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Diagnostics (Basel, Switzerland)
Aug 14, 2023;13 (16):

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.

Noémie Rabilloud, Pierre Allaume, Oscar Acosta, Renaud De Crevoisier, Raphael Bourgade, Delphine Loussouarn, Nathalie Rioux-Leclercq, Zine-Eddine Khene, Romain Mathieu, Karim Bensalah, Thierry Pecot, Solene-Florence Kammerer-Jacquet
Author Information
  1. Noémie Rabilloud: Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes University, 35033 Rennes, France.
  2. Pierre Allaume: Department of Pathology, Rennes University Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France.
  3. Oscar Acosta: Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes University, 35033 Rennes, France.
  4. Renaud De Crevoisier: Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes University, 35033 Rennes, France.
  5. Raphael Bourgade: Department of Pathology, Nantes University Hospital, 44000 Nantes, France. ORCID
  6. Delphine Loussouarn: Department of Pathology, Nantes University Hospital, 44000 Nantes, France.
  7. Nathalie Rioux-Leclercq: Department of Pathology, Rennes University Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France.
  8. Zine-Eddine Khene: Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes University, 35033 Rennes, France.
  9. Romain Mathieu: Department of Urology, Rennes University Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France.
  10. Karim Bensalah: Department of Urology, Rennes University Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France.
  11. Thierry Pecot: Facility for Artificial Intelligence and Image Analysis (FAIIA), Biosit UAR 3480 CNRS-US18 INSERM, Rennes University, 2 Avenue du Professeur Léon Bernard, 35042 Rennes, France.
  12. Solene-Florence Kammerer-Jacquet: Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes University, 35033 Rennes, France.
PMID: 37627935 DOI: 10.3390/diagnostics13162676

Abstract

Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology. Article research was performed using PubMed and Embase to collect relevant articles. A Risk of Bias (RoB) was assessed with an adaptation of the QUADAS-2 tool. Out of the 77 included studies, eight focused on pre-processing tasks such as quality assessment or staining normalization. Most articles ( = 53) focused on diagnosis tasks like cancer detection or Gleason grading. Fifteen articles focused on prediction tasks, such as recurrence prediction or genomic correlations. Best performances were reached for cancer detection with an Area Under the Curve (AUC) up to 0.99 with algorithms already available for routine diagnosis. A few biases outlined by the RoB analysis are often found in these articles, such as the lack of external validation. This review was registered on PROSPERO under CRD42023418661.

Keywords

Gleason grading artificial intelligence convolutional neural networks deep learning digital pathology prostate cancer

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