Application of deep learning in cancer prognosis prediction model.
10.7507/1001-5515.201909066
- Author:
Wen CHEN
1
;
Xu WANG
1
;
Huihong DUAN
1
;
Xiaobing ZHANG
1
;
Ting DONG
1
;
Shengdong NIE
1
Author Information
1. Institute of Medical Imaging, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China.
- Publication Type:Journal Article
- Keywords:
cancer prognosis model;
deep learning;
precision medical treatment;
prognosis
- MeSH:
Deep Learning;
Humans;
Neoplasms;
Precision Medicine;
Prognosis
- From:
Journal of Biomedical Engineering
2020;37(5):918-929
- CountryChina
- Language:Chinese
-
Abstract:
In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.
