Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI

Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI

Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-...

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Bibliographic Details
Main Authors: Santiago Cepeda, Angel Pérez-Nuñez, Sergio García-García, Daniel García-Pérez, Ignacio Arrese, Luis Jiménez-Roldán, Manuel García-Galindo, Pedro González, María Velasco-Casares, Tomas Zamora, Rosario Sarabia
Format: Article
Language:English
Published: MDPI AG 2021-10-01
Series:Cancers
Subjects:
glioblastoma
radiomics
texture analysis
survival
machine learning
Online Access:https://www.mdpi.com/2072-6694/13/20/5047

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