Yonsei Med J.
2024 May;65(5):283-292. 10.3349/ymj.2023.0323.
Diffusion- and Perfusion-Weighted MRI Radiomics for Survival Prediction in Patients with Lower-Grade Gliomas
- Affiliations
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- 1Department of Radiology, Research Institute of Radiological Science, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- 2Department of Applied Statistics, Yonsei University, Seoul, Korea
- 3Department of Radiology, Center for Clinical Imaging Data Science, Research Institute of Radiological Sciences, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- 4Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Korea
- 5Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
- 6Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
- KMID: 2555465
- DOI: http://doi.org/10.3349/ymj.2023.0323
Abstract
- Purpose
Lower-grade gliomas of histologic grades 2 and 3 follow heterogenous clinical outcomes, which necessitates risk stratification. This study aimed to evaluate whether diffusion-weighted and perfusion-weighted MRI radiomics allow overall survival (OS) prediction in patients with lower-grade gliomas and investigate its prognostic value.
Materials and Methods
In this retrospective study, radiomic features were extracted from apparent diffusion coefficient, relative cerebral blood volume map, and Ktrans map in patients with pathologically confirmed lower-grade gliomas (January 2012–February 2019). The radiomics risk score (RRS) calculated from selected features constituted a radiomics model. Multivariable Cox regression analysis, including clinical features and RRS, was performed. The models’ integrated area under the receiver operating characteristic curves (iAUCs) were compared. The radiomics model combined with clinical features was presented as a nomogram.
Results
The study included 129 patients (median age, 44 years; interquartile range, 37–57 years; 63 female): 90 patients for training set and 39 patients for test set. The RRS was an independent risk factor for OS with a hazard ratio of 6.01. The combined clinical and radiomics model achieved superior performance for OS prediction compared to the clinical model in both training (iAUC, 0.82 vs.0.72, p=0.002) and test sets (0.88 vs. 0.76, p=0.04). The radiomics nomogram combined with clinical features exhibited good agreement between the actual and predicted OS with C-index of 0.83 and 0.87 in the training and test sets, respectively.
Conclusion
Adding diffusion- and perfusion-weighted MRI radiomics to clinical features improved survival prediction in lowergrade glioma.
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