OpenTME提供5类癌症的海量癌变微环境定量分析数据，助力病理研究与算法开发。
OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
- 基于AI模型对3634张常规染色切片进行细胞级分析，生成高维定量指标
- 每张切片产出超4500个空间特征，涵盖组织质量控制、细胞识别与邻域关系
- 开源可共享，适合肿瘤微环境研究者和开发病理分析算法的团队使用
肿瘤微环境（TME）在癌症进展、治疗反应和患者预后中起核心作用，但来自常规苏木精-伊红（H&E）染色病理切片的大规模、一致且量化的TME表征仍十分稀缺。本文推出OpenTME，一个开放获取的数据集，包含来自癌症基因组图谱（TCGA）的5种癌症类型（膀胱癌、乳腺癌、结直肠癌、肝癌和肺癌）共3,634张全切片图像的预计算TME特征。所有结果均由基于Atlas病理基础模型家族的AI工具Atlas H&E-TME生成，该工具完成组织质量控制、组织分割、细胞检测与分类以及空间邻域分析，单张切片产生超过4,500个细胞级分辨率的定量读数。OpenTME可在Hugging Face上用于非商业学术研究，未来将持续扩展，预期将支持生物标志物发现、空间生物学研究及计算TME分析方法的发展。
原文摘要 · Abstract (English)
The tumor microenvironment (TME) plays a central role in cancer progression, treatment response, and patient outcomes, yet large-scale, consistent, and quantitative TME characterization from routine hematoxylin and eosin (H&E)-stained histopathology remains scarce. We introduce OpenTME, an open-access dataset of pre-computed TME profiles derived from 3,634 H&E-stained whole-slide images across five cancer types (bladder, breast, colorectal, liver, and lung cancer) from The Cancer Genome Atlas (TCGA). All outputs were generated using Atlas H&E-TME, an AI-powered application built on the Atlas family of pathology foundation models, which performs tissue quality control, tissue segmentation, cell detection and classification, and spatial neighborhood analysis, yielding over 4,500 quantitative readouts per slide at cell-level resolution. OpenTME is available for non-commercial academic research on Hugging Face. We will continue to expand OpenTME over time and anticipate it will serve as a resource for biomarker discovery, spatial biology research, and the development of computational methods for TME analysis.
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