# Exploring Multilingual Large Language Models for Enhanced TNM classification of Radiology Report in lung cancer staging

arXiv: https://arxiv.org/abs/2406.06591 · HTML: https://mlsanity.com/paper/2406.06591/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Finding Universal Grammatical Relations in Multilingual BERT](https://mlsanity.com/paper/2005.04511/) (2020)

## Built on by (most cited first, 4 of 4)

- [Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection](https://mlsanity.com/paper/2504.13186/) (2025)
- [Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging](https://mlsanity.com/paper/2410.10869/) (2024)
- [Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English Language](https://mlsanity.com/paper/2409.20147/) (2024)
- [SIFTING: A Novel LLM-Based Framework for Structured and Transparent Information Extraction from Clinical Free-Text Reports, with Application to Tumor Staging in Lung Cancer](https://mlsanity.com/paper/2609.07185/) (2026)

## Abstract (arXiv)

Background: Structured radiology reports remains underdeveloped due to labor-intensive structuring and narrative-style reporting. Deep learning, particularly large language models (LLMs) like GPT-3.5, offers promise in automating the structuring of radiology reports in natural languages. However, although it has been reported that LLMs are less effective in languages other than English, their radiological performance has not been extensively studied. Purpose: This study aimed to investigate the accuracy of TNM classification based on radiology reports using GPT3.5-turbo (GPT3.5) and the utility of multilingual LLMs in both Japanese and English. Material and Methods: Utilizing GPT3.5, we developed a system to automatically generate TNM classifications from chest CT reports for lung cancer and evaluate its performance. We statistically analyzed the impact of providing full or partial TNM definitions in both languages using a Generalized Linear Mixed Model. Results: Highest accuracy was attained with full TNM definitions and radiology reports in English (M = 94%, N = 80%, T = 47%, and ALL = 36%). Providing definitions for each of the T, N, and M factors statistically improved their respective accuracies (T: odds ratio (OR) = 2.35, p < 0.001; N: OR = 1.94, p < 0.01; M: OR = 2.50, p < 0.001). Japanese reports exhibited decreased N and M accuracies (N accuracy: OR = 0.74 and M accuracy: OR = 0.21). Conclusion: This study underscores the potential of multilingual LLMs for automatic TNM classification in radiology reports. Even without additional model training, performance improvements were evident with the provided TNM definitions, indicating LLMs' relevance in radiology contexts.