@inproceedings{oai:repository.nii.ac.jp:02001328,
 author = {Yuta Nakamura and Shouhei Hanaoka and Shuntaro Yada and Shoko Wakamiya and Eiji Aramaki},
 month = {Dec},
 note = {This paper describes the Radiology Report TNM staging (RR-TNM) subtask as a part of NTCIR-17 Medical Natural Language Processing for Social Media and Clinical Texts (MedNLP-SC) shared task in 2023. This subtask focused on automated lung cancer staging based on radiology reports. We created a dataset of 243 Japanese radiology reports containing no personal health information. A total of three teams with 16 members participated and submitted seven solutions. The best accuracy scores for the T, N, and M categories reached 67%, 80%, and 93%, respectively. Through the RR- TNM subtask, we have provided a valuable open Japanese clinical corpus and useful insights to apply natural language processing for secondary usage of staging information.},
 publisher = {NII Institutional Repository},
 title = {NTCIR-17 MedNLP-SC Radiology Report Subtask Overview: Dataset and Solutions for Automated Lung Cancer Staging},
 year = {2023}
}