An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune...

Area of Science:

• Computational pathology
• Artificial intelligence in pathology
• Digital pathology

Background:

• Multiplex immunofluorescence (mIF) is expensive and requires specialized expertise.
• Manual annotation of tumor immune microenvironments is subjective and prone to errors.
• There is a need for cost-effective and reproducible methods for analyzing tumor pathology.

Purpose of the Study:

• To introduce a novel AI-ready computational pathology dataset.
• To demonstrate the equivalence of multiplex immunohistochemistry (mIHC) and multiplex immunofluorescence (mIF) staining methods.
• To enable accurate and reproducible characterization of the tumor immune microenvironment.

Main Methods:

• Created a dataset of restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients.
• Applied both mIF and mIHC staining to the same tumor sections.
• Developed AI-driven use cases including style transfer and virtual staining.

Main Results:

• Established the equivalence of mIHC and mIF staining for head-and-neck squamous cell carcinoma.
• Showcased cost-effective mIHC as a viable alternative to expensive mIF.
• Demonstrated AI applications for immune cell quantification and phenotyping.

Conclusions:

• The developed dataset facilitates reproducible and accurate computational pathology analysis.
• The equivalence of mIHC and mIF reduces costs and reliance on specialized labs.
• This resource advances AI-driven research in tumor immune microenvironment characterization and immunotherapy.