Radiology reports play a vital role in clinical workflows, serving as a primary means for radiologists to communicate imaging findings to physicians. However, the increasing number of imaging studies has made it challenging to produce and interpret comprehensive reports in a timely manner. Natural language processing (NLP) has shown potential to alleviate this burden, yet most existing studies are limited to English, while clinical reports are often written in local languages. To address this gap, we have developed and released Japanese medical text datasets through a series of shared tasks. Our recent efforts, including NTCIR-16 Real-MedNLP and NTCIR-17 RR-TNM, focused on automating lung cancer staging from radiology reports using the TNM classification system. This task is clinically significant, yet challenging due to the implicit nature of staging information and the complexity of TNM criteria. In this paper, we introduce the NTCIR-18 RadNLP 2024 shared task, which extends the previous task with finer-grained classification, a larger and bilingual corpus, and new sentence-level subtasks. We present the dataset, participating systems, and evaluation results, aiming to provide practical insights into building NLP systems for cancer staging support.
NAKAMURA et al. (Fri,) studied this question.