Artificial Intelligence and Multiomics Beyond PSA Screening in African and Middle Eastern Prostate Cancer Patients

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Prostate cancer (PCa) is a growing global health concern due to aging populations and tumor heterogeneity.
• Current screening methods, like prostate-specific antigen (PSA) testing, suffer from low specificity and sensitivity, leading to overdiagnosis and overtreatment.
• Existing screening limitations disproportionately affect underrepresented ethnic and regional groups, including men of African descent and those in the Middle East and North Africa (MENA).

Purpose of the Study:

• To review the potential of multiomics integration and artificial intelligence (AI) for enhancing PCa detection and risk stratification.
• To highlight the limitations of current PSA-based screening, particularly in diverse populations.
• To advocate for the development of inclusive datasets for equitable precision oncology in PCa.

Main Methods:

• Review of current literature on multiomics (transcriptomics, DNA methylation, proteomics, metabolomics) in PCa.
• Analysis of the role of artificial intelligence (AI) in integrating multiomics data for improved diagnostics.
• Examination of ethnic and regional disparities in PCa screening effectiveness and data representation.

Main Results:

• Multiomics combined with AI can validate biological mechanisms and improve diagnostic reliability for PCa.
• PSA testing's limitations contribute to overdiagnosis, especially of indolent tumors.
• Current datasets lack the diversity needed for equitable and clinically valid AI-driven PCa models.

Conclusions:

• Advancing PCa screening beyond PSA requires integrating multiomics and AI.
• Addressing ethnic and regional disparities is crucial for equitable precision oncology.
• Development of demographically representative datasets is essential for inclusive and effective AI models in PCa care.