ArticleCancers2024
Exploring Multilingual Large Language Models for Enhanced TNM Classification of Radiology Report in Lung Cancer Staging.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- LLM-powered prostate cancer staging from PSMA-PET/CT reports using PROMISE v2.European journal of nuclear medicine and molecular imaging · 2026Article
- Automated classification of clinical T staging pulmonary oncology from chest CT reports: a natural language processing framework for real-world data utilization.BMC medical informatics and decision making · 2026Article
- Identification of high-priority radiology reports with unexpected findings using fine-tuned large language models.European radiology · 2026Article
- Article
- Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual Evaluation.Diagnostics (Basel, Switzerland) · 2026Article
- Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions.Frontiers in oncology · 2026Review
- Article
- Automated Esophageal Cancer Staging From Free-Text Radiology Reports: Large Language Model Evaluation Study.JMIR medical informatics · 2025Article
- Article
- A Review of the Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead.AJNR. American journal of neuroradiology · 2025Review
- Conversion of Mixed-Language Free-Text CT Reports of Pancreatic Cancer to National Comprehensive Cancer Network Structured Reporting Templates by Using GPT-4.Korean journal of radiology · 2025Article
- Application of NotebookLM, a large language model with retrieval-augmented generation, for lung cancer staging.Japanese journal of radiology · 2025Article
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5 authors.
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Abstract
BACKGROUND/
objectivesThis study aimed to investigate the accuracy of Tumor, Node, Metastasis (TNM) classification based on radiology reports using GPT3.5-turbo (GPT3.5) and the utility of multilingual large language models (LLMs) in both Japanese and English.
methodsUtilizing GPT3.5, we developed a system to automatically generate TNM classifications from chest computed tomography 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.
resultsThe highest accuracy was attained with full TNM definitions and radiology reports in English (M = 94%, N = 80%, T = 47%, and TNM combined = 36%). Providing definitions for each of the T, N, and M factors statistically improved their respective accuracies (T: odds ratio [OR] = 2.35,
conclusionsThis 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.
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