1 |
杨锋,樊军,田周俊逸,等. 人群肺亚实性结节CT筛查及人工智能应用研究初探[J]. 中华胸心血管外科杂志,2020, 36(3): 145-150.
|
2 |
Lancet T. GLOBOCAN 2018: counting the toll of cancer[J]. Lancet, 2018, 392(10152): 985.
|
3 |
Jacobs C, van Rikxoort EM, Scholten ET, et al. Solid, part-solid, or non-solid: classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system[J]. Invest Radiol, 2015 , 50(3): 168-173.
|
4 |
中华医学会呼吸病学分会肺癌学组,中国肺癌防治联盟专家组. 肺部结节诊治中国专家共识[J]. 中华结核和呼吸杂志,2015, 38(4): 249-254.
|
5 |
Chunxue Bai, Chang-Min Choi, Chung Ming Chu, et al. Evaluation of pulmonary nodules: clinical practice consensus guidelines for Asia[J]. Chest, 2016: 877-893.
|
6 |
中国食品药品检定研究院,中华医学会放射学分会心胸学组. 胸部CT肺结节数据标注与质量控制专家共识(2018)[J]. 中华放射学杂志,2019, 53(1): 9-15.
|
7 |
Pinsky PF, Gierada DS, Black W, et al. Performance of lung-RADS in the national lung screening trial: a retrospective assessment[J]. Ann Int Med, 2015, 162(7): 485-491.
|
8 |
许志高,唐光健,彭泰松,等. 肺部影像报告和数据系统(Lung-RADS1.1)更新解读[J]. 中华放射学杂志,2020, 54(9): 904-907.
|
9 |
中华医学会呼吸病学分会肺癌学组,中国肺癌防治联盟专家组. 肺结节诊治中国专家共识(2018年版)[J]. 中华结核和呼吸杂志,2018, 41(10): 763-771.
|
10 |
高丰,葛虓俊,李铭,等. 经多层螺旋CT探讨肺磨玻璃结节与支气管的关系[J]. 中华放射学杂志,2013, 47(2): 157-161.
|
11 |
于晶,王亮,伍建林,等. 周围型肺癌伴薄壁空腔的CT表现与征象分析[J]. 中华放射学杂志,2015, 2(99): 99-102.
|
12 |
袁鑫鑫,易琳,芮军,等. 肺内孤立结节的高分辨CT征象诊断价值观察[J]. 实用医技杂志,2017, 24(6): 615-616.
|
13 |
王璐,洪群英. 肺结节诊治中国专家共识(2018年版)解读[J]. 中国实用内科杂志,2019, 39(5): 48-50.
|
14 |
雷正文,史宏灿. 孤立性肺结节良恶性评估的研究现状及进展[J]. 国际外科学杂志,2016, 43(4): 270-274.
|
15 |
Sawada S, Yamashita N, Sugimoto R, et al. Long-term outcomes of patients with ground-glass opacities detected using CTScanning[J]. Chest, 2017, 151(2): 308-315.
|
16 |
Kenji Suzuki, Hisao Asamura, Masahiko Kusumoto, et al. "Early" peripheral lung cancer: prognostic significance of ground glass opacity on thin-section computed tomographic scan[J]. Ann Thorac Surg, 2002, 74(5): 1635-1659.
|
17 |
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 43(4): 441-446.
|
18 |
Sala E, Mema E, Himoto Y, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging[J]. Clin Radiol, 2017, 72(1): 3-10.
|
19 |
中国食品药品检定研究院,中华医学会放射学分会心胸学组. 胸部CT肺结节数据标注与质量控制专家共识(2018)[J]. 中华放射学杂志,2019, 53(1): 9-15.
|
20 |
张利文,方梦捷,臧亚丽,等. 影像组学的发展与应用[J]. 中华放射学杂志,2017, 51(1): 75-77.
|
21 |
Mackin D, Fave X, Zhang L, et al. Measuring computed tomography scanner variability of radiomics features[J]. Invest Radiol, 2015, 50(11): 757.
