Computer-aided diagnosis based on 3D deep convolutional neural network system using novel 3D magnetic resonance imaging sequences for high-grade prostate cancer.

Abstract

Background: With the rising incidence of prostate cancer (PCa), there is a global demand for assistive tools that aid in the diagnosis of high-grade PCa. This study aimed to develop a diagnostic support system for high-grade PCa using innovative magnetic resonance imaging (MRI) sequences in conjunction with artificial intelligence (AI).

Materials and methods: We examined image sequences of 254 patients with PCa obtained from diffusion-weighted and T2-weighted imaging, using novel MRI sequences before prostatectomy, to elucidate the characteristics of the 3-dimensional (3D) image sequences. The presence of PCa was determined based on the final diagnosis derived from pathological results after prostatectomy. A 3D deep convolutional neural network (3DCNN) was used as the AI for image recognition. Data augmentation was conducted to enhance the image dataset. High-grade PCa was defined as Gleason grade group 4 or higher.

Results: We developed a learning system using a 3DCNN as a diagnostic support system for high-grade PCa. The sensitivity and area under the curve values were 85% and 0.82, respectively.

Conclusions: The 3DCNN-based AI diagnostic support system, developed in this study using innovative 3D multiparametric MRI sequences, has the potential to assist in identifying patients at a higher risk of pretreatment of high-grade PCa.