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Quantitative image analysis of immunohistochemical stains using a CMYK color model
Open accessAbstract
Background: Computer image analysis techniques have decreased effects of observer biases, and increased the sensitivity and the throughput of immunohistochemistry (IHC) as a tissue-based procedure for the evaluation of diseases. Methods: We adapted a Cyan/Magenta/Yellow/Key (CMYK) model for automated computer image analysis to quantify IHC stains in hematoxylin counterstained histological sections. Results: The spectral characteristics of the chromogens AEC, DAB and NovaRed as well as the counterstain hematoxylin were first determined using CMYK, Red/Green/Blue (RGB), normalized RGB and Hue/Saturation/Lightness (HSL) color models. The contrast of chromogen intensities on a 0–255 scale (24-bit image file) as well as compared to the hematoxylin counterstain was greatest using the Yellow channel of a CMYK color model, suggesting an improved sensitivity for IHC evaluation compared to other color models. An increase in activated STAT3 levels due to growth factor stimulation, quantified using the Yellow channel image analysis was associated with an increase detected by Western blotting. Two clinical image data sets were used to compare the Yellow channel automated method with observer-dependent methods. First, a quantification of DAB-labeled carbonic anhydrase IX hypoxia marker in 414 sections obtained from 138 biopsies of cervical carcinoma showed strong association between Yellow channel and positive color selection results. Second, a linear relationship was also demonstrated between Yellow intensity and visual scoring for NovaRed-labeled epidermal growth factor receptor in 256 non-small cell lung cancer biopsies. Conclusion: The Yellow channel image analysis method based on a CMYK color model is independent of observer biases for threshold and positive color selection, applicable to different chromogens, tolerant of hematoxylin, sensitive to small changes in IHC intensity and is applicable to simple automation procedures. These characteristics are advantageous for both basic as well as clinical research in an unbiased, reproducible and high throughput evaluation of IHC intensity.
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Varghese, F., Bukhari, A. B., Malhotra, R., & De, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096801 · Cited by 1,318
Tuominen, V. J., Ruotoistenmäki, S., Viitanen, A., Jumppanen, M., & Isola, J. (2010) · Breast Cancer Research · doi:10.1186/bcr2615 · Cited by 472
Liudahl, S. M., Betts, C. B., Sivagnanam, S., Morales-Oyarvide, V., da Silva, A., Yuan, C., Hwang, S., Grossblatt-Wait, A., Leis, K. R., Larson, W., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0841 · Cited by 164
Metadata
- Authors
- Pham, N.-A., Morrison, A., Schwock, J., Aviel-Ronen, S., Iakovlev, V., Tsao, M.-S., Ho, J., & Hedley, D. W.
- Venue
- Diagnostic Pathology
- Published
- 2007-02-27
- Volume
- 2
- Issue
- 1
- Pages
- 8-8
- ISSN
- 1746-1596 (ISSN-L)
- OpenAlex
- W2139479159
- Cited by
- 179
- References
- 0
- FWCI
- 11.03
- Type
- journal article
- Language
- en
- Open access
- gold
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- Pham, N.-A., Morrison, A., Schwock, J., Aviel-Ronen, S., Iakovlev, V., Tsao, M.-S., Ho, J., & Hedley, D. W. (2007). Quantitative image analysis of immunohistochemical stains using a CMYK color model. Diagnostic Pathology. https://doi.org/10.1186/1746-1596-2-8
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