Artificial intelligence in radiotherapy

被引:23
|
作者
Li, Guangqi [1 ,2 ]
Wu, Xin [3 ]
Ma, Xuelei [1 ,2 ,4 ]
机构
[1] Sichuan Univ, West China Hosp, Canc Ctr, Div Biotherapy, 37 GuoXue Alley, Chengdu 610041, Peoples R China
[2] Sichuan Univ, State Key Lab Biotherapy, 37 GuoXue Alley, Chengdu 610041, Peoples R China
[3] Sichuan Univ, West China Hosp, Head & Neck Oncol ward, Canc Ctr,Div Radiotherapy Oncol, 37 GuoXue Alley, Chengdu 610041, Peoples R China
[4] Sichuan Univ, West China Hosp, Canc Ctr, Div Biotherapy,State Key Lab Biotherapy, 37 Guoxue Alley, Chengdu 610041, Sichuan, Peoples R China
关键词
Artificial intelligence; Radiotherapy; Auto-segmentation; Auto-planning; Quality assurance; DEFORMABLE IMAGE REGISTRATION; CLINICAL TARGET VOLUMES; QUALITY-ASSURANCE; ADAPTIVE RADIOTHERAPY; AUTO-SEGMENTATION; IMRT QA; RADIOMIC ANALYSIS; DOSE CALCULATION; ERROR-DETECTION; DEEP;
D O I
10.1016/j.semcancer.2022.08.005
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional bal-ances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.
引用
收藏
页码:160 / 171
页数:12
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