Research progress of radiomics and artificial intelligence in lung cancer

被引：0
作者：
Xiang Wang
Wenjun Huang
Jingyi Zhao
Shaochun Xu
Song Chen
Man Gao
Li Fan
机构：
[1] Naval Medical University,Department of Radiology, Changzheng Hospital
[2] The Second People’s hospital of Deyang,Department of Radiology
[3] Sichuan Province,undefined
来源：
Chinese Journal of Academic Radiology | 2023年 / 6卷 / 3期
关键词：
Artificial intelligence; Radiomics; Lung cancer; Differential diagnosis; Therapeutic evaluation; Prognosis;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In recent years, the incidence of respiratory diseases has remained high due to environmental pollution, smoking and the ageing population. The incidence and mortality of lung cancer in China was the highest globally. Thus, early diagnosis and treatment is the key to lung cancer prevention and treatment. Radiomics and artificial intelligence have been successfully and widely used in lung cancer detection, differential diagnosis and efficacy evaluation, providing support for personalised patient treatment. In this paper, we reviewed the research progress of radiomics and artificial intelligence in lung cancer diagnosis and treatment.
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页码：91 / 99
页数：8
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共 306 条
[1]  
Sung H(2020)GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J Clin 10 100179-513
[2]  
Ferlay J(2021)Lung cancer mortality reduction by LDCT screening: UKLS randomised trial results and international meta-analysis Lancet Reg Health Eur. 382 503-72
[3]  
Siegel RL(2020)Reduced lung-cancer mortality with volume CT screening in a randomized trial N Engl J Med 92 64-303
[4]  
Laversanne M(2018)3D skeletonization feature based computer-aided detection system for pulmonary nodules in CT datasets Comput Biol Med 1 e180084-2791
[5]  
Soerjomataram I(2019)Evaluating a fully automated pulmonary nodule detection approach and its impact on radiologist performance Radiol Artif Intell. 18 295-212
[6]  
Jemal A(2012)Lung cancer screening: making the transition from research to clinical practice Curr Opin Pulm Med 12 2777-5063
[7]  
Global cancer statistics, JK(2022)The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study Quant Imaging Med Surg 303 202-1484
[8]  
Field D(2022)Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT Radiology 49 5052-446
[9]  
Vulkan MPA(2022)Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: a phantom study Med Phys 9 1477-95
[10]  
Davies DR(2022)Lung-optimized deep-learning-based reconstruction for ultralow-dose CT Acad Radiol 11 22330-4463
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