LKAN: LLM-Based Knowledge-Aware Attention Network for Clinical Staging of Liver Cancer

Clinical staging of liver cancer (CSoLC), an important indicator for evaluating primary liver cancer (PLC), is key in the diagnosis, treatment, and rehabilitation of liver cancer. In China, the current CSoLC adopts the China liver cancer (CNLC) staging, which is usually evaluated by clinicians based on radiology reports. Therefore, inferring clinical information from unstructured radiology reports can provide auxiliary decision support for clinicians. The key to solving the challenging task is to guide the model to pay attention to the staging-related words or sentences, and the following issues may occur: 1) Imbalanced categories: Early- and mid-stage liver cancer symptoms are subtle, resulting in more data in the end-stage. 2) Domain sensitivity of liver cancer data: The liver cancer dataset contains substantial domain knowledge, leading to out-of-vocabulary issues and reduced classification accuracy. 3) Free-text and lengthy report: Radiology reports sparsely describe various lesions using domain-specific terms, making it hard to mine staging-related information. To address these, this article proposes a large language model (LLM)-based Knowledge-aware Attention Network (LKAN) for CSoLC. First, for maintaining semantic consistency, LLM and a rule-based algorithm are integrated to generate more diverse and reasonable data. Second, an unlabeled radiology corpus is pre-trained to introduce domain knowledge for subsequent representation learning. Third, attention is improved by incorporating both global and local features to guide the model's focus on staging-relevant information. Compared with the baseline models, LKAN has achieved the best results with 90.3% Accuracy, 90.0% Macro_F1 score, and 90.0% Macro_Recall.