Enhancing mpMRI-Based Prostate Cancer Detection by Ensemble Quantum Machine Learning Models
DOI:
https://doi.org/10.62292/10.62292/njp.v34i2.2025.404Keywords:
Prostate Cancer Detection, Quantum Machine Learning, Ensemble Learning, Variational Quantum Classifier, Quantum Neural Network, PROSTATEx Dataset, Radiomics, Angle EncodingAbstract
Prostate cancer is among the most prevalent malignancies in men, and early, accurate diagnosis is critical for effective treatment. Traditional machine learning techniques have demonstrated success in analyzing multiparametric magnetic resonance imaging (MRI) and clinical biomarkers; however, their scalability and capacity to model complex feature interactions remain limited. This study proposes an Ensemble Quantum Machine Learning (QML) framework to enhance prostate cancer detection using the PROSTATEx Challenge dataset. Radiomic features were extracted from MRI modalities and clinical attributes, then standardized and reduced using principal component analysis (PCA) to match current quantum hardware constraints. Three quantum classifiers—Quantum Support Vector Machine (QSVM), Variational Quantum Classifier (VQC), and Quantum Neural Network (QNN) - were independently trained and integrated using both soft voting and stacked ensemble strategies. Results from stratified 5-fold cross-validation show that the stacked ensemble outperformed individual models and baseline classifiers, achieving an average accuracy of 88.4%, recall of 89.1%, precision of 87.9%, F1-score of 88.5%, and area under the curve - receiver operating characteristic (AUC-ROC) of 0.94 (stack ensemble). These findings highlight the potential of hybrid quantum-classical ensemble learning to improve diagnostic robustness, particularly in reducing false negatives. Furthermore, validation on real quantum hardware demonstrated consistent performance, underscoring the feasibility of QML in near-term medical applications. This work contributes to the growing intersection of quantum computing and clinical AI, offering a scalable and interpretable approach to precision oncology.
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