There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Guoqong Zhao |
| Institution: | Shandong University |
| Department: | School of Control Science and Engineering |
| Country: | |
| Proposed Analysis: | Breast cancer is the most common cancer type in females worldwide [1]. As the development of machine learning, automatic breast cancer subtype classification and prognosis prediction could help clinicians make informed decisions and guide appropriate therapy [2,3]. We plan to use machine learning for human breast cancer prognosis prediction by integrating multi-dimensional data. [1] F. Bray, J. Ferlay, I. Soerjomataram, R.L. Siegel, L.A. Torre, A. Jemal, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: A Cancer Journal for Clinicians. 68 (2018) 394–424. https://doi.org/10.3322/caac.21492. [2] D. Sun, M. Wang, A. Li, A Multimodal Deep Neural Network for Human Breast Cancer Prognosis Prediction by Integrating Multi-Dimensional Data, IEEE/ACM Trans. Comput. Biol. and Bioinf. 16 (2019) 841–850. https://doi.org/10.1109/TCBB.2018.2806438. [3] A. El-Nabawy, N. El-Bendary, N.A. Belal, A feature-fusion framework of clinical, genomics, and histopathological data for METABRIC breast cancer subtype classification, Applied Soft Computing. 91 (2020) 106238. https://doi.org/10.1016/j.asoc.2020.106238. |
| Additional Investigators |
