LLM-Based Two-stage Reasoning for TNM Staging: NECMedDX at the NTCIR-18 RadNLP Task

Abstract

We propose a novel method for automatically inferring TNM stages from radiology reports. The proposed method includes a two-stage reasoning process. In Stage 1, kNN few-shot learning with the Chain of Thought is used for initial inference, followed by a self-review to evaluate the reasoning process. In Stage 2, if the inference results after the self-review are inconsistent, a second review is conducted from an alternative perspective. The proposed method achieved superior results in the NTCIR-18 RadNLP 2024 Main Task (Japanese), outperforming other teams by approximately 7.4 points, thereby winning the competition. The proposed method is designed as an extension of prompt engineering. It requires no complex training, which makes it applicable to various large language models.

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