Prognostic Modeling of Overall Survival in Glioblastoma Using Radiomic Features Derived from Intraoperative Ultrasound: A Multi-Institutional Study

Intraoperative ultrasound relies on acoustic properties for tissue characterization, capturing unique features of glioblastomas. This study demonstrated that radiomic features derived from this imaging modality have the potential to support the development of survival models.

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Publication Details

Journal
Cancers
Published
2025-01-16
DOI
https://doi.org/10.3390/cancers17020280
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
2.72
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article

Prognostic Modeling of Overall Survival in Glioblastoma Using Radiomic Features Derived from Intraoperative Ultrasound: A Multi-Institutional Study

O Esteban Sinovas, Massimiliano Del Bene, Ilyess Zemmoura, Santiago Cepeda et al.
2 citations
Cancers
Radiomics and Machine Learning in Medical Imaging
2.72
article

Prognostic Modeling of Overall Survival in Glioblastoma Using Radiomic Features Derived from Intraoperative Ultrasound: A Multi-Institutional Study

O Esteban Sinovas, Massimiliano Del Bene, Ilyess Zemmoura, Santiago Cepeda, Arianna Barbotti, Francesco DiMeco, Ignacio Arrese, Aliasgar Moiyadi, Timothy R. West, Brian V. Nahed, Giuseppe Roberto Giammalva, Rosario Sarabia, Vikas Singh
article en
2 citations

Abstract

Intraoperative ultrasound relies on acoustic properties for tissue characterization, capturing unique features of glioblastomas. This study demonstrated that radiomic features derived from this imaging modality have the potential to support the development of survival models.

CancersVol. 17(2)
Université de Tours (FR), Harvard University (US), Johns Hopkins University (US), Inserm (FR), University of Milan (IT), Homi Bhabha National Institute (IN), Tata Memorial Hospital (IN), Massachusetts General Hospital (US), Centre Hospitalier Universitaire de Tours (FR), Azienda di Rilievo Nazionale ed Alta Specializzazione (IT), Neurological Surgery (US), Hospital Universitario Río Hortega (ES), Fondazione IRCCS Istituto Neurologico Carlo Besta (IT), Mass General Brigham (US)
Good health and well-being
Openalex Percentile: Top 13%
Radiomics and Machine Learning in Medical Imaging
2.72
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