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• W3025835124 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.放射治疗（简称：放疗）作为肿瘤的主要治疗方式之一，在整个流程中对于治疗的技术精度和设备稳定性具有越来越高的要求。机器学习方法能够使放疗决策更加简化、个体化和精确化，提高了放疗计划设计和质量控制环节的自动化程度，推动了个体化的精准治疗。本文以放疗流程为线索，对机器学习方法尤其是深度学习法，在正常组织和肿瘤靶区的勾画、放疗计划设计、放疗实施、质量控制和放疗疗效预测等几个方面的应用、研究情况予以综述，并对发展前景做出展望。." @default.
• W3025835124 created "2020-05-21" @default.
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• W3025835124 date "2019-10-25" @default.
• W3025835124 modified "2023-09-26" @default.
• W3025835124 title "[A review of machine learning in tumor radiotherapy]." @default.
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• W3025835124 doi "https://doi.org/10.7507/1001-5515.201810051" @default.
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