Automated Quantification of Ki-67 on Gastric Epithelial Tissue based on Cell Nuclei Area Ratio
The objective was to develop an automated algorithm for the estimation of a protein (Ki-67) index based on cell nuclei area ratio of gastric epithelial tissue cells; for this purpose, digital histopathology images were used. An expert manually annotated each region of interest of the images. A proportion of Ki-67 positive and negative cells within that region was used to obtain the color distribution of the corresponding pixels. The histogram of each color distribution was modeled as a Gaussian and, later, thresholded for segmentation and classification. Finally, the Ki-67 index was estimated as the ratio between the segmented positive area of the nuclei divided by the total area of the positive and negative nuclei. The automated method has a strong correlation of 0.725 and a root mean square error of 0.293 when compared to the manual method, which gives certainty that the automated method can be used to analyze the proliferation rate. Furthermore, compared to manual classification, the presented method automatically classifies every image in the same Ki-67 category: low, intermediate, and high. Despite the small sample size, the utility of the presented method was demonstrated. However, the low number of scored images did not allow for thoroughly sampling the ranges of pixel values and intensities observed by pathologists, which will be addressed in future work.
Aperio Technologies, Inc. (2007). IHC Nuclear Image Analysis User’s Guide. https://tmalab.jhmi.edu/aperiou/userguides/IHC_Nuclear.pdf
Barricelli, Barbara Rita., Casiraghi, Elena., Gliozzo, Jessica., Huber, Veronica., Leone, Biagio Eugenio., Rizzi, Alessandro. & Vergani, Barbara. (2019). ki67 nuclei detection and ki67-indexestimation: a novel automatic approach based on human vision modeling. BCM Bioinformaticss, 20(733).
Bray, Freddie., Ferlay, Jacques., Soerjomataram, Isabelle., Siegel, Rebecca L., Torre, Lindsey A. & Jemal, Ahmedin. (2018). Global Cancer Statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. A Cancer Journal for Clinicians, 68(6). https://doi.org/10.3322/caac.21492
Correa, Pelayo. & Houghton, Jeanmarie. (2007). Carcinogenesis of Helicobacter pylori. Gastroentorology, 133(2), 659–672. https://doi.org/10.1053/j.gastro.2007.06.026
Duraiyan, Jeyapradha.; Govindarajan, Rajeshwar; Kaliyappan., Karunakaran. & Palanisamy, Murugesan. (2012). Applications of immunohistochemistry. Journal of Pharmacy and Bioallied Sciences, 4(2), 307–309. https://doi.org/10.4103/0975-7406.100281
Goldhirsch, A., Wood, W. C., Coates, A. S., Gelber, R. D., Thürlimann, B. & Senn, H.J. (2011). Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Annals of Oncology, 12(8), 1736–1747. https://doi.org/10.1093/annonc/mdr304
Li, Lian Tao., Jiang, Guan., Chen, Qian. & Zheng, Jun Nian. (2014). Ki67 is a promising molecular target in the diagnosis of cancer (Review). Molecular Medicine Reports, 11(3), 1566-1572. https://doi.org/10.3892/mmr.2014.2914
Shi,Peng,., Jing, Zhong., Jinsheng, Hong., Rongfang, Huang., Kaijun, Wang. & Yunbin, Chen. (2016). Automated Ki-67 Quantification of Immunohistochemical Staining Image of Human Nasopharyngeal Carcinoma Xenografts. Scientific Reports, 6(32127).
QuPath. (2021). Cell classification - QuPath 0.3.0 documentation. https://qupath.readthedocs.io/en/latest/docs/tutorials/cell_classification.html
Rawla, Prashanth. & Barsouk, Adam. (2019). Epidemiology of gastric cancer: global trends, risk factors and prevention. Gastroenterology Review, 14(1), 26-38, https://doi.org/10.5114/pg.2018.80001
Tuominen, Vilppu J., Ruotoistenmäki, Sanna., Viitanen, Arttu., Jumppanen, Mervi. & Isola, Jorma. (2010). ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Research, 12(R56).
Xing, Fuyong., Su, Hai., Neltner, Janna., & Yang, Lin. (2014). Automatic Ki-67 Counting Using Robust Cell Detection and Online Dictionary Learning. IEEE Transactions on Biomedical Engineering, 61(3), 859-870. https://doi.org/10.1109/TBME.2013.2291703
Yeo, Min-Kyung., Kim, Hee Eun., Kim, Sung Hun., Chae, Byung Joo., Song, Byung Joo. & Lee, Ahwon. (2017). Clinical usefulness of the free web-based image analysis application ImmunoRatio for assessment of Ki-67 labelling index in breast cancer. Journal of Clinical Pathology, 70(8), 715-719. http://dx.doi.org/10.1136/jclinpath-2016-204162
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