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

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.

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Publication Details

Journal
Scientific Reports
Published
2025-04-30
DOI
https://doi.org/10.1038/s41598-025-99795-y
Citations
3
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
7.44

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article

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

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3 citations
Scientific Reports
AI in cancer detection
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Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector

Inês Santiago, Ana Mascarenhas Gaivão, Kostas Marias, Nickolas Papanikolaou, Leonardo Vanneschi, Celso Matos, Daniele Regge, Ana Sofia Castro Verde, Sara Belião, José Guilherme de Almeida, Manolis Tsiknakis, Joana Ip, Sara Silva, Nuno M. Rodrigues, Carlos Bireiro
article en
3 citations

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 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.

Scientific ReportsVol. 15(1)
University of Pisa (IT), Champalimaud Foundation (PT), Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT), Royal Marsden NHS Foundation Trust (GB), University of Lisbon (PT), Radboud University Nijmegen (NL), Universitat de València (ES), Mediterranean University (ME), Hospital Universitari i Politècnic La Fe (ES), Massachusetts General Hospital (US), Centro de Investigación Biomédica en Red de Salud Mental (ES), Institut d'Investigació Biomèdica de Girona (ES), Hospital Quirónsalud Barcelona (ES), Royal Marsden Hospital (GB), Candiolo Cancer Institute (IT), National Cancer Institute (LT), Instituto de Investigación Sanitaria La Fe (ES), University of Turin (IT), Hacettepe University (TR), University of Birmingham (GB), Universidade Nova de Lisboa (PT), Foundation for Research and Technology Hellas (GR)
European Commission, Horizon 2020 Framework Programme, Fundação para a Ciência e a Tecnologia, Horizon 2020
Openalex Percentile: Top 3%
AI in cancer detection
7.44
DOIOpen Access PDFOpenAlex
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