Journal article
Clinical and Magnetic Resonance Imaging Radiomics-Based Survival Prediction in Glioblastoma Using Multiparametric Magnetic Resonance Imaging
Journal of computer assisted tomography, Vol.47(6), pp.919-923
11/2023
DOI: 10.1097/RCT.0000000000001493
PMID: 37948367
Abstract
INTRODUCTION Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging-derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS). METHODOLOGY A retrospective, institutional review board-approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models. RESULTS For all patients combined, the clinical feature-derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case. CONCLUSIONS When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature-derived model for predicting PFS.
Details
- Title: Subtitle
- Clinical and Magnetic Resonance Imaging Radiomics-Based Survival Prediction in Glioblastoma Using Multiparametric Magnetic Resonance Imaging
- Creators
- Girish Bathla - Mayo ClinicNeetu Soni - University of Rochester Medical CenterCaitlin Ward - University of MinnesotaRavishankar Pillenahalli MaheshwarappaAmit Agarwal - Mayo Clinic in FloridaSarv Priya - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of computer assisted tomography, Vol.47(6), pp.919-923
- DOI
- 10.1097/RCT.0000000000001493
- PMID
- 37948367
- eISSN
- 1532-3145
- Language
- English
- Electronic publication date
- 07/27/2023
- Date published
- 11/2023
- Academic Unit
- Radiology; Nursing
- Record Identifier
- 9984448055902771
Metrics
7 Record Views