Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.

ABSTRACT: Despite being one of the most prevalent cancers, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Currently, several screening and diagnostic tests are required to be carried out in order to detect PCa. These tests are often invasive, requiring either a biopsy (Gleason score and ISUP) or blood tests (PSA). Computational methods have been shown to help this process, using multiparametric MRI (mpMRI) data to detect PCa, effectively providing value during the diagnosis and monitoring stages. While delineating lesions requires a high degree of experience and expertise from the radiologists, being subject to a high degree of inter-observer variability, often leading to inconsistent readings, these computational models can leverage

SUBMITTER: Rodrigues NM 

PROVIDER: S-EPMC12043811 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature