Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis

Area of Science:

• Oncology
• Digital Pathology
• Medical Imaging

Background:

• Renal cell carcinoma (RCC) presents a global health challenge, demanding precise diagnostic methods.
• Traditional pathology faces limitations like interobserver variability and lengthy analysis times.
• Digital pathology offers a promising avenue to overcome these challenges in RCC diagnosis and management.

Purpose of the Study:

• To provide a comprehensive review of digital pathology's current state and future potential in renal cell carcinoma.
• To explore the application of AI and advanced image analysis in RCC diagnostics.
• To discuss the integration of digital pathology with other modalities for multimodal characterization.

Main Methods:

• Review of current digital pathology instruments and AI applications for RCC.
• Analysis of image analysis algorithms for quantifying cellular and molecular markers.
• Exploration of remote collaboration and database creation facilitated by digital platforms.

Main Results:

• AI-driven image analysis enhances accuracy and reproducibility in RCC diagnosis.
• Digital pathology platforms support pathologist collaboration and data archiving for research.
• Integration with radiology and genomics enables multimodal RCC characterization.

Conclusions:

• Digital pathology and AI are poised to significantly improve RCC diagnostics and clinical decision-making.
• These technologies offer enhanced accuracy, reproducibility, and collaborative potential.
• Future advancements will likely solidify AI's role in improving patient outcomes for renal cell carcinoma.