Zhang Junqian, Zhang Yuan, Yin Yong, Zhu Jian, Li Baosheng
School of Information Science and Engineering, University of Jinan, Jinan 250022, P.R.China.
School of Information Science and Engineering, University of Jinan, Jinan 250022,
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2019 Oct 25;36(5):879-884. doi: 10.7507/1001-5515.201810051.
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.
放射治疗是肿瘤的主要治疗手段之一,对技术精度和设备稳定性的要求越来越高。机器学习可能会给放射治疗带来简便性、个性化和精确性,并可能提高计划和质量保证的自动化水平。本文基于放射治疗的流程,综述了机器学习的应用与研究,重点是深度学习,并从正常组织和肿瘤的分割、计划、治疗实施、质量保证和预后预测等方面提出了展望。