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Harnessing artificial intelligence for prostate cancer management.

Lingxuan Zhu1, Jiahua Pan2, Weiming Mou3

  • 1Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Changping Laboratory, Beijing, China.

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Artificial intelligence (AI) enhances prostate cancer (PCa) pathology by improving detection, grading, and outcome prediction. AI tools can assist pathologists, reducing workload and aiding treatment decisions.

Keywords:
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Area of Science:

  • Pathology
  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Prostate cancer (PCa) is a prevalent male malignancy.
  • Traditional pathology review is time-consuming and subjective.
  • Digital pathology and whole-slide imaging pave the way for AI applications.

Purpose of the Study:

  • To review the advancements and applications of AI in prostate cancer pathology.
  • To highlight AI's role in PCa detection, grading, outcome prediction, and molecular subtyping.
  • To discuss the development process, challenges, and resources for AI pathology models in PCa.

Main Methods:

  • Review of current literature on AI in prostate cancer pathology.
  • Analysis of AI applications in PCa detection, grading, and outcome prediction.
  • Summary of AI model development processes, challenges, and available resources.

Main Results:

  • AI demonstrates significant success in detecting and grading prostate cancer.
  • AI aids in predicting patient outcomes and identifying molecular subtypes of PCa.
  • AI-based methods show potential for collaboration with pathologists to optimize clinical decision-making.

Conclusions:

  • AI integration in prostate cancer pathology offers a promising approach to enhance diagnostic accuracy and efficiency.
  • AI tools can support pathologists, reduce workload, and assist in personalized treatment recommendations.
  • Availability of public datasets and open-source codes will accelerate AI development and validation in PCa research.