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

• Published in: Scientific reports Vol. 15; no. 1; pp. 15211 - 15
• Main Authors: Rodrigues, Nuno M., de Almeida, José Guilherme, Verde, Ana Sofia Castro, Gaivão, Ana Mascarenhas, Bireiro, Carlos, Santiago, Inês, Ip, Joana, Belião, Sara, Matos, Celso, Vanneschi, Leonardo, Tsiknakis, Manolis, Marias, Kostas, Regge, Daniele, Silva, Sara, Papanikolaou, Nickolas
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.04.2025; Nature Portfolio
• Subjects: 631/114/1305; 631/67/2321; 631/67/589/466; Biopsy; Deep Learning; Humanities and Social Sciences; Humans; Male; multidisciplinary; Multiparametric Magnetic Resonance Imaging - methods; Neoplasm Grading; Prostate-Specific Antigen - blood; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - pathology; Science; Science (multidisciplinary); Unnecessary Procedures
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-99795-y

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 the information from mpMRI to locate the lesions with a high degree of certainty. By considering as positive samples only those that have an ISUP 2 we can train aggressive index lesion detection models. The main advantage of this approach is that, by focusing only on aggressive disease, the output of such a model can also be seen as an indication for biopsy, effectively reducing unnecessary biopsy screenings. In this work, we utilize both the highly heterogeneous ProstateNet dataset, and the PI-CAI dataset, to develop accurate aggressive disease detection models.