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

OBJECTIVE

To develop a prognostic prediction model for early-stage triple-negative breast cancer (TNBC) using H&E-stained pathological images and to investigate its underlying biological interpretability.

METHODS

A deep learning model was trained on 340 WSIs and externally validated using 81 TCGA cases. Image-derived features extracted through convolutional neural networks were integrated with clinicopathological variables. Model performance was assessed using ROC curve analysis, and interpretability was evaluated by correlating image features with mRNA-seq data and characteristics of the immune microenvironment.

RESULTS

The model achieved AUCs of 0.86 and 0.75 in the training and validation cohorts, respectively. Analysis using HoVer-Net indicated that lymphocyte abundance was associated with recurrence risk. Texture-related features showed significant correlations with immune cell infiltration and prognostic gene expression profiles.

CONCLUSION

This study demonstrates that deep learning can enable accurate prognostic prediction in early-stage TNBC, with interpretable image features that reflect the tumor immune microenvironment and gene expression profiles.