首个公开的非小细胞肺癌多中心组织与细胞级标注数据集，支持免疫治疗研究。
A tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer
- 构建包含H&E和PD-L1染色的多中心全切片图像数据集
- 涵盖155例患者、887个标注区域，覆盖原发与转移病灶
- 提供组织分割、核检测与PD-L1阳性细胞识别三类标注，助力精准医疗
非小细胞肺癌（NSCLC）肿瘤免疫微环境（TIME）的组织病理学特征具有预测免疫治疗响应的潜力。通过计算方法量化这些特征，如细胞检测与组织分割，可支持生物标志物开发。然而，现有用于训练细胞检测或组织分割算法的NSCLC数字病理数据集在范围上有限，缺乏临床常见转移部位的标注，且缺少如PD-L1免疫组化（IHC）等分子信息。为填补这一空白，我们推出IGNITE数据工具包，一个多染色、多中心、多扫描仪的标注型NSCLC全切片图像数据集。我们公开发布来自155名独立患者的887个完全标注的兴趣区域，涵盖三个互补任务：(i) H&E染色切片中16类组织区间的多类别语义分割，包括原发与转移性NSCLC；(ii) 细胞核检测；(iii) PD-L1 IHC切片中PD-L1阳性肿瘤细胞检测。据我们所知，这是首个公开的、包含转移部位H&E标注与PD-L1 IHC标注的NSCLC数据集。
原文摘要 · Abstract (English)
The tumor immune microenvironment (TIME) in non-small cell lung cancer (NSCLC) histopathology contains morphological and molecular characteristics predictive of immunotherapy response. Computational quantification of TIME characteristics, such as cell detection and tissue segmentation, can support biomarker development. However, currently available digital pathology datasets of NSCLC for the development of cell detection or tissue segmentation algorithms are limited in scope, lack annotations of clinically prevalent metastatic sites, and forgo molecular information such as PD-L1 immunohistochemistry (IHC). To fill this gap, we introduce the IGNITE data toolkit, a multi-stain, multi-centric, and multi-scanner dataset of annotated NSCLC whole-slide images. We publicly release 887 fully annotated regions of interest from 155 unique patients across three complementary tasks: (i) multi-class semantic segmentation of tissue compartments in H&E-stained slides, with 16 classes spanning primary and metastatic NSCLC, (ii) nuclei detection, and (iii) PD-L1 positive tumor cell detection in PD-L1 IHC slides. To the best of our knowledge, this is the first public NSCLC dataset with manual annotations of H&E in metastatic sites and PD-L1 IHC.
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