What question did this study set out to answer?

To classify cancer TNM stages using a language model, enhancing accuracy through data augmentation.

April 1, 2026Open Access

Classification of cancer TNM stage from Japanese radiology report using on-premise LLM at NTCIR-17 MedNLP-SC RR-TNM subtask

Key Points

To classify cancer TNM stages using a language model, enhancing accuracy through data augmentation.
Created extensive question-and-answer pairs for TNM classification
Utilized an on-premise language model for analysis
Participated in the NTCIR-17 MedNLP-SC shared task
Observed improved accuracy in TNM classification
Significant improvement noted particularly for the M stage

Abstract

In this manuscript, we describe our submission to the RR-TNM subtask of NTCIR-17 MedNLP-SC shared task. We took an approach to create extensive question-and-answer (Q&A) pairs related to TNM classification as a method of domain-specific augmentation. Compared to the result without data augmentation, improvement in the accuracy especially for the M stage was observed.

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