Intermediate risk prostate tumors contain lethal subtypes.

Area of Science:

• Oncology
• Pathology
• Computational Biology

Background:

• Prostate cancer (PCa) is a leading cause of cancer death in the US, with intermediate-risk PCa comprising 65% of cases.
• Key intermediate-risk subtypes, cribriform architecture (CA) and intraductal carcinoma of the prostate (IDC-P), are linked to higher metastatic risk, recurrence, and mortality.
• Current biopsy methods, including MRI-guided approaches, have limited sensitivity (54% for CA, 37% for IDC-P) for detecting these high-risk subtypes, leading to potential underdiagnosis.

Purpose of the Study:

• To highlight the diagnostic challenges posed by CA and IDC-P in intermediate-risk PCa.
• To emphasize the need for advanced molecular and computational approaches for reliable detection of these subtypes.
• To underscore the importance of accurate identification for appropriate patient management and treatment selection.

Main Methods:

• Review of current literature on PCa subtypes, diagnostic limitations, and emerging technologies.
• Discussion of computational pathology and AI-driven analysis of biopsy and surgical specimens.
• Exploration of associating identified phenotypes with therapeutic vulnerabilities.

Main Results:

• CA and IDC-P are significant drivers of aggressive disease in intermediate-risk PCa.
• Existing diagnostic methods frequently yield false negatives, hindering accurate risk assessment.
• Computational pathology and AI offer promising avenues for improved detection and characterization of these subtypes.

Conclusions:

• Accurate detection of CA and IDC-P is critical to prevent underestimation of PCa risk and guide treatment decisions.
• Advanced computational and AI-based pathology methods are essential for understanding PCa heterogeneity and improving patient outcomes.
• Further research associating molecular phenotypes with treatment strategies may prevent disease progression and upstaging.