Abstract: Prostate cancer (PCa) is one of the most common cancers in men, and imaging techniques have been progressively used in its diagnosis. This study develops a prostate cancer model based on magnetic resonance T2-weighted imaging (T2WI) combined with diffusion-weighted imaging (DWI) texture analysis. Significant feature parameters were selected to construct a predictive model for prostate cancer, and the diagnostic value of texture features for disease discrimination was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The results showed that constructing a prostate cancer prediction model using variance T2WI and mean DWI has a high predictive value, with an AUC value of 0.994, which demonstrated that the textural features based on T2WI and DWI have good application value in distinguishing prostate cancer.

Key words: T2 weighted imaging, Diffusion-weighted imaging, Prostate cancer, Benign prostatic hyperplasia, texture analysis