Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI
The globally accepted surgical strategy in glioblastomas is removing the enhancing tumor. However, the peritumoral region harbors infiltration areas responsible for future tumor recurrence. This study aimed to evaluate a predictive model that identifies areas of future recurrence using a voxel-based...
Main Authors: | Santiago Cepeda, Luigi Tommaso Luppino, Angel Pérez-Núñez, Ole Solheim, Sergio García-García, María Velasco-Casares, Anna Karlberg, Live Eikenes, Rosario Sarabia, Ignacio Arrese, Tomás Zamora, Pedro Gonzalez, Luis Jiménez-Roldán, Samuel Kuttner |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-03-01
|
Series: | Cancers |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-6694/15/6/1894 |
Similar Items
-
Machine Learning-based Identification of Local Recurrence Regions in Glioblastoma using Postoperative MRI: Implications for Survival Prognostication
by: Santiago Cepeda, et al.
Published: (2023-01-01) -
Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI
by: Santiago Cepeda, et al.
Published: (2021-10-01) -
Towards Advanced Ultrasound Image Analysis by Combining Radiomics and Artificial Intelligence in Brain Tumors
by: Santiago Cepeda, et al.
Published: (2021-07-01) -
MRI radiomics and potential applications to glioblastoma
by: Grayson W. Hooper, et al.
Published: (2023-02-01) -
Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review
by: Sergio García-García, et al.
Published: (2022-11-01)