To develop and validate an apparent diffusion coefficient (ADC)-based radiomics predictive model in distinguishing the prostate cancer from benign prostate hyperplasia.

The ADC imaging data of 42 patients (21 cases of prostate cancer, and 21 cases of benign prostate hyperplasia) with histologically confirmed from December 2018 to May 2019 were analyzed retrospectively. The software ITK-SNAP was used to draw the region of interest (ROI), and the radiomic features based on ADC map were generated automatically from Analysis-Kinetics(GE Healthcare). Lasso regression model was used for data dimension reduction. The linear combination of the features selected by Lasso dimensionality reduction screening and the corresponding weighted coefficient product was used to establish prediction model. The model performance was assessed with respect to discrimination using the area under the curve(AUC) of ROC analysis.

396 radiomics features were extracted automatically by software and 5 features were left after redundancy reduction step. Radiomics features after modeling have a good predictive effect for the identification of prostate cancer and benign prostate hyperplasia. The prediction model showed good discrimination in both primary dataset(AUC=0.89, 95%; Accuracy=90%, sensitivity=80%, specificity=100%, positive predictive value=100%, negative predictive value=0.83) and independent validation datset(AUC=0.96, 95%; sensitivity=83%, Accuracy=90%, specificity=83%, positive predictive value=83%, negative predictive value=83%).

The radiomics model could provide important reference in differentiation between prostate cancer and benign prostate hyperplasia.