Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.

Area of Science:

• Computational pathology
• Oncology
• Artificial intelligence in medicine

Background:

• The tumor microenvironment (TME) is a complex ecosystem crucial for tumor progression and treatment response.
• Traditional manual pathological diagnosis of TME is subjective and inefficient.
• Whole-slide imaging and deep learning offer advanced tools for TME characterization.

Purpose of the Study:

• To review recent advances in deep learning for panoramic TME feature recognition from H&E slides.
• To highlight the clinical applications of these computational pathology techniques.
• To discuss the translational potential and limitations of deep learning in TME analysis.

Main Methods:

• Comprehensive literature review of deep learning applications in TME analysis.
• Integration of whole-slide imaging technology with deep learning algorithms.
• Automated characterization of cellular, spatial, and molecular TME heterogeneity.

Main Results:

• Deep learning enables automated and efficient analysis of TME features from H&E slides.
• These methods provide integrated insights into TME heterogeneity.
• Potential for improved precision diagnostics and prediction of therapeutic outcomes.

Conclusions:

• Deep learning-driven computational pathology significantly advances TME characterization.
• The technology shows promise for improving oncology diagnostics and treatment prediction.
• Further research is needed to address current limitations for clinical translation.