Abstract
BACKGROUND AND OBJECTIVE: To assess whether conventional brightness-mode (B-mode) transrectal ultrasound images of the prostate reveal clinically significant cancers with the help of artificial intelligence methods. METHODS: This study included 2986 men who underwent biopsies at two institutions. We trained the PROstate Cancer detection on B-mode transrectal UltraSound images NETwork (ProCUSNet) to determine whether ultrasound can reliably detect cancer. Specifically, ProCUSNet is based on the well-established nnUNet frameworks, and seeks to detect and outline clinically significant cancer on three-dimensional (3D) examinations reconstructed from 2D screen captures. We compared ProCUSNet against (1) reference labels (n = 515 patients), (2) eight readers that interpreted B-mode ultrasound (n = 20-80 patients), and (3) radiologists interpreting magnetic resonance imaging (MRI) for clinical care (n = 110 radical prostatectomy patients). KEY FINDINGS AND LIMITATIONS: ProCUSNet found 82% clinically significant cancer cases with a lesion boundary error of up to 2.67 mm and detected 42% more lesions than ultrasound readers (sensitivity: 0.86 vs 0.44, p < 0.05, Wilcoxon test, Bonferroni correction). Furthermore, ProCUSNet has similar performance to radiologists interpreting MRI when accounting for registration errors (sensitivity: 0.79 vs 0.78, p > 0.05, Wilcoxon test, Bonferroni correction), while having the same targeting utility as a supplement to systematic biopsies. CONCLUSIONS AND CLINICAL IMPLICATIONS: ProCUSNet can localize clinically significant cancer on screen capture B-mode ultrasound, a task that is particularly challenging for clinicians reading these examinations. As a supplement to systematic biopsies, ProCUSNet appears comparable with MRI, suggesting its utility for targeting suspicious lesions during the biopsy and possibly for screening using ultrasound alone, in the absence of MRI.