Overview of automated lung cancer staging in Japanese radiology reports, highlighting dataset and findings.
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.
No takes yet. Share an insight, caveat, or question.
NAKAMURA et al. (2023) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: