Deep Learning-Powered Whole Slide Image Analysis in Cancer Pathology

Area of Science:

• Digital pathology
• Artificial intelligence in oncology
• Computational pathology

Background:

• Histopathologic diagnosis is crucial for modern cancer care and precision oncology.
• Accurate biomarker assessment is essential for personalized cancer therapy.
• Whole slide imaging (WSI) digitalizes slides, improving histopathologic evaluation precision and efficiency.

Purpose of the Study:

• To present a framework for integrating deep learning (DL) with WSI analysis in cancer pathology.
• To highlight advancements in clinical tasks driven by DL-WSI.
• To discuss opportunities and challenges for clinical translation of DL-based digital pathology.

Main Methods:

• Review of deep learning approaches (CNN, GCN, Transformer) applied to WSI analysis.
• Integration of DL models with high-resolution digital slide data.
• Exploration of morphologic features beyond pathologist's visual perception.

Main Results:

• DL-WSI enhances sensitivity and accuracy in cancer pathology analysis.
• DL models can automate clinical diagnosis, grade assessment, and outcome prediction.
• Novel morphologic biomarkers can be discovered using DL-WSI.

Conclusions:

• DL-WSI offers significant potential to advance cancer care through automated analysis and biomarker discovery.
• Successful clinical translation requires addressing current opportunities and challenges.
• This approach supports personalized cancer treatment strategies.