nih.gov/pubmed/32440216
http://dx.doi.org/10.2147/CMAR.S244262

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7213892/
https://www.ncbi.nlm.nih.gov/pubmed/32440216
http://dx.doi.org/10.2147/CMAR.S244262

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