Artificial intelligence in pathologic diagnosis, prognosis and prediction of prostate cancer.

Am J Clin Exp Urol

Department of Computational Pathology, NovinoAI 1443 NE 4th Ave, Fort Lauderdale, FL 33304, USA.

Published: August 2024

AI Article Synopsis

  • Histopathology is the standard method for diagnosing prostate cancer but faces challenges due to the increasing number of biopsies and the need for precise assessments for patient care.
  • Recent advancements in digital pathology have made it possible to incorporate artificial intelligence (AI) and machine learning techniques to aid pathologists in their work.
  • This review explores the clinical applications of AI in histopathology for prostate cancer, highlighting its potential to improve diagnoses, grading, prognosis evaluation, and overall patient outcomes.

Article Abstract

Histopathology, which is the gold-standard for prostate cancer diagnosis, faces significant challenges. With prostate cancer ranking among the most common cancers in the United States and worldwide, pathologists experience an increased number for prostate biopsies. At the same time, precise pathological assessment and classification are necessary for risk stratification and treatment decisions in prostate cancer care, adding to the challenge to pathologists. Recent advancement in digital pathology makes artificial intelligence and learning tools adopted in histopathology feasible. In this review, we introduce the concept of AI and its various techniques in the field of histopathology. We summarize the clinical applications of AI pathology for prostate cancer, including pathological diagnosis, grading, prognosis evaluation, and treatment options. We also discuss how AI applications can be integrated into the routine pathology workflow. With these rapid advancements, it is evident that AI applications in prostate cancer go beyond the initial goal of being tools for diagnosis and grading. Instead, pathologists can provide additional information to improve long-term patient outcomes by assessing detailed histopathologic features at pixel level using digital pathology and AI. Our review not only provides a comprehensive summary of the existing research but also offers insights for future advancements.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11411179PMC
http://dx.doi.org/10.62347/JSAE9732DOI Listing

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