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.
@article{arxiv.2507.16855,
title = {A tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer},
author = {Joey Spronck and Leander van Eekelen and Dominique van Midden and Joep Bogaerts and Leslie Tessier and Valerie Dechering and Muradije Demirel-Andishmand and Gabriel Silva de Souza and Roland Nemeth and Enrico Munari and Giuseppe Bogina and Ilaria Girolami and Albino Eccher and Balazs Acs and Ceren Boyaci and Natalie Klubickova and Monika Looijen-Salamon and Shoko Vos and Francesco Ciompi},
journal= {arXiv preprint arXiv:2507.16855},
year = {2025}
}
Comments
Our dataset is available at 'https://zenodo.org/records/15674785' and our code is available at 'https://github.com/DIAGNijmegen/ignite-data-toolkit'