Harnessing Artificial Intelligence for Risk Stratification and Outcome Prediction in Urologic Cancers: A Systematic...

Area of Science:

• Urologic oncology
• Digital pathology
• Artificial intelligence

Background:

• Digital pathology-based artificial intelligence (DP-AI) biomarkers are emerging as powerful tools for cancer management.
• Their prognostic and predictive utility in urologic cancers requires synthesis of current evidence.

Purpose of the Study:

• To systematically review and synthesize evidence on the prognostic and predictive value of DP-AI models in urologic cancers.
• To assess the readiness of DP-AI biomarkers for clinical integration.

Main Methods:

• A systematic review of MEDLINE, Embase, and Web of Science was conducted for studies on DP-AI in prostate, bladder, renal cell, testicular, and penile cancers.
• Risk of bias was assessed using the ROBINS-I tool, and results were summarized qualitatively.

Main Results:

• 31 studies involving 21,155 patients were included, primarily focusing on prostate cancer.
• DP-AI models demonstrated prognostic associations in various stages of prostate, bladder, renal cell, and testicular cancers.
• Specific DP-AI models showed potential in identifying patients who could omit or benefit from specific therapies, such as androgen deprivation therapy or Bacillus Calmette-Guérin treatment.

Conclusions:

• DP-AI biomarkers show significant promise for enhancing treatment personalization in urologic malignancies.
• Integration into clinical practice could improve patient management, but prospective validation is essential.