Prostate Cancer Detection Using Reflectional Asymmetry Analysis in MRI Images

Multiparametric magnetic resonance imaging (mpMRI) is the standard imaging modality for the detection and evaluation of prostate cancer (PCa); however, diagnostic challenges remain due to overlapping imaging features of malignant and benign tissue. The healthy prostate is approximately reflectionally symmetric, whereas malignant transformation introduces structural asymmetry. A novel algorithm that exploits this asymmetry to detect PCa on T2-weighted (T2W), diffusion weighted (DWI), and apparent diffusion coefficient (ADC) images is presented. This study represents a proof-of-concept evaluation of a symmetry-based, training-free approach. Asymmetry features are extracted across all three sequences using band-pass filtering, adaptive thresholding, and intensity-based criteria, and fused into a unified detection map. The method was evaluated in 66 men with histopathological confirmation (33 biopsy-confirmed PCa cases and 33 biopsy-negative controls). The algorithm correctly detected cancer in 29 of 33 cases (sensitivity 87.9%) and correctly classified 15 of 33 non-cancer cases, yielding a specificity of 45.5% and an overall accuracy of 66.7%. Detection performance was higher for lesions ≥ 10 mm (sensitivity 91.7%) than for lesions < 10 mm (sensitivity 77.8%). The PcaAsym framework demonstrated complete intra-reader reproducibility and substantial inter-reader agreement. These results demonstrate the feasibility of symmetry-based analysis as an interpretable and deterministic approach for PCa detection. Validation in larger, consecutive cohorts is warranted to assess performance in routine clinical settings.