Author:
Fulawka Lukasz,Blaszczyk Jakub,Tabakov Martin,Halon Agnieszka
Abstract
AbstractThe proliferation index (PI) is crucial in histopathologic diagnostics, in particular tumors. It is calculated based on Ki-67 protein expression by immunohistochemistry. PI is routinely evaluated by a visual assessment of the sample by a pathologist. However, this approach is far from ideal due to its poor intra- and interobserver variability and time-consuming. These factors force the community to seek out more precise solutions. Virtual pathology as being increasingly popular in diagnostics, armed with artificial intelligence, may potentially address this issue. The proposed solution calculates the Ki-67 proliferation index by utilizing a deep learning model and fuzzy-set interpretations for hot-spots detection. The obtained region-of-interest is then used to segment relevant cells via classical methods of image processing. The index value is approximated by relating the total surface area occupied by immunopositive cells to the total surface area of relevant cells. The achieved results are compared to the manual calculation of the Ki-67 index made by a domain expert. To increase results reliability, we trained several models in a threefold manner and compared the impact of different hyper-parameters. Our best-proposed method estimates PI with 0.024 mean absolute error, which gives a significant advantage over the current state-of-the-art solution.
Publisher
Springer Science and Business Media LLC
Reference30 articles.
1. Saha, M., Chakraborty, C., Arun, I., Ahmed, R. & Chatterjee, S. An advanced deep learning approach for Ki-67 stained hotspot detection and proliferation rate scoring for prognostic evaluation of breast cancer. Sci. Rep. 71(7), 1–14 (2017).
2. Negahbani, F. 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, 25 (2021).
3. Lakshmi, S., Vijayasenan, D., Sumam, D. S., Sreeram, S. & Suresh, P. K. An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index. In Proceedings of the TENCON 2019 : Technology, Knowledge, and Society : 17–20 October 2019, Grand Hyatt Kochi Bolgatti, Kerala, India. 2310–2314 (2019).
4. Swiderska-Chadaj, Z., Gallego, J., Gonzalez-Lopez, L. & Bueno, G. Detection of Ki67 hot-spots of invasive breast cancer based on convolutional neural networks applied to mutual information of H& E and Ki67 whole slide images. Appl. Sci. 10, 7761 (2020).
5. Valkonen, M. et al. Cytokeratin-supervised deep learning for automatic recognition of epithelial cells in breast cancers stained for ER, PR, and Ki-67. IEEE Trans. Med. Imaging 39, 534–542 (2020).
Cited by
20 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献