Stochastic Geometry for Automatic Assessment of Ki-67 Index in Breast Cancer Preparations
Proliferative activity of cells is one of the most critical factors in breast cancer diagnosis. It is used to evaluate tumor cell progression and to predict treatment responses in chemotherapy. Ki-67 is a nuclear biomarker commonly used to measure cellular proliferation rate. The ratio between the number of Ki-67 positive tumor nuclei and all tumor nuclei defines Ki-67 index. However, manual cell counting is tedious and time consuming because hundreds of nuclei must be labeled. To speed up the analysis process, nuclei can be segmented automatically and then classified based on staining color. Unfortunately, segmentation of individual nuclei is a big challenge because they often create complex clusters comprised of many touching and overlapping nuclei. To deal with complexities and ambiguities of cytological material we propose a generative model which approximates nuclei using ellipses. We assume that the process of generating a cytological sample has stochastic nature. Therefore it is possible to reconstruct this process using marked point process tuned according to observed cytological sample. To verify the potential of the proposed method, we applied it to determine Ki-67 index in breast cancer immunochemistry samples. The results of experiments have shown that Ki-67 indices determined by proposed approach correlate well with those computed manually.
KeywordsBreast cancer Ki-67 index Nuclei segmentation Stochastic process Steepest ascent optimization Maximum a posteriori estimation
The research was supported by National Science Centre, Poland (2015/17/B/ST7/03704).
- 1.Abubakar, M., et al.: High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium. J. Pathol: Clin. Res. 2(3), 138–153 (2016)Google Scholar
- 3.Baddeley, A.J., van Lieshout, M.N.M.: Stochastic geometry models in high-level vision. In: Mardia, K.V., Kanji, G.K. (eds.) Advances in Applied Statistics, Statistics and Images, vol. 1, pp. 231–256. Carfax Publishing, Abingdon (1993)Google Scholar
- 10.Markowsky, P., Reith, S., Zuber, T.E., König, R., Rohr, K., Schnörr, C.: Segmentation of cell structures using model-based set covering with iterative reweighting. In: 2017 IEEE 14th International Symposium Biomedical Imaging (ISBI 2017), pp. 392–396, April 2017Google Scholar
- 12.Ruifrok, A.C., Johnston, D.A.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291–299 (2001)Google Scholar
- 15.Tuominen, V.J., Ruotoistenmäki, S., Viitanen, A., Jumppanen, M., Isola, J.: ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Res. 12(4), R56 (2010). https://doi.org/10.1186/bcr2615CrossRefGoogle Scholar
- 16.Vörös, A., Csörgő, E., Nyári, T., Cserni, G.: An intra- and interobserver reproducibility analysis of the Ki-67 proliferation marker assessment on core biopsies of breast cancer patients and its potential clinical implications. Pathobiology 80, 111–118 (2013). https://doi.org/10.1159/000343795CrossRefGoogle Scholar
- 18.Yeo, M.K., Kim, H.E., Kim, S.H., Chae, B.J., Song, B.J., Lee, A.: Clinical usefulness of the free web-based image analysis application ImmunoRatio for assessment of Ki-67 labelling index in breast cancer. J. Clin. Pathol. 70(8), 715–719 (2017). http://jcp.bmj.com/content/70/8/715CrossRefGoogle Scholar