Interpretable Staging Prediction of Liver Cancer Based on Joint-Knowledge Network

Clinical staging is crucial for treatment strategies and improving 5-year survival rates in hepatocellular carcinoma (HCC) patients. However, existing methods struggle to distinguish stages with highly similar textual features. Additionally, their lack of interpretability hampers their practical application in medical scenarios. Here, we introduce KnowST, a joint-knowledge network designed to leverage task relevance to explore implicit knowledge for interpretable staging prediction of liver cancer. First, the relevance of auxiliary tasks and the main task is established from two perspectives to guide the model's focus on staging-related implicit knowledge in radiology reports. Stages-to-stages: KnowST learns the inter-stage distinctions between different stages and the similarities within the same stages, using these as important references for staging differentiation. Factors-to-stages: Clinically, staging is determined by multiple tumor factors. These factors can serve as effective clues to assist KnowST in predicting the correct stage, especially in the case of confusing stages. Second, domain-specific word embeddings are introduced to bridge the gap between pre-trained language models and Chinese radiology reports. Lastly, tumor factor prediction enhances the credibility of the deep model in staging prediction, and its visualized results effectively demonstrate the model's interpretability. Overall, KnowST leverages the joint-knowledge from these two perspectives, effectively utilizing implicit information in radiology reports to achieve interpretable clinical staging. Compared to the optimal baselines, KnowST improves AUC by 7.69% and achieves 90.52% accuracy on 573 real-world radiology reports, while also demonstrating superior stage identification and stable performance across various metrics.