PFCA-Net: a post-fusion based cross-attention model for predicting PCa Gleason Group using multiparametric MRI

Prostate cancer (PCa) is a malignancy originating from epithelial cells within the prostate gland. The gold standard for diagnosing PCa is typically based on the Gleason score. However, the inherent variability in biopsy sampling and its potential discordance with radical prostatectomy outcomes can result in the misclassification of the International Society of Urological Pathology (ISUP) Gleason Group (GG). Furthermore, the employment of prostate-specific antigen (PSA) for screening, guiding biopsy decisions, and the reduction in PSA biopsy thresholds has led to a notable increase in unwarranted biopsies among patients with PCa. Consequently, developing an efficient and accurate method to predict ISUP GG is imperative. Currently, most studies focus on ISUP GG binary classification for specific GG. In this study, we leverage multiparametric Magnetic Resonance Imaging (mpMRI) images, encompassing T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC), to introduce a post-fusion based cross-attention model named PFCA-Net. The model is designed to predict ISUP GG categories, specifically GG 0/1, GG 2, GG 3, and GG 4/5. We validate our approach using three distinct deep learning classification models and a mpMRI pre-fusion classification model. Our proposed approach demonstrates exceptional performance, attaining an accuracy (ACC) of 0.9692 and an area under the curve (AUC) of 0.9986, over a dataset comprising 107 PCa patients and encompassing a total of 927 MRI images. This study emphasizes the practical value of mpMRI in distinguishing various ISUP GG categories and expediting the evaluation of PCa GG by clinicians.