Radiomics-based quantification of tumor infiltration in the non-enhancing peritumoral region on postoperative MRI is associated with survival in glioblastoma

Abstract Glioblastoma is characterized by diffuse infiltration, making accurate detection of residual disease essential for improving prognostication and guiding treatment. This study evaluates whether the volume of predicted infiltration, generated by a machine learning (ML) model trained on radiom...

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Hauptverfasser:	Santiago Cepeda, Olga Esteban-Sinovas, Luigi Tommaso Luppino, Samuel Kuttner, Marek Wodzinski, Ole Solheim, Roberto Romero, Angel Pérez-Núñez, Live Eikenes, Anna Karlberg, Ignacio Arrese, Roberto Hornero, Rosario Sarabia
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Nature Portfolio 2025-12-01
Schriftenreihe:	Scientific Reports
Schlagworte:	Glioblastoma Machine learning Radiomics Survival
Online-Zugang:	https://doi.org/10.1038/s41598-025-27711-5
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