Decoding pathology: the role of computational pathology in research and diagnostics

Area of Science:

• Digital pathology and computational methods.
• Artificial intelligence in medical diagnostics.
• Histopathology and precision medicine.

Background:

• Traditional histopathology faces challenges like low throughput and inter-observer variability.
• These limitations impede the integration of precision medicine in pathology.
• Digital pathology offers a solution through computational analysis.

Purpose of the Study:

• To provide an introductory overview of computational pathology advancements.
• To discuss the implications of these advancements for histopathology research and diagnostics.
• To highlight the role of deep learning in modern pathology.

Main Methods:

• Leveraging computational methods, particularly deep learning (DL) techniques.
• Analyzing histopathology specimens using advanced algorithms.
• Integrating multimodal data sources and foundation models.

Main Results:

• Deep learning models demonstrate impressive performance in various pathology tasks.
• Applications include mutation prediction, pathomics analyses, and prognosis prediction.
• Emerging approaches utilize multimodal data and multi-purpose foundation models.

Conclusions:

• Computational pathology significantly enhances histopathology analysis.
• It promises to accelerate the adoption of precision medicine in diagnostics and research.
• The field is rapidly evolving with new AI-driven methodologies.