Prostate Cancer Classification Using Quantum Machine Learning on Multi-parametric MRI

Prostate cancer is one of the most common malignancies in men, and accurate classification of lesions into clinically significant or insignificant categories is essential for patient management. Multiparametric MRI, including T2-weighted (T2W) and apparent diffusion coefficient (ADC) imaging, enables extraction of quantitative radiomic features that can be exploited by machine learning for improved diagnosis. While classical machine-learning models such as support vector machines (SVM), random forests (RF), and extreme gradient boosting (XGBoost) have shown strong performance in radiomics-based classification, quantum machine learning offers a new paradigm that leverages quantum feature spaces, potentially uncovering complex patterns inaccessible to classical kernels. In this study, we systematically compared three classical classifiers (SVM-RBF, RF, and XGBoost) with three quantum support vector machine (QSVM) variants: amplitude encoding, angle encoding, and angle encoding with a projected quantum kernel, for classifying 299 prostate lesions from the PROSTATEx Challenge dataset. Radiomics features were extracted from T2W and ADC images. A nested stratified cross-validation pipeline was employed, with feature selection performed in each outer fold and hyperparameters optimized via grid search. QSVM-amplitude encoding achieved the highest mean AUC (0.799 ± 0.082), outperforming SVM-RBF (0.608 ± 0.244) and matching or exceeding RF (0.728 ± 0.083) and XGBoost (0.720 ± 0.065), while offering higher sensitivity at comparable specificity. These findings demonstrate that qubit-efficient QSVMs can deliver competitive or superior performance in small-sample, low-dimensional clinical imaging settings, highlighting their potential for prostate cancer lesion classification.