Machine Learning-Based Integration of Dosiomics and Pre-Radiotherapy Multimodal MRI Radiomics for Survival Stratification in Patients with Glioblastoma Multiforme

Background Tumor heterogeneity is a significant factor contributing to the marked differences in survival rates among glioblastoma multiforme (GBM) patients, who face a poor prognosis. To improve personalized treatment, it is essential to identify specific tumor characteristics that capture this variability and aid in predicting survival. This study aimed to evaluate the utility of dosiomics and radiomics in predicting overall survival (OS). The central hypothesis was that integrating dosiomics and radiomics could improve survival outcome predictions. Methods A total of 74 GBM patients from The Cancer Imaging Archive were retrospectively included. Dosiomic features from the gross tumor volume (GTV) of planned dose distributions, along with radiomic features from the contrast-enhanced tumor (CET) and edema/non-contrast-enhanced tumor (ED/nCET) subregions across various pre-radiation MRI modalities, were extracted and optimized using L1-based feature selection. Logistic Regression (LR) models were built utilizing different feature configurations to assess the discriminative power of dosiomic and radiomic features, considering the impact of heterogenous subregions. Model performance was assessed through stratified 10-fold cross-validation. Results The dosiomic model exhibited a mean area under the receiver operating characteristic curve (AUC) of 0.80 0.12. The subregion-based models demonstrated mean AUC values of 0.90 0.09 for the CET subregion and 0.76 0.10 for the ED/nCET subregion, indicating that the CET subregion significantly outperformed the ED subregion (p-value < 0.05). The mean AUC values for modality-based models were as follows: 0.86 0.12 for T1CE, 0.84 0.18 for T1, 0.85 0.14 for T2, and 0.76 0.21 for FLAIR sequences. There was no significant difference in discrimination power among the four modalities (p-value >0.05). The combined CET and dosiomic model improved performance to 0.96 0.07 (p < 0.05). Conclusions Dosiomic and pre-radiotherapy MRI-derived radiomic features are capable of stratifying GBM patients into two long-term and short-term groups. Notably, the integration of dosiomics and radiomics significantly enhances survival prediction in GBM patients.