6 March 2018 Validation of multiplex immunohistochemistry assays using automated image analysis
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Abstract
Multiplex brightfield immunohistochemistry (IHC) offers the potential advantage to simultaneously analyze multiple biomarkers in order to, for example, determine T-cell numbers and phenotypes in a patient’s immune response to cancer. This paper presents a fully automatic image-analysis framework to utilize multiplex assays to identify and count stained cells of interest; it was validated by comparison with multiple “gold standard” 3,3'-Diaminobenzidine (DAB) singleplex assays. Both multiplex and singleplex assays were digitized using an RGB slide scanner. The proposed image-analysis algorithms consist of 1) a novel color-deconvolution method, 2) cell candidate detection, 3) feature extraction, and 4) cell classification based on supervised machine learning. Fully automated cell counts on the singleplex images were first rigorously verified by comparing to experts’ ground truth counts: A total of 72,076 for CD3-, 34,133 for CD8-, and 2,615 for FoxP3-positive T-cells were used in this singleplex algorithm validation. Concordance correlation coefficients (CCC) of the singleplex algorithm-to-observer agreements were 0.945, 0.965, and 0.997, respectively. Then, the singleplex slides were registered to the adjacent multiplex slides and the automated cell counts for each were compared. For this validation of the multiplex assay cell counts, the CCC values were 0.914, 0.943, and 0.877 for 12,828, 2,545, and 1,647 cells, respectively; we observed good slide-to-slide agreement between multiplex and singleplex. We conclude that the proposed fully-automated image analysis can be a useful and reliable tool to assess multiplex IHC assays.
Conference Presentation
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Auranuch Lorsakul, Auranuch Lorsakul, Joerg Bredno, Joerg Bredno, Robert L. Ochs, Robert L. Ochs, Larry Morrison, Larry Morrison, William Day, William Day, "Validation of multiplex immunohistochemistry assays using automated image analysis", Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810J (6 March 2018); doi: 10.1117/12.2293168; https://doi.org/10.1117/12.2293168
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