Radiomic Score as a Noninvasive Predictor of Overall Survival in Gliomas: A Multicenter Study

Motivation: Developing a prognostic score out of high-dimensional radiomics features could reflect the underlying pathophysiology of gliomas, enabling an easy-to-use tool for predicting patient prognosis. Goal(s): To develop a radiomics-based risk score (RS) for a preoperative, noninvasive assessment of overall survival (OS) in gliomas. Approach: T2w MRI of a local discovery dataset was used for developing RS, which was then externally validated on an independent dataset (UCSF-PDGM). Results: The tumor grade, age, and RS were significantly associated with OS (P<0.005, for all). RS showed significant prognostic performance in both discovery and validation datasets (P=1.48E-8 and P=1.84E-15, respectively). Impact: Risk stratification in gliomas based on RS could help identify high-risk patients, who may benefit from more aggressive treatment and closer follow-up, enabling personalized approach to care. The RS was established on preoperative T2w-MRI and stably predicts the glioma OS.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/2883
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Radiomic Score as a Noninvasive Predictor of Overall Survival in Gliomas: A Multicenter Study

Ayça Erşen Danyeli, Alp Di̇nçer, M. Necmettin Pamir, Esin Öztürk-Işık et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

Radiomic Score as a Noninvasive Predictor of Overall Survival in Gliomas: A Multicenter Study

Ayça Erşen Danyeli, Alp Di̇nçer, M. Necmettin Pamir, Esin Öztürk-Işık, Koray Özduman, Esra Sümer-Arpak
article en

Abstract

Motivation: Developing a prognostic score out of high-dimensional radiomics features could reflect the underlying pathophysiology of gliomas, enabling an easy-to-use tool for predicting patient prognosis. Goal(s): To develop a radiomics-based risk score (RS) for a preoperative, noninvasive assessment of overall survival (OS) in gliomas. Approach: T2w MRI of a local discovery dataset was used for developing RS, which was then externally validated on an independent dataset (UCSF-PDGM). Results: The tumor grade, age, and RS were significantly associated with OS (P<0.005, for all). RS showed significant prognostic performance in both discovery and validation datasets (P=1.48E-8 and P=1.84E-15, respectively). Impact: Risk stratification in gliomas based on RS could help identify high-risk patients, who may benefit from more aggressive treatment and closer follow-up, enabling personalized approach to care. The RS was established on preoperative T2w-MRI and stably predicts the glioma OS.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 65%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.