Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Abstract Background Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-n...

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Main Authors: Carole H. Sudre, Jasmina Panovska-Griffiths, Eser Sanverdi, Sebastian Brandner, Vasileios K. Katsaros, George Stranjalis, Francesca B. Pizzini, Claudio Ghimenton, Katarina Surlan-Popovic, Jernej Avsenik, Maria Vittoria Spampinato, Mario Nigro, Arindam R. Chatterjee, Arnaud Attye, Sylvie Grand, Alexandre Krainik, Nicoletta Anzalone, Gian Marco Conte, Valeria Romeo, Lorenzo Ugga, Andrea Elefante, Elisa Francesca Ciceri, Elia Guadagno, Eftychia Kapsalaki, Diana Roettger, Javier Gonzalez, Timothé Boutelier, M. Jorge Cardoso, Sotirios Bisdas
Format: Article
Language:English
Published: BMC 2020-07-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Diagnostic machine learning
Glioma stratification
Isocitrate dehydrogenase; DSC-MRI
Online Access:http://link.springer.com/article/10.1186/s12911-020-01163-5

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http://link.springer.com/article/10.1186/s12911-020-01163-5

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