Surface-Based Ultrasound Scans for the Screening of Prostate Cancer

IEEE Open J Eng Med Biol. 2024 Nov 20:6:212-218. doi: 10.1109/OJEMB.2024.3503494. eCollection 2025.

Abstract

Surface-based ultrasound (SUS) systems have undergone substantial improvement over the years in image quality, ease-of-use, and reduction in size. Their ability to image organs non-invasively makes them a prime technology for the diagnosis and monitoring of various diseases and conditions. An example is the screening/risk- stratification of prostate cancer (PCa) using prostate-specific antigen density (PSAD). Current literature predominantly focuses on prostate volume (PV) estimation techniques that make use of magnetic resonance imaging (MRI) or transrectal ultrasound (TRUS) imaging, while SUS techniques are largely overlooked. If a reliable SUS PCa screening method can be introduced, patients may be able to forgo unnecessary MRI or TRUS scans. Such a screening procedure could be introduced into standard primary care settings with point-of-care ultrasound systems available at a fraction of the cost of their larger hospital counterparts. This review analyses whether literature suggests it is possible to use SUS-derived PV in the calculation of PSAD.

Keywords: Abdominal ultrasound; PSA-density; machine learning; prostate cancer; surface-based ultrasound.

Grants and funding

The work of Tristan Barrett was supported by the NIHR Cambridge Biomedical Research Centre under Grant NIHR203312, in part by Cancer Research U.K., Cambridge Imaging Centre under Grant C197/A16465, in part by the Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester, and in part by the Cambridge Experimental Cancer Medicine Centre. This work was supported in part by the Academy of Medical Sciences Professorship (APR6\1011), in part by Royal Society Wolfson Fellowship, Cancer Research UK under Grant EDDPMA-Nov21\100026, in part by the National Institutes of Health (NIH) Bench-to-Bedside Award, in part by the NIH Center for Interventional Oncology under Grant ZID BC011242 and Grant CL040015, and in part by the Intramural Research Program of the National Institutes of Health.