Explainable attention-enhanced heuristic paradigm for multi-view prognostic risk score development in hepatocellular carcinoma

TL;DR

This paper introduces a new deep learning method to create interpretable risk scores for liver cancer patients, improving on existing staging systems by identifying high-risk tissues and refining patient stratification.

Contribution

A novel attention-enhanced deep learning paradigm for generating interpretable, multi-view risk scores in hepatocellular carcinoma.

Findings

The Hybrid Deep Score (HDS) achieved hazard ratios of 3.24 and 2.34 in two cohorts for Disease-Free Survival.

HDS integration with clinical staging systems improved risk stratification, identifying high-risk patients within low-risk groups.

The Attention Activator (ATAT) identified key tissue junctions linked to high prognostic risk in HCC patients.

Abstract

Existing prognostic staging systems depend on expensive manual extraction by pathologists, potentially overlooking latent patterns critical for prognosis, or use black-box deep learning models, limiting clinical acceptance. This study introduces a novel deep learning-assisted paradigm that complements existing approaches by generating interpretable, multi-view risk scores to stratify prognostic risk in hepatocellular carcinoma (HCC) patients.

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 with high prognostic risk, and a multi-view risk-scoring system based on ATAT established HDS from microscopic to macroscopic levels. HDS was also validated on an external testing cohort (TCGA-LIHC) with 341 HCC patients. We assessed prognostic significance using Cox regression and the concordance index (c-index).

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, which were concatenated and then input into a neural network to generate the final HDS for each patient. The HDS demonstrated competitive results with hazard ratios (HR) (HR 3.24, 95% confidence interval (CI) 1.91–5.43 in SYSUCC; HR 2.34, 95% CI 1.58–3.47 in TCGA-LIHC) and c-index values (0.751 in SYSUCC; 0.729 in TCGA-LIHC) for Disease-Free Survival (DFS). Furthermore, integrating HDS into existing clinical staging systems allows for more refined stratification, which enables the identification of potential high-risk patients within low-risk groups.

This novel paradigm, from identifying high-risk tissues to constructing prognostic risk scores, offers fresh insights into HCC research. Additionally, the integration of HDS complements the existing clinical staging system by facilitating more detailed stratification in DFS and Overall Survival (OS).

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