|
22 |
Shu Li, Ting Luo, Changwei Ding, et al. Detailed identification of epidermal growth factor receptor mutations in lung adenocarcinoma: Combining radiomics with machine learning[J]. Med Phys, 2020, 47(8): 3458-3466.
|
23 |
Choi W, Oh JH, Riyahi S, et al. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer[J]. Med Phys, 2018, 45(4).DOI: 10.1002/mp.12820.
|
24 |
Wei Wu, Larry A Pierce, Yuzheng Zhang, et al. Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study[J]. Eur Radiol, 2019, 29(11): 6100-6108.
|
25 |
Noemi Garau, Chiara Paganelli, Paul Summers, et al. External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis[J]. Med Phys, 2020, 47(9): 4125-4136.
|
26 |
Feng B, Chen X, Chen Y, et al. Differentiating minimally invasive and invasive adenocarcinomas in patients with solitary sub-solid pulmonary nodules with a radiomics nomogram[J]. Clin Radiol, 2019, 74(7): 570.
|
27 |
Wilson R, Devaraj A. Radiomics of pulmonary nodules and lung cancer[J]. Translat Lung Cancer Res, 2017, 6(1): 86.
|
28 |
Yan Xu, Lin Lu, Lin-Ning E, et al. Application of Radiomics in Predicting the Malignancy of Pulmonary Nodules in Different Sizes[J]. AJR Am J Roentgenol, 2019, 213(6): 1213-1220.
|
29 |
Beyer F, Zierott L, Fallenberg EM, et al. Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader[J]. Eur Radiol, 2007, 17(11): 2941.
|
30 |
Lodwick GS, Keats TE, Dorst JP. The Coding of roentgen images for computer analysis as applied to lung cancer[J]. Radiol, 1963, 81(81): 185-200.
|
31 |
Jie-Zhi Cheng, Dong Ni, Yi-Hong Chou, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in CT scans[J]. Sci Rep, 2016, 6: 24454.
|
32 |
Yoshiharu Ohno, Kota Aoyagi, Atsushi Yaguchi, et al. Differentiation of benign from malignant pulmonary nodules by using a convolutional neural network to determine volume change at chest CT[J]. Radiol, 2020, 296(2): 432-443.
|
33 |
Song Q, Zhao L, Luo X, et al. Using deep learning for classification of lung nodules on computed tomography images[J]. J Healthc Eng, 2017, 2017: 8314740.
|
34 |
Shen W. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification[J]. Pattern Recognit, 2017, 61(61): 663-673.
|
35 |
Nicholas Bien, Pranav Rajpurkar, Robyn L Ball, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet[J]. PLoS Med, 2018, 15(11): e1002699.
|
36 |
Yaojun Dai, Shiju Yan, Bin Zheng, et al. Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification[J]. Phys Med Biol, 2018, 63(24): 245004.
|
37 |
Xinzhuo Zhao, Shouliang Qi, Baihua Zhang, et al. Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning[J]. J Xray Sci Technol, 2019, 27(4): 615-629.
|
38 |
Shaffie A, Soliman A, Fraiwan L, et al. A Generalized deep learning-based diagnostic system for early diagnosis of various types of pulmonary nodules[J]. Technol Cancer Res Treat, 2018, 17: 1533033818798800.
|
39 |
Bao Feng, XiangMeng Chen, YeHang Chen, et al. Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas[J]. Eur Radiol, 2020, 30(12): 6497-6507.
|
40 |
Xianwu Xia, Jing Gong, Wen Hao, et al. Comparison and fusion of deep learning and radiomics features of ground-glass nodules to predict the invasiveness risk of stage-i lung adenocarcinomas in CT scan[J]. Front Oncol, 2020, 10: 418.
|
41 |
Hu XF, Gong J, Zhou W, et al. Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features[J]. Phys Med Biol, 2021, 66(6): 065015.
|
42 |
Johanna Uthoff, Matthew J Stephens, John D Newell Jr, et al. Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT[J]. Med Phys, 2019, 46(7): 3207-3216.
|
43 |
Ather S, Kadir T, Gleeson F. Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications[J]. Clin Radiol, 2020, 75(1): 13-19.
|