Deep Learning Based on ResNet-18 for Classification of Prostate Imaging-Reporting and Data System Category 3 Lesions

Rationale and Objectives

To explore the classification and prediction efficacy of the deep learning model for benign prostate lesions, non-clinically significant prostate cancer (non-csPCa) and clinically significant prostate cancer (csPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions.

Materials and Methods

From January 2015 to December 2021, lesions diagnosed with PI-RADS 3 by multi-parametric MRI or bi-parametric MRI were retrospectively included. They were classified as benign prostate lesions, non-csPCa, and csPCa according to the pathological results. T2-weighted images of the lesions were divided into a training set and a test set according to 8:2. ResNet-18 was used for model training. All statistical analyses were performed using Python open-source libraries. The receiver operating characteristic curve (ROC) was used to evaluate the predictive effectiveness of the model. T-SNE was used for image semantic feature visualization. The class activation mapping was used to visualize the area focused by the model.

Results

A total of 428 benign prostate lesion images, 158 non-csPCa images and 273 csPCa images were included. The precision in predicting benign prostate disease, non-csPCa and csPCa were 0.882, 0.681 and 0.851, and the area under the ROC were 0.875, 0.89 and 0.929, respectively. Semantic feature analysis showed strong classification separability between csPCa and benign prostate lesions. The class activation map showed that the deep learning model can focus on the area of the prostate or the location of PI-RADS 3 lesions.

Conclusion

Deep learning model with T2-weighted images based on ResNet-18 can realize accurate classification of PI-RADS 3 lesions.