An AI-ready multiplex staining dataset for reproducible and accurate characterization of tumor immune microenvironment Conference Paper
| Authors: | Ghahremani, P.; Marino, J.; Hernandez-Prera, J.; de la Iglesia, J. V.; Slebos, R. J. C.; Chung, C. H.; Nadeem, S. |
| Title: | An AI-ready multiplex staining dataset for reproducible and accurate characterization of tumor immune microenvironment |
| Conference Title: | 26th International Conference of the Medical Image Computing and Computer Assisted Intervention (MICCAI 2023) |
| Abstract: | 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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. |
| Keywords: | immunohistochemistry; fluorescence; pathology; tumors; tumor microenvironment; immune cells; head-and-neck squamous cell carcinoma; microenvironments; carcinoma patients; tumor microenvironments; deep learning; digitized images; multiplex immunohistochemistry; multiplex immuofluorescence; virtual stain-to-stain translation |
| Journal Title | Lecture Notes in Computer Science |
| Volume: | 14225 |
| Conference Dates: | 2023 Oct 8-12 |
| Conference Location: | Vancouver, Canada |
| ISBN: | 0302-9743 |
| Publisher: | Springer |
| Date Published: | 2023-01-01 |
| Start Page: | 704 |
| End Page: | 713 |
| Language: | English |
| DOI: | 10.1007/978-3-031-43987-2_68 |
| PROVIDER: | scopus |
| PMCID: | PMC10571229 |
| PUBMED: | 37841230 |
| DOI/URL: | |
| Notes: | Conference paper -- Located in proceedings book, part VI (ISBN: 978-3-031-43986-5) -- Source: Scopus |
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