An Exploratory Study on Prognostic Prediction and Interpretability Analysis in Early-stage Triple-negative Breast Cancer Using Pathological Images.

Key Points

• This research aims to explore the use of deep learning for prognostic predictions in early-stage triple-negative breast cancer (TNBC) and to analyze the interpretability of the results.
• Utilized deep learning algorithms for image analysis of pathological images.
• Evaluated prognostic outcomes based on image features.
• Analyzed the tumor immune microenvironment and gene expression profiles.
• Deep learning models provided accurate predictions for patient outcomes in early-stage TNBC.
• Identified interpretable features from pathological images that correlate with tumor characteristics.