〔Abstract〕 Objective Radiomics was used to extract high-throughput texture features from the enhanced magnetic resonance imaging (MRI) of prostate tumors, combined with least absolute shrinkage and selection operator (LASSO) regression algorithm for feature selection. The selected texture features were used to establish a prostate enhanced MRI classification model to automatically detect prostate cancer. Methods A total of 118 patients with prostate cancer who underwent enhanced 3.0T MRI scan were retrospectively analyzed, including 63 cases of prostate cancer and 55 cases of benign prostate tumors confirmed by pathology. The enhanced images were manually segmented on ITK-SNAP 3.6.0. A total of 118 texture features of benign and malignant prostate tumors were quantitatively analyzed by A.K. software, and features with correlation coefficients greater than 0.9 were removed to eliminate repetitive redundancy. Two feature selection methods, max-relevance and min-redundancy (mRMR) and LASSO were used to select the most useful predictive features in the training cohort. LASSO regression was used to establish two automatic detection models for benign and malignant lesions based on tumor stroma and tumor stroma containing peripheral tissues. The performance of the model was analyzed and evaluated by receiver operating characteristic (ROC) curve and Delong test curve. Results The area under curve (AUC) of the test group was 0.819 in the tumor stroma group, and the AUC of the tumor stroma and peripheral group was 0.865. When the threshold of prediction score was 0.5, the sensitivity, specificity and accuracy of the tumor stroma group model, thetumor stroma and peripheral group model were 0.750 and 0.776, 0.875 and 0.912, 0.823 and 0.862, respectively. Delong test was performed on the ROC curve of the two groups, P = 0.0134. Conclusion The radiomics model based on magnetic resonance enhanced image combined with LASSO algorithm shows high performance in automatically detecting prostate cancer. The performance of intratumoral stroma and peripheral group was significantly better than that of the intratumoral stroma group.