The NITKC team participated in the RadNLP Shared task of TNM classification from lung cancer radiology reports written in English, using an LLM-based approach. LLM accuracy varies depending on training methods and the number of parameters. We aimed to solve this task using open-source LLMs with fewer parameters than closed-source, proprietary LLMs and made improvements accordingly. Open-source LLMs have less prior knowledge than closed-source LLMs, putting them at a disadvantage for TNM classification. To address this, we used Graph-RAG to improve accuracy and address issues by representing domain knowledge for unfamiliar tasks as a graph and incorporating it as knowledge into the LLM. This method uses a graph database to represent domain knowledge for TNM classification in a graph structure. It dynamically incorporates the graph information into LLM prompts, compensating for the knowledge gaps in open-source LLMs and enabling more accurate inference. Additionally, to enhance performance, we trained BioBERT and MedBERT on a dataset labeled with lung cancer progression stages and utilized these inference results concurrently. As a result, we achieved a joint accuracy of 0.2963 in the TNM classification task. This demonstrates that our approach effectively mitigates the limitations of open-source LLMs in TNM classification.
Kondo et al. (Fri,) studied this question.