A review of machine learning in tumor radiotherapy.
10.7507/1001-5515.201810051
- Author:
Junqian ZHANG
1
;
Yuan ZHANG
2
;
Yong YIN
3
,
4
;
Jian ZHU
3
,
4
;
Baosheng LI
3
,
4
Author Information
1. School of Information Science and Engineering, University of Jinan, Jinan 250022, P.R.China.
2. School of Information Science and Engineering, University of Jinan, Jinan 250022, P.R.China.yuan.zhang@ieee.org.
3. Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan 250117, P.R.China
4. Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan 250117, P.R.China.
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
cancer;
deep learning;
machine learning;
neural network;
radiotherapy
- MeSH:
Deep Learning;
Humans;
Machine Learning;
Neoplasms;
radiotherapy
- From:
Journal of Biomedical Engineering
2019;36(5):879-884
- CountryChina
- Language:Chinese
-
Abstract:
Radiotherapy is one of the main treatments for tumor with increasingly high request for technique precision and the equipment stability. Machine learning may bring radiotherapy simplicity, individualization and precision, and may improve the automatic level of planning and quality assurance. Based on the process of radiotherapy, this paper reviews the applications and researches on machine learning, with an emphasis on deep learning, and proposes the prospects in the following aspects: segmentation of normal tissue and tumor, planning, treatment delivery, quality assurance and prognosis prediction.
