用于肿瘤免疫微环境再现和精确表征的AI就绪多重染色数据集。

Parmida Ghahremani, Joseph Marino, Juan Hernandez-Prera, Janis V de la Iglesia, Robbert Jc Slebos, Christine H Chung, Saad Nadeem
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引用次数: 0

摘要

我们介绍了一个新的人工智能计算病理学数据集,其中包含来自八名头颈部鳞状细胞癌患者的重新存储和共同注册的数字化图像。具体而言,相同的肿瘤切片首先用昂贵的多重免疫荧光(mIF)分析染色,然后用廉价的多重免疫组织化学(mIHC)重新染色。这是第一个公开的数据集,证明了这两种染色方法的等效性,这反过来又允许几个用例;由于等效性,我们更便宜的mIHC染色方案可以抵消对昂贵的mIF染色/扫描的需求,这需要高度熟练的实验室技术人员。与来自个体病理学家的主观和易出错的免疫细胞注释(分歧>50%)驱动SOTA深度学习方法相反,该数据集通过mIF/mIHC重新构建提供了客观的免疫和肿瘤细胞注释,以更可重复和准确地表征肿瘤免疫微环境(例如用于免疫治疗)。我们在三个用例中证明了该数据集的有效性:(1)通过风格转移对CD3/CD8肿瘤浸润淋巴细胞进行IHC定量,(2)将廉价的mIHC染色虚拟转化为更昂贵的mIF染色,以及(3)在标准苏木精图像上的虚拟肿瘤/免疫细胞表型。数据集位于https://github.com/nadeemlab/DeepLIIF.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment.

We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly-skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard hematoxylin images. The dataset is available at https://github.com/nadeemlab/DeepLIIF.

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