Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector
Abstract
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 $$\ge$$ 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.
Article Details
Authors (87)
Nuno M. Rodrigues
Ana Sofia Castro Verde
Ana Mascarenhas Gaivão
Carlos Bireiro
Inês Santiago
Joana Ip
Sara Belião
Celso Matos
Leonardo Vanneschi
Sara Silva
Manolis Tsiknakis
Kostas Marias
Stelios Sfakianakis
Varvara Kalokyri
Eleftherios Trivizakis
Grigorios Kalliatakis
Avtantil Dimitriadis
Dimitris Fotiadis
Nikolaos Tachos
Eugenia Mylona
Dimitris Zaridis
Charalampos Kalantzopoulos
Nikolaos Papanikolaou
José Guilherme de Almeida
Ana Castro Verde
Ana Carolina Rodrigues
Nuno Rodrigues
Miguel Chambel
Henkjan Huisman
Maarten de Rooij
Anindo Saha
Jasper J. Twilt
Jurgen Futterer
Luis Marti-Bonmati
Leonor Cerdá-Alberich
Gloria Ribas
Silvia Navarro
Manuel Marfil
Emanuele Neri
Giacomo Aringhieri
Lorenzo Tumminello
Vincenzo Mendola
Deniz Akata
Mustafa Ozmen
Ali Devrim Karaosmanoglu
Firat Atak
Musturay Karcaaltincaba
Joan C. Vilanova
Jurgita Usinskiene
Ruta Briediene
Audrius Untanas
Kristina Slidevska
Katsaros Vasilis
Georgiou Georgios
Dow-Mu Koh
Robby Emsley
Sharon Vit
Ana Ribeiro
Simon Doran
Tiaan Jacobs
Gracián García-Martí
Daniele Regge
Valentina Giannini
Simone Mazzetti
Giovanni Cappello
Giovanni Maimone
Valentina Napolitano
Sara Colantonio
Maria Antonietta Pascali
Eva Pachetti
Giulio del Corso
Danila Germanese
Andrea Berti
Department of Physics, University of Trieste, via Valerio 2, 34127 Trieste, Italy
Gianluca Carloni
Jayashree Kalpathy-Cramer
Christopher Bridge
Joao Correia
Walter Hernandez
Zoi Giavri
Christos Pollalis
Dimitrios Agraniotis
Ana Jiménez Pastor
Jose Munuera Mora
Clara Saillant
Theresa Henne
Rodessa Marquez
Nickolas Papanikolaou