Prostate cancer T-stage intelligent diagnosis based on MRI images and deep learning

Abstract

Objective To explore the advantages of Swin-Transformer (SwinT) network in the intelligent diagnosis of prostate cancer (PCA) based on MRI images by comparing Dense-Net, Res-Net, and Vision-Transformer (ViT) networks. Methods A total of 3 017 MRI images including T2WI and fT2WI from 152 patients with PCA confirmed by puncture biopsy were retrospectively collected from Shanxi Cancer Hospital and Second Affiliated Hospital of Army Medical University between April 2020 and March 2022. According to the results of clinical T stage, these images were divided into low- to middle-risk group (T≤T2c) and high-risk group (T≥T3a). And then, the patients were randomly divided into training set (n=107), verification set (n=15) and test set (n=30) at a ratio of 7:1:2 to train the intelligent T-stage diagnosis models. Finally, accuracy, precision, confusion matrix, receiver operating characteristic (ROC) curves and areas under the curve (AUC) were used to evaluate the performance of intelligent T-stage diagnosis of each network. Results In the low- to middle-risk and high-risk groups, the accuracy and AUC of above 4 networks were 0.587 and 0.630, 0.410 and 0.477, 0.600 and 0.648, and 0.680 and 0.708, respectively. The Grad-CAM of SwinT networks had the attention almost focused on the prostate, showing the best feature extraction. Conclusion Compared with the models based on Dense-Net, Res-Net and ViT networks, the SwinT model achieves the best predictive performance in the task of classification of PCA MRI images. The model can be used for the automatic diagnosis of PCA T-stage, and is helpful to improve diagnostic efficiency.

Keywords

• deep learning
• prostate cancer
• t stage diagnosis
• classification
• mri