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An Efficient Methodology to Assess Ki-67 and Tumor-Infiltrating Lymphocytes in Heterogeneous Tumors Detection

Năm XB 2024 Tạp chí / Hội thảo Lecture Notes in Networks and Systems Volume 1205 LNNS Đơn vị CNTT DOI / Link https://doi.org/10.1007/978-3-031-80943-9_12 ↗

Tác giả

Tóm tắt

Protein Ki67 is a protein that serves as a marker for cellular proliferation in cancer research and molecular biology. To determine the expression of Ki67 protein, the Immunohistochemistry (IHC) is used as the most popular technique, in which the determination...

Tài liệu tham khảo

[1] Fulawka, L., et al.: Ki-67 evaluation in breast cancer. The daily diagnostic practice. Indian J. Pathol. Microbiol. 60(2), 177–184 (2017)

[2] Inwald, E.C., Klinkhammer-Schalke, M., Hofstaedter, F., et al.: Ki-67 is a prognostic parameter in breast cancer patients: results of a large population-based cohort of a cancer registry. Breast Cancer Res. Treat. 139, 539–552 (2013)

[3] Nielsen, T.O., Leung, S.C.Y., Rimm, D.L., et al.: Assessment of Ki67 in breast cancer: updated recommendations from the international Ki67 in breast cancer working group. JNCI: J. Nat. Cancer Instit. 113(7), 808–819 (2020). https://doi.org/10.1093/jnci/djaa201

[4] Kammerer-Jacquet, S.F., Ahmad, A., ller, H., et al,: Ki-67 is an independent predictor of prostate cancer death in routin (2019)

[5] Wei, D.M., Chen, W.J., Meng, R.M., et al.: Augmented expression of Ki-67 is correlated with clinicopathological characteristics and prognosis for lung cancer patients: an up-dated systematic review and meta-analysis with 108 studies and 14,732 patients. Respir. Res. 19(1), 150 (2018)

[6] Mungle, T., Tewary, S., Arun, I., et al.: Automated characterization and counting of Ki-67 protein for breast cancer prognosis: a quantitative immunohistochemistry approach. Comput. Methods Programs Biomed. 139, 149–161 (2017). https://doi.org/10.1016/j.cmpb.2016.11.002

[7] Xie, W., Noble, J.A., Zisserman, A.: Microscopy cell counting and detection with fully convolutional regression networks. Comput. Methods Biomech. Biomed. Eng. Imaging 6(3), 283–292 (2018). https://doi.org/10.1080/21681163.2016.1149104

[8] Negahbani, F., Sabzi, R., Jahromi, B.P., et al.: PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer. Sci. Rep. 11(1), 8489 (2021)

[9] Xing, F., Su, H., Neltner, J.H., Yang, L.: Automatic Ki-67 counting using robust cell detection and online dictionary learning. IEEE Tran. Biomed. Eng. 61(3), 859–870 (2014)

[10] Geread, R.S., et al.: Ihc colour histograms for unsupervised ki67 proliferation index calculation. Front. Bioeng. Biotechnol. 7, 226 (2019)

[11] Shi, P., et al.: Automated ki-67 quantification of immunohistochemical staining image of human nasopharyngeal carcinoma xenografts. Sci. Rep. 6, 32127 (2016)

[12] Xu, Y., et al.: Deep learning of feature representation with multiple instance learning for medical image analysis. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1626–1630 (2014)

[13] Weidi, X., Noble, J.A., Zisserman, A.: Microscopy cell counting with fully convolutional regression networks. In: 1st Deep Learning Workshop, Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2015)

[14] Niazi, M., Tavolara, T., Arole, V., et al.: Identifying tumor in pancreatic neuroendocrine neoplasms from ki67 images using transfer learning. PLoS ONE (2018). https://doi.org/10.1371/journal.pone.019562

[15] Feng, M., Deng, Y., Yang, L., et al.: Automated quantitative analysis of Ki-67 staining and he images recognition and registration based on whole tissue sections in breast carcinoma. Diagn. Pathol. 15(1), 1–12 (2020)

[16] Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

[17] Geread, R.S., Sivanandarajah, A., Brouwer, E.R., et al.: Pinet-an automated proliferation index calculator framework for Ki67 breast cancer images. Cancers (2021). https://doi.org/10.3390/cancers13010011

[18] Chang, C.-C., et al.: Automatic segmentation of breast cancer nuclei based on morphological features using unsupervised learning and morphological operations. IEEE Trans. Med. Imaging 38(1), 415–423 (2019)

[19] Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

[20] Isensee, F., et al.: Abstract: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation. In: Handels, H., Deserno, T.M., Maier, A., Maier-Hein, K.H., Palm, C., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2019: Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck, pp. 22–22. Springer Fachmedien Wiesbaden, Wiesbaden (2019). https://doi.org/10.1007/978-3-658-25326-4_7

[21] Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)

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