Purpose
Existing prognostic staging systems depend on expensive manual extraction by pathologists, potentially overlooking hidden information, or use black-box deep learning models, which limits their clinical acceptance.This study introduces a novel deep learning-assisted paradigm for creating interpretable, multi-view risk scores to stratify prognostic risk in hepatocellular carcinoma (HCC) patients.
Methods
510 HCC patients were enrolled in an internal dataset (SYSUCC) as training and validation cohorts to develop the Hybrid Deep Score (HDS): The Attention Activator (ATAT) was designed to heuristically identify tissues associated with high prognostic risk, and a multi-view risk scoring system based on ATAT established HDS from microscopic to macroscopic levels. The HDS was also validated on an external testing cohort (TCGA-LIHC) with 341 HCC patients. We assessed the prognostic significance using Cox regression and the concordance index (c-index).
Results
The ATAT first heuristically identified regions where necrosis, lymphocytes, and tumor tissues converge, particularly focusing on their junctions in high-risk patients. From this, this study developed three independent risk factors: microscopic morphological, co-localization, and deep global indicators, ultimately predicting HDS for each patient. The HDS outperformed existing clinical prognostic staging systems, showing higher hazard ratios (HR 3.24, 95% CI 1.91-5.43 in SYSUCC; HR 2.34, 95% CI 1.58-3.47 in TCGA-LIHC) and c-index (0.751 in SYSUCC; 0.729 in TCGA-LIHC) for Disease-Free Survival (DFS).
Conclusion
This novel paradigm, from identifying high-risk tissues to constructing prognostic risk scores, offers fresh insights into HCC research. It more precisely stratifies HCC patients into high- and low-risk groups for DFS and Overall Survival (OS) compared to existing clinical risk staging systems.