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

ProCAncer-I Consortium, Rodrigues, N. M., Almeida, J. G. D., Verde, A. S. C., Gaivão, A. M., Bireiro, C., Santiago, I., Ip, J., Belião, S., Matos, C., Vanneschi, L., Tsiknakis, M., Marias, K., Regge, D., Silva, S., & Papanikolaou, N. (2025). Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector. Scientific Reports, 15, 1-15. Article 15211. https://doi.org/10.1038/s41598-025-99795-y --- This work was supported by FCT through the LASIGE Research Unit, ref. UID/000408/2025, and Nuno Rodrigues PhD Grant10.54499/2021.05322.BD (https://doi.org/10.54499/2021.05322.BD). Ana Sofia and José Guilherme de Almeida were supported by the European Union H2020: ProCAncer-I project (EU grant 952159). This work was supported by national funds throughFCT (Fundação para a Ciência e a Tecnologia), under the project UIDB/04152/2020 (https://doi.org/10.54499/UIDB/04152/2020) -Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.

Publication Details

Journal
Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa)
Published
2025-04-30
Primary Topic
Prostate Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
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article

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

Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa)
Prostate Cancer Diagnosis and Treatment
article

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

article en

Abstract

ProCAncer-I Consortium, Rodrigues, N. M., Almeida, J. G. D., Verde, A. S. C., Gaivão, A. M., Bireiro, C., Santiago, I., Ip, J., Belião, S., Matos, C., Vanneschi, L., Tsiknakis, M., Marias, K., Regge, D., Silva, S., & Papanikolaou, N. (2025). Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector. Scientific Reports, 15, 1-15. Article 15211. https://doi.org/10.1038/s41598-025-99795-y --- This work was supported by FCT through the LASIGE Research Unit, ref. UID/000408/2025, and Nuno Rodrigues PhD Grant10.54499/2021.05322.BD (https://doi.org/10.54499/2021.05322.BD). Ana Sofia and José Guilherme de Almeida were supported by the European Union H2020: ProCAncer-I project (EU grant 952159). This work was supported by national funds throughFCT (Fundação para a Ciência e a Tecnologia), under the project UIDB/04152/2020 (https://doi.org/10.54499/UIDB/04152/2020) -Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.

Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa)
European Commission, Horizon 2020 Framework Programme, Fundação para a Ciência e a Tecnologia
Partnerships for the goals
Openalex Percentile: Top 35%
Prostate Cancer Diagnosis and Treatment
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