Datasets:
license: cc-by-4.0
task_categories:
- text-classification
language:
- ja
- en
tags:
- medical
size_categories:
- n<1K
RadNLP 2024 sub task: Document Segmentation for Lung Cancer Staging
📚 Introduction
RadNLP 2024 is a shared task in the international conference NTCIR-18, organized by the National Institute of Informatics in Japan.
Management of lung cancer is based on the stage, and radiology reports provide various related information by describing medical images such as CT and MRI.
However, radiology reports do not always specify the stage explicitly¹. This imposes extra workload on human experts for careful manual information extraction, which can be aided by automation.
Sub task is a document segmentation to identify up to eight spans related to the following topics:
(i) Omittable – Spans that are clearly free of any positive findings or clearly unrelated to lung cancer staging. In this definition, “clearly” means the high clarity that does not require detailed knowledge of the lung cancer staging criteria.
(ii) Measure – Span describing mainly the existence and diameter of the primary lesion.
(iii) Extension – Span describing the range of the primary lesion’s extension outside the lung parenchyma.
(iv) Atelectasis – Span pointing out atelectasis or obstructive pneumonia.
(v) Satellite – Span pointing out intrapulmonary metastasis or lymphangiomatosis carcinomatosa.
(vi) Lymphadenopathy – Span pointing out enlarged regional lymph nodes.
(vii) Pleural – Span pointing out pleural/pericardial effusion/dissemination.
(viii) Distant – Span pointing out distant metastasis outside the lung parenchyma.
In NLP terminology, this sub task is a multi-label sentence binary classification.
The segmentation is at the sentence level, and it is possible for a topic span to be discontinuous or for the same sentence to belong to more than one topic.
Participants are requested to determine whether each sentence falls into categories (i) to (viii). If it does, mark it as “1,” and if it does not, mark it as “0.” Therefore, each sentence requires eight binary answers.
Note that every sentence will either fall into only category (i) Omittable, or into one or more of categories (ii) Measure to (viii) Distant.
In RadNLP 2024, we follow the staging criteria by the Japan Lung Cancer Society (JLCS). Note that the JLCS criteria closely aligns the global standard, namely the 8th edition of the TNM Classification of Malignant Tumours by the Union for International Cancer Control (UICC).
All radiology reports in RadNLP 2024 diagnose lung cancer at the initial evaluation. There are no reports of lung cancer post-treatment evaluation or different cancer.
Our datasets contain NO personal health information. The radiology reports are not derived from real medical institutions but are created with crowdsourcing by diagnosing de-identified images on Radiopaedia. Task participants requires no complex applications to use our datasets.
📊 Dataset Size & Format
This repository contains 6 files:
| Language | Split | Number of radiology reports | Number of sentences |
|---|---|---|---|
| English | Training | 108 | 918 |
| English | Validation | 54 | 415 |
| English | Test | 81 | 568 |
| English | Total | 243 | 1,901 |
| Japanese | Training | 108 | 1,020 |
| Japanese | Validation | 54 | 451 |
| Japanese | Test | 216 | 1,976 |
| Japanese | Total | 378 | 3,447 |
Each file contains 5 columns:
| Column | Type | Description |
|---|---|---|
id |
int | Unique identifier |
sentence_index |
int | Sentence index |
text |
string | Radiology Report |
omittable |
int | Gold standard for the "omittable" category |
measure |
int | Gold standard for the "measure" category |
extension |
int | Gold standard for the "extension" category |
atelectasis |
int | Gold standard for the "atelectasis" category |
satellite |
int | Gold standard for the "satellite" category |
lymphadenopathy |
int | Gold standard for the "lymphadenopathy" category |
pleural |
int | Gold standard for the "pleural" category |
distant |
int | Gold standard for the "distant" category |
📖 Citation
If you find our work useful, please consider citing:
@inproceedings{radnlp2024,
title={NTCIR-18 RadNLP 2024 Overview: Dataset and Solutions for Automated Lung Cancer Staging},
author={Nakamura, Yuta and Fujimoto, Koji and Kluckert, Jonas and Krauthammer, Michael and Kanzawa, Jun and Katayama, Akira and Kikuchi, Tomohiro and Kurokawa, Ryo and Gonoi, Wataru and Tashiro, Yuki and Hanaoka, Shouhei and Yada, Shuntaro and Aramaki, Eiji},
year={2025},
booktitle={NTCIR-18: Proceedings of the 18th NTCIR Conference on Evaluation ofInformation Access Technologies},
doi={10.20736/0002002061}
}
📜 License
This project is licensed under the CC-BY-4.0 license.