Advances in artificial intelligence in prostate cancer pathology

Area of Science:

• Pathology
• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Prostate cancer diagnosis relies on pathology, but traditional methods like Gleason grading face challenges like interobserver variability and limited access to expertise.
• Digital pathology and AI offer solutions to improve accuracy, consistency, and accessibility in prostate cancer evaluation worldwide.

Purpose of the Study:

• To explore the role and potential of artificial intelligence (AI) in digital pathology for prostate cancer assessment.
• To discuss the benefits and limitations of AI in improving diagnostic accuracy, risk stratification, and treatment planning.

Main Methods:

• Whole slide imaging (WSI) serves as the basis for AI development, enabling automated analysis for tumor detection, Gleason grading, and molecular prediction.
• AI applications are being developed for diagnostic support, prognostic modeling, and predicting treatment response.

Main Results:

• AI demonstrates potential in augmenting pathology expertise, improving diagnostic efficiency, and accuracy in prostate cancer evaluation.
• Current limitations include data variability, reliance on high-resource training sets, and the need for AI transparency and explainability.

Conclusions:

• AI in digital pathology holds significant promise for advancing prostate cancer care by enhancing diagnostic capabilities and global accessibility.
• Collaborative efforts are crucial for the safe, equitable, and effective integration of AI tools into clinical pathology practice.