Applications of artificial intelligence in prostate cancer histopathology

Dallin Busby, Ralph Grauer, Krunal Pandav, Akshita Khosla, Parag Jain, Mani Menon, G. Kenneth Haines, Carlos Cordon-Cardo, Michael A. Gorin, Ashutosh K. Tewari

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

Abstract

The diagnosis of prostate cancer (PCa) depends on the evaluation of core needle biopsies by trained pathologists. Artificial intelligence (AI) derived models have been created to address the challenges posed by pathologists’ increasing workload, workforce shortages, and variability in histopathology assessment. These models with histopathological parameters integrated into sophisticated neural networks demonstrate remarkable ability to identify, grade, and predict outcomes for PCa. Though the fully autonomous diagnosis of PCa remains elusive, recently published data suggests that AI has begun to serve as an initial screening tool, an assistant in the form of a real-time interactive interface during histological analysis, and as a second read system to detect false negative diagnoses. Our article aims to describe recent advances and future opportunities for AI in PCa histopathology.

Original languageEnglish
Pages (from-to)37-47
Number of pages11
JournalUrologic Oncology: Seminars and Original Investigations
Volume42
Issue number3
DOIs
StatePublished - Mar 2024

Keywords

  • Artificial intelligence
  • Deep learning
  • Gleason grading
  • Histopathology
  • Machine learning
  • Prostate cancer

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