ArticleJapanese journal of radiology2025
Preliminary assessment of TNM classification performance for pancreatic cancer in Japanese radiology reports using GPT-4.
Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Multi-reader, multi-model benchmark of large language models for modified Outerbridge cartilage grading from knee MRI reports.Skeletal radiology · 2026Article
- Can chatGPT-4o reliably standardize PSMA PET/CT and PET/MRI reports using PROMISE V2 criteria? - An exploratory study.EJNMMI research · 2026Article
- Automated PROMISE V2 Scoring from PSMA PET/CT Reports Using Large Language Models: A Comparative Evaluation of Prompt Design and Model Performance.Current oncology (Toronto, Ont.) · 2026Article
- Enhancing pancreatic cancer staging with large language models: the role of retrieval-augmented generation.Radiological physics and technology · 2026Article
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- Estimation of histopathological types from breast MRI findings using a large language model.International journal of computer assisted radiology and surgery · 2026Article
- [Research progress of large language models in tumor diagnosis: applications in textual reports and medical imaging].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.JMIR medical informatics · 2025Article
- Artificial intelligence for immunotherapy response assessment in lung cancer using PET/CT reports.Japanese journal of radiology · 2025Article
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- Evaluation of radiology residents' reporting skills using large language models: an observational study.Japanese journal of radiology · 2025Observational
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- The critical need for an open medical imaging database in Japan: implications for global health and AI development.Japanese journal of radiology · 2025Article
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5 authors.
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Abstract
purposeA large-scale language model is expected to have been trained with a large volume of data including cancer treatment protocols. The current study aimed to investigate the use of generative pretrained transformer 4 (GPT-4) for identifying the TNM classification of pancreatic cancers from existing radiology reports written in Japanese. MATERIALS AND
methodsWe screened 100 consecutive radiology reports on computed tomography scan for pancreatic cancer from April 2020 to June 2022. GPT-4 was requested to classify the TNM from the radiology reports based on the General Rules for the Study of Pancreatic Cancer 7th Edition. The accuracy and kappa coefficient of the TNM classifications by GPT-4 was evaluated with the classifications by two experienced abdominal radiologists as gold standard.
resultsThe accuracy values of the T, N, and M factors were 0.73, 0.91, and 0.93, respectively. The kappa coefficients were 0.45 for T, 0.79 for N, and 0.83 for M.
conclusionAlthough GPT is familiar with the TNM classification for pancreatic cancer, its performance in classifying actual cases in this experiment may not be adequate.
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