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.
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).
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.
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.
European urology oncology. 2025 Jan 28 [Epub ahead of print]
Mirabela Rusu, Hassan Jahanandish, Sulaiman Vesal, Cynthia Xinran Li, Indrani Bhattacharya, Rajesh Venkataraman, Steve Ran Zhou, Zachary Kornberg, Elijah Richard Sommer, Yash Samir Khandwala, Luke Hockman, Zhien Zhou, Moon Hyung Choi, Pejman Ghanouni, Richard E Fan, Geoffrey A Sonn
Department of Radiology Stanford University Stanford CA USA; Department of Urology Stanford University Stanford CA USA; Stanford University, Department of Biomedical Data Science, 300 Pasteur, Stanford, CA USA. Electronic address: ., Department of Radiology Stanford University Stanford CA USA; Department of Urology Stanford University Stanford CA USA., Institute of Computational and Mathematical Engineering Stanford University Stanford CA USA., Department of Radiology Stanford University Stanford CA USA., Eigen Health Services LLC, Grass Valley CA USA., Department of Urology Stanford University Stanford CA USA., School of Medicine Stanford University Stanford CA USA., Peking Union Medical College Hospital Beijing China., Department of Radiology, College of Medicine, The Catholic University of Korea Seoul Korea.