{"id":3014,"date":"2023-08-31T18:52:39","date_gmt":"2023-08-31T18:52:39","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/hnscc-mif-mihc-comparison\/"},"modified":"2023-09-13T11:55:53","modified_gmt":"2023-09-13T11:55:53","slug":"hnscc-mif-mihc-comparison","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/hnscc-mif-mihc-comparison\/","title":{"rendered":"HNSCC-MIF-MIHC-COMPARISON"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Head and Neck Cancer"],"citations":[2941,8877,2925],"collection_doi":"10.7937\/TCIA.2020.T90F-WB82","collection_download_info":"Click the Versions tab for more info about data releases.\nPlease contact help@cancerimagingarchive.net<\/a>\u00a0 with any questions regarding usage.","collection_downloads":[3143],"full_export":"

Summary<\/h1>

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 code for stain translation is available at<\/span>\u00a0<\/span>https:\/\/github.com\/nadeemlab\/DeepLIIF<\/span><\/a>\u00a0<\/span>and the code for performing interactive deep learning whole-cell\/nuclear segmentation is available at<\/span>\u00a0<\/span>https:\/\/github.com\/nadeemlab\/impartial<\/span><\/a>. After scanning the full images, nine regions of interest (ROIs) from each slide\/Case<\/strong> were chosen by an experienced pathologist on both mIF and mIHC images: three in the tumor core (T<\/strong>), three at the tumor margin (M<\/strong>),and three outside in the adjacent stroma (S<\/strong>) area. These individual ROIs were further subdivided into four 512x512 patches with indices [0_0], [0_1], [1_0], [1_1]. The final notation for each file is Case[patient_id]_[T\/M\/S][1\/2\/3]_[ROI_index]_[Marker_name]. More details can be found in the paper.<\/span><\/p>

Acknowledgments<\/span><\/h3>

This work was supported by MSK Cancer Center Support Grant\/Core Grant (P30 CA008748) and by James and Esther King Biomedical Research Grant (7JK02) and Moffitt Merit Society Award to C. H. Chung. It is also supported in part by the Moffitt\u2019s Total Cancer Care Initiative, Collaborative Data Services, Biostatistics and Bioinformatics, and Tissue Core Facilities at the H. Lee Moffitt Cancer Center and Research Institute, an NCI-designated Comprehensive Cancer Center (P30-CA076292).<\/span>\u00a0<\/span><\/p>

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