Table 1_Leveraging deep learning for prognostic stratification of hepatocellular carcinoma using histopathological images.docx

Model performance was assessed using the area under the receiver operating characteristic curve and additional metrics. Then Attention Heatmap Visualization was employed to analyze interpretability. The Kolmogorov-Smirnov curve was utilized to stratify patients into distinct risk groups.

Survival analysis was performed to evaluate differences in overall survival between distinct risk groups. Univariate and multivariate Cox regression analyses were used to identify key clinical indicators and pathological features as significant predictors. The riskRegression package generated calibration curves comparing predicted versus observed survival outcomes.

Calibration curves were quantify the deviation between model-predicted probabilities and actual observed outcomes.</p>Results<p>A prognostic prediction model was developed using the CLAM self-attention framework based on TCGA dataset, whose generalization capability was verified by histologically stained slide data from a proprietary dataset. The results showed that the model could achieve better migration. Mortality rates demonstrated a progressive increase across ascending risk groups.

The risk stratification system possesses potential clinical utility for guiding treatment selection. Forest plots generated demonstrated that the risk stratification system functions as an independent prognostic predictor for HCC. Calibration curve analysis confirmed the predictive accuracy of the model, and this finding from Decision Curve Analysis underscores its superior utility in guiding clinical decision-making.</p>Conclusion<p>This study developed a prognostic prediction model, whose risk stratification could be an independent prognostic predictor for HCC and a guide for therapeutic decision-making.</p>