Hybrid Quantum-Classical Predictive Modelling for Glioblastoma Survival: Addressing Data Scarcity in Resource-Constrained Neuro-Oncology
Abstract
OBJECTIVES: A hybrid quantum-classical machine learning framework was proposed to evaluate whether quantum mechanics could extract high-dimensional patterns from small, localized patient registries, bypassing the small-data bottleneck entirely.
METHOD: An algorithmic pipeline bridging classical preprocessing with quantum state manipulation was designed. Eight core features comprising four structural MRI parameters (tumour volume, oedema index, necrotic core ratio, and perfusion intensity) and four molecular markers were mapped into a low-dimensional Hilbert space using quantum amplitude encoding. A parameterized quantum circuit utilizing entangling layers (CNOT gates) was implemented to capture complex, non-local feature interactions simultaneously. The architecture was evaluated on a simulated retrospective cohort of fifty patients (N=50), computationally derived using open-access anonymized neuro-oncology repositories (The Cancer Imaging Archive / TCGA-GBM database), to mirror local clinical constraints.
RESULTS: While standard classical models (multi-layer perceptrons and random forests) failed to generalize due to severe overfitting on the small training sample, the HQCNN pipeline demonstrated robust convergence. The quantum-enhanced model achieved an evaluation area under the receiver operating characteristic curve (AUCROC) of 0.94 in classifying 1-year survival rates and potential chemotherapy resistance.
CONCLUSIONS: This framework demonstrates that shifting the…
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Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 50 participants or records.
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