TNM staging of esophageal cancer using fine-tuned pathology foundation models and multiple instance learning.

Background: Esophageal cancer is a common and aggressive malignancy worldwide. Accurate TNM staging is essential for clinical decision-making and prognosis. While whole slide images (WSIs) offer rich histopathological details for automated staging, their sheer size and complexity make manual analysis subjective and time-consuming. Consequently, advanced deep learning models are critically needed to improve the accuracy and efficiency of WSI-based TNM staging. Methods: We propose a novel deep learning approach that integrates a fine-tuned pathology foundation model within a Multiple Instance Learning (MIL) framework for automated TNM staging from WSIs. To boost predictive accuracy, our model is enhanced with a feature attention mechanism to pinpoint critical tumor areas and an adaptive layer for effective multi-scale feature fusion. Furthermore, we developed a class-weighted cross-entropy loss function to address pronounced TNM stage imbalance, targeting composite pathological stage classification as a single multi-class prediction task rather than separate T, N, and M components. Performance was rigorously evaluated using three-fold cross-validation and benchmarked against state-of-the-art methods. Results: Our model achieved outstanding performance on the TCGA-ESCA dataset, outperforming existing methods in accuracy, macro F1-score, and AUC. These results confirm the model's effectiveness. Visualization analysis further showed that the model accurately localizes tumor regions and their boundaries, increasing the interpretability and reliability of its predictions. Conclusion: Our study validates the potential of a WSI-based deep learning approach for accurate prediction of esophageal cancer's TNM stage. This tool has the potential to augment pathological diagnosis, refine patient risk assessment, and ultimately guide better treatment strategies.