In Urologic oncology
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.
Busby Dallin, Grauer Ralph, Pandav Krunal, Khosla Akshita, Jain Parag, Menon Mani, Haines G Kenneth, Cordon-Cardo Carlos, Gorin Michael A, Tewari Ashutosh K
2023-Jan-11
Artificial intelligence, Deep learning, Gleason grading, Histopathology, Machine learning, Prostate cancer
