arXiv 2507.168552025-07-21q-bio.QMcs.CVeess.IV

非小细胞肺癌中一个组织和细胞级标注的 H&E 与 PD-L1 组织病理学图像数据集

A tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer

Radboud University Medical Center（拉德堡德大学医学中心）；University of Brescia（布雷西亚大学）；Ospedale Sacro Cuore（圣十字医院）；Provincial Hospital of Bolzano (SABES-ASDAA)（博尔扎诺省医院）；University and Hospital Trust of Verona（威尼斯大学与医院信托）；Karolinska University Hospital（卡罗林斯卡大学医院）；Biopticka Laboratory, Ltd（Biopticka实验室）

Joey Spronck, Leander van Eekelen, Dominique van Midden, Joep Bogaerts, Leslie Tessier, Valerie Dechering, Muradije Demirel-Andishmand, Gabriel Silva de Souza, … 展开作者

Joey Spronck, Leander van Eekelen, Dominique van Midden, Joep Bogaerts, Leslie Tessier, Valerie Dechering, Muradije Demirel-Andishmand, Gabriel Silva de Souza, Roland Nemeth, Enrico Munari, Giuseppe Bogina, Ilaria Girolami, Albino Eccher, Balazs Acs, Ceren Boyaci, Natalie Klubickova, Monika Looijen-Salamon, Shoko Vos, Francesco Ciompi

备注：: Our dataset is available at 'https://zenodo.org/records/15674785' and our code is available at 'https://github.com/DIAGNijmegen/ignite-data-toolkit'

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AI总结：

本文发布 IGNITE 多中心多染色 NSCLC 全切片数据集，提供 155 名患者、887 个标注区域，支持组织分割、细胞核检测和 PD-L1 阳性肿瘤细胞检测。

摘要：

非小细胞肺癌（NSCLC）组织病理学中的肿瘤免疫微环境（TIME）包含可预测免疫治疗反应的形态学和分子特征。对 TIME 特征进行计算量化，例如细胞检测和组织分割，可支持生物标志物开发。然而，目前可用于开发细胞检测或组织分割算法的 NSCLC 数字病理学数据集范围有限，缺少临床常见转移部位的标注，并且放弃了诸如 PD-L1 免疫组织化学（IHC）之类的分子信息。为填补这一空白，我们推出 IGNITE 数据工具包，这是一个多染色、多中心和多扫描仪的已标注 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 个完整标注感兴趣区域，涵盖三项互补任务：（i）H&E 染色切片中组织区室的多类别语义分割，包含跨越原发性和转移性 NSCLC 的 16 个类别；（ii）细胞核检测；（iii）PD-L1 IHC 切片中的 PD-L1 阳性肿瘤细胞检测。据我们所知，这是首个在转移部位 H&E 和 PD-L1 IHC 方面具有人工标注的公开 NSCLC 数据集。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。