Evaluation of a deep learning prostate cancer detection system on biparametric MRI against radiological reading.

Journal: European radiology

Published Date: Dec 19, 2024

Abstract

OBJECTIVES: This study aims to evaluate a deep learning pipeline for detecting clinically significant prostate cancer (csPCa), defined as Gleason Grade Group (GGG) ≥ 2, using biparametric MRI (bpMRI) and compare its performance with radiological reading.

Authors

Noëlie Debs

CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France. Electronic address: noelie.debs@creatis.insa-lyon.fr.
Alexandre Routier

Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France; Inria, ARAMIS Project-team, F-75013, Paris, France.
Alexandre Bône

Guerbet Research, Villepinte.
Marc-Miche Rohé

Guerbet Research, Paris, France.

Keywords

Aged Deep Learning Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Male Middle Aged Neoplasm Grading Prostatic Neoplasms Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (39699671)

Evaluation of a deep learning prostate cancer detection system on biparametric MRI against radiological reading.

Abstract

Authors

Keywords

External Resources

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