ホンダ　ゴロウ   HONDA Gorou   本田　五郎    所属   医学部 医学科（東京女子医科大学病院）    職種   教授・基幹分野長
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Preliminary assessment of TNM classification performance for pancreatic cancer in Japanese radiology reports using GPT-4.
掲載誌名	正式名：Japanese Journal of Radiology 略　 称：Jpn J Radiol ISSNコード：1867108X/18671071
掲載区分	国内
巻・号・頁	pp.e-e
著者・共著者	Suzuki Kazufumi, Yamada Hiroki, Yamazaki Hiroshi, Honda Goro, Sakai Shuji
発行年月	2024/08
概要	PURPOSE:A 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 METHODS:We 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.RESULTS:The 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.CONCLUSION:Although GPT is familiar with the TNM classification for pancreatic cancer, its performance in classifying actual cases in this experiment may not be adequate.
DOI	10.1007/s11604-024-01643-y
PMID	39162781