An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment (2305.16465v1)

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 \url{https://github.com/nadeemlab/DeepLIIF}.

• The paper introduces an innovative multiplex staining dataset that validates both mIF and mIHC methods for tumor immune microenvironment analysis.
• It outlines a novel brightfield mIHC protocol using AEC chromogen, achieving signal clarity similar to mIF and enhancing reproducibility.
• Comparative studies using metrics like RMSE and SSIM demonstrate high concordance, supporting AI-driven precision in immunotherapy research.

AI-Ready Multiplex Staining Dataset for Tumor Immune Microenvironment Characterization

The paper introduces an innovative multiplex staining dataset designed for the AI-driven characterization of tumor immune microenvironments (TIME), specifically focusing on head-and-neck squamous cell carcinoma cases. Leveraging both multiplex immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) staining techniques, this dataset addresses the necessity for reproducible and cost-effective analysis tools necessary for precision cancer therapy.

Dataset Composition and Methodology

The dataset consists of restained and co-registered image samples derived from eight patients. Notably, identical tumor sections were processed using both mIF and mIHC staining techniques. While mIF is renowned for precision, its cost and operational complexity render it less accessible compared to mIHC. The dataset's significance lies in its capacity to validate the equivalency between these staining methods, thus bolstering the adoption of mIHC for broad clinical and research applications.

The paper describes a novel brightfield mIHC staining protocol utilizing aminoethyl carbazole (AEC) chromogen, facilitating repeated weeding and staining of the same tissue section. This approach ensures clearer signal profiles similar to those obtained via mIF, making it a viable alternative in contexts requiring detailed immune and tumor cell marker characterizations.

Implications and Use Cases

The paper underscores three critical applications of the dataset. Firstly, it facilitates the quantification of CD3/CD8 tumor-infiltrating lymphocytes through style transfer techniques. Secondly, it supports the virtual translation of affordable mIHC stains to more comprehensive mIF stains. Lastly, standard hematoxylin images can be virtually phenotyped to ascertain immune and tumor cell characteristics.

These use cases have profound implications in both clinical and research settings. The dataset offers an objective foundation to overcome subjective variability in pathologist evaluations. Moreover, it assists in refining deep learning models for TIME assessment, which is pivotal for advancing immunotherapy protocols.

Experimental Evaluation and Concordance Study

The research highlights a comparative paper between mIF and mIHC staining techniques across numerous quantitative metrics, including RMSE and SSIM. High concordance between methods affirms the dataset's reliability as a standard for future modalities in tumor marker assessment. The use of sophisticated machine learning models, such as Adaptive Attention Normalization (AdaAttN) and DeepLIIF, demonstrates the dataset's potential in enhancing computational pathology tools.

Future Directions

The paper acknowledges potential expansions into datasets covering more cancer types, further reducing variability and uncertainty in immunophenotyping. The release of comprehensive cell segmentations is anticipated to provide an even more robust computational foundation, fostering new AI applications in pathology.

In sum, the introduction of this multiplex staining dataset marks a significant development in AI-amplified pathology, offering cost-effectiveness and improving reproducibility. While the methods and results are compelling, further validation across diverse histological frameworks and continued algorithmic development will be crucial in fully realizing the dataset's potential in clinical practice.