Quantum-Enhanced Radiomics for Early Detection and Risk Stratification of Prostate Cancer Using Multiparametric MRI - Meateio Wailters; Jerome Oliver (International Journal of Clinical and Translational Medicine, 2026)
The integration of quantum computing with radiomic analysis of multiparametric magnetic resonance imaging (mpMRI) represents a transformative frontier in the early detection and risk stratification of prostate cancer. This paper presents a systems-level investigation of quantum-enhanced radiomics, focusing not on individual algorithmic performance but on the architectural, governance, infrastructural, and policy dimensions that govern the translation of such hybrid computational pipelines into safe and equitable clinical practice. We analyze the structural trade-offs inherent in designing federated learning architectures that couple high-dimensional radiomic feature extraction from prostate mpMRI with variational quantum circuits, tensor network encodings, and quantum kernel methods. The discussion encompasses data provenance across heterogeneous scanner ecosystems, the robustness of quantum-classical decision support systems under distributional shift, and the ethical imperatives of bias mitigation across diverse populations. Further, we examine deployment pathways that must accommodate hospital IT constraints, real-time inference demands, and lifecycle sustainability including model updating and hardware evolution. Through a comparative analysis of centralized, decentralized, and hybrid quantum-cloud configurations, we delineate how early quantum advantage claims intersect with clinical workflow burden, interpretability requirements, and regulatory frameworks such as the FDA’s predetermined change control plans. The paper argues that the success of quantum-enhanced radiomics will depend less on quantum speedup alone and more on the deliberate design of socio-technical infrastructures that prioritize fairness, longitudinal validation, and seamless integration into existing diagnostic pathways.