Quantum Radiomics Ensemble Model for Glioblastoma Survival Prediction and External Validation on BraTS 2020

Introduction/Objective Glioblastoma (GBM) is the most lethal primary brain malignancy, with a 12-15-month median survival. This study externally validates quantum-inspired machine learning for GBM risk stratification using the BraTS 2020 cohort, assessing cross-domain and cross-tumor-type generalization. The aim was to validate the quantum radiomics methodology on an independent GBM dataset and evaluate cross-domain generalizability from genomics to radiomics applications. Methods A total of 75 radiomics features were extracted from multi-modal MRI (T1, T1ce, T2, FLAIR) of 494 GBM patients. Quantum transformation expanded features to 715 dimensions. A calibrated ensemble model was trained for 12-month survival risk stratification with a 60/15/25% train/validation/test split. Results Test performance achieved 96.80% accuracy [95% CI: 94.92%-98.68%], 99.97% AUC- ROC, 100% precision, 93.55% sensitivity, and 100% specificity. Perfect specificity (zero false positives) enables confident low-risk prediction. Performance outperformed publicized benchmarks: traditional radiomics (73-76%), simple deep learning (82-88%), and complex deep learning models (89-92%). The small validation-test gap (98.65% and 96.80%) also indicates adequate generalization. Discussion The significant advantage over current methods suggests that quantum-inspired feature transformation is capable of capturing nonlinear patterns in radiomics data, which conventional methodologies fail to do. Perfect specificity has clinical significance, indicating that confident identification of low-risk patients can be achieved for decisions about treatment planning. The out-of-domain success of cross-domain from genomics to radiomics indicates domain independence for the quantum transform. Conclusion Quantum radiomics generalizes to external validation with clinical-grade performance. Cross-domain validation indicates the domain-agnostic nature of quantum transformation as an improvement to biomedical data and its potential for clinical treatment planning.