Deep learning-based radiomics and machine learning for prognostic assessment in IDH-wildtype glioblastoma after
Jianpeng Liu1, Shufan Jiang2, Yanfei Wu3
1Department of Radiology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China.
Background:
Glioblastoma (GBM) is a highly aggressive brain tumor with a poor prognosis. This study aimed to construct and validate a radiomics-based machine learning model for predicting overall survival (OS) in isocitrate dehydrogenase-wildtype GBM after maximal safe surgical resection using magnetic resonance imaging.
Methods:
A total of 582 patients were retrospectively enrolled, comprising 301 in the training cohort, 128 in the internal validation cohort, and 153 in the external validation cohort. Volumes of interest from contrast-enhanced T1-weighted imaging were segmented into three regions: contrast-enhancing tumor, necrotic non-enhancing core, and peritumoral edema using a ResNet-based segmentation network. A total of 4227 radiomic features were extracted and filtered using Least Absolute Shrinkage and Selection Operator-Cox regression to identify signatures. The prognostic model was constructed using the Mime prediction framework, categorizing patients into high- and low-risk groups based on the median OS. Model performance was assessed using the concordance index (CI) and Kaplan-Meier survival analysis. Independent prognostic factors were identified through multivariable Cox regression analysis, and a nomogram was developed for individualized risk assessment.
Results:
The Step Cox[backward] + RSF model achieved CIs of 0.89, 0.81, and 0.76 in the training, internal, and external validation cohorts. Log-rank tests demonstrated significant survival differences between high- and low-risk groups across all cohorts ( P < 0.05). Multivariate Cox analysis identified age (hazard ratio [HR]: 1.022; 95% CI: 0.979, 1.009, P < 0.05), Karnofsky Performance Status score (HR: 0.970, 95% CI: 0.960, 0.978, P < 0.05), rad-scores of the necrotic non-enhancing core (HR: 8.164; 95% CI: 2.439, 27.331, P < 0.05), and peritumoral edema (HR: 3.748; 95% CI: 1.212, 11.594, P < 0.05) as independent predictors of OS. A nomogram integrating these predictors provided individualized risk assessment.
Conclusion:
This deep learning segmentation-based radiomics model demonstrated robust performance in predicting OS in GBM after maximal safe surgical resection. By incorporating radiomic signatures and advanced machine learning algorithms, it offers a noninvasive tool for personalized prognostic assessment and supports clinical decision-making.
