README.md

---
license: cc-by-4.0
task_categories:
- text-classification
language:
- ja
- en
tags:
- medical
size_categories:
- n<1K
---

# RadNLP 2024 main task: Document Classification for Lung Cancer Staging

<p align="center">
  <a href="https://sociocom.naist.jp/radnlp-2024/">
    <img src="https://sociocom.naist.jp/radnlp-2024/wp-content/uploads/sites/11/2024/06/radnlp_2024_top_img_small.png" alt="RadNLP 2024 logo">
  </a>
</p>
<p align="center">
<a href="https://research.nii.ac.jp/ntcir/workshop/OnlineProceedings18/pdf/ntcir/01-NTCIR18-OV-RADNLP-NakamuraY.pdf">📜 Paper</a>
</p>


## 📚 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.

<img src="https://sociocom.naist.jp/radnlp-2024/wp-content/uploads/sites/11/2024/06/radnlp_2024_overview_en_96dpi.png" alt="RadNLP objective">

Main task is a multi-label document classification to correctly determine T, N, and M categories for each radiology report.

- T category - Assessment of the size and/or extension of the primary lesion. Available choices: T0, Tis, T1mi, T1a, T1b, T1c, T2a, T2b, T3, T4
- N category - Assessment of the extent of lymph node metastasis. Available choices: N0, N1, N2, N3
- M category - Assessment of the extent of distant metastasis. Available choices: M0, M1a, M1b, M1c

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 |
|--------|------|-------------|
| English | Training | 108 |
| English | Validation | 54 |
| English | Test | 81 |
| English | Total | 243 |
| Japanese | Training | 108 |
| Japanese | Validation | 54 |
| Japanese | Test | 216 |
| Japanese | Total | 378 |

Each file contains 5 columns:

| Column | Type | Description |
|--------|------|-------------|
| `id` | int | Unique identifier |
| `text` | string | Radiology Report |
| `t` | string | Gold standard for the T category |
| `n` | string | Gold standard for the N category |
| `m` | string | Gold standard for the M category |


## 📖 Citation

If you find our work useful, please consider citing:

```bibtex
@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.