Clinical Evaluation of Quantum-Enhanced RCC Diagnostics: Expandable Quantum Neural Networks Built on Imaging for Realistic Precision Oncology Usage

Abstract

Renal cell carcinoma (RCC) is a lethal urological cancer, and high mortality rates associated with the disease are frequently due to late diagnosis and the functional diversity of a tumor. Current diagnostic paradigms, mostly depending on radiological visualization and histopathological evaluation, are plagued with problems in the form of inter-observer variation and lag time in detection, and are also inadequate in recapitulating the heterogeneous nature of the tumor microenvironment. To address these challenges, in this work we propose an intelligent diagnostic framework combining quantumenhanced expandable neural networks (Q-ENNs) and medical imaging for precision oncology. In contrast to traditional DL models, Q-ENNs leverage quantum parallelism and entanglement-induced strategies to traverse high-dimensional imaging spaces by outperforming feature extraction and diagnostic performance. The scalable design can flexibly scale network layers and qubits according to clinical requirements, allowing multi-stage RCC diagnosis. Q-ENNs trained on multimodal imaging data (including CT, MRI, and histopathological slides) outperform state-of-theart convolutional neural networks (CNNs) in the tasks of tumor detection, staging, and subtype classification. In addition, the quantum-assisted optimization approach accelerates the convergence and alleviates local minima, which are not only benefit for better discrimination performance but also robust diagnostic results among different populations of patients. We validate our results by clinical evaluation, where a better trade-off between sensitivity and specificity with higher interpretability are obtained with respect to state-of-the-art through explainable quantum feature mapping, as claimed in precision oncology terms. The system we present is intended to be deployed in a realistic clinical setting, supporting computational performance (scalability), integration in the existing radiology workflow, and generic imaging modalities. By interfacing principles of quantum computing with clinical applications, this study paves the road for the future of oncology diagnostics. These discoveries underpin that quantum-driven diagnostic systems could change the way RCC is assessed, providing for earlier and more reliable detection, help in the personalization of treatment and thus enhance patient outcomes in precision oncology.

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