切换至 "中华医学电子期刊资源库"

中华肺部疾病杂志(电子版) ›› 2021, Vol. 14 ›› Issue (05) : 549 -553. doi: 10.3877/cma.j.issn.1674-6902.2021.05.001

专家论坛

影像组学及深度学习在肺结节良恶性鉴别诊断中的新理念
范卫杰1, 张冬1,()   
  1. 1. 400037 重庆,陆军(第三)军医大学第二附属医院放射科
  • 收稿日期:2021-09-06 出版日期:2021-10-25
  • 通信作者: 张冬

New concepts of imaging omics and deep learning in differential diagnosis of benign and malignant pulmonary nodules

Weijie Fan1, Dong Zhang1()   

  • Received:2021-09-06 Published:2021-10-25
  • Corresponding author: Dong Zhang
引用本文:

范卫杰, 张冬. 影像组学及深度学习在肺结节良恶性鉴别诊断中的新理念[J/OL]. 中华肺部疾病杂志(电子版), 2021, 14(05): 549-553.

Weijie Fan, Dong Zhang. New concepts of imaging omics and deep learning in differential diagnosis of benign and malignant pulmonary nodules[J/OL]. Chinese Journal of Lung Diseases(Electronic Edition), 2021, 14(05): 549-553.

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.
[1] 王亚红, 蔡胜, 葛志通, 杨筱, 李建初. 颅骨骨膜窦的超声表现一例[J/OL]. 中华医学超声杂志(电子版), 2024, 21(11): 1089-1091.
[2] 李洋, 蔡金玉, 党晓智, 常婉英, 巨艳, 高毅, 宋宏萍. 基于深度学习的乳腺超声应变弹性图像生成模型的应用研究[J/OL]. 中华医学超声杂志(电子版), 2024, 21(06): 563-570.
[3] 洪玮, 叶细容, 刘枝红, 杨银凤, 吕志红. 超声影像组学联合临床病理特征预测乳腺癌新辅助化疗完全病理缓解的价值[J/OL]. 中华医学超声杂志(电子版), 2024, 21(06): 571-579.
[4] 叶莉, 杜宇. 深度学习在牙髓根尖周病临床诊疗中的应用[J/OL]. 中华口腔医学研究杂志(电子版), 2024, 18(06): 351-356.
[5] 屈翔宇, 张懿刚, 李浩令, 邱天, 谈燚. USP24及其共表达肿瘤代谢基因在肝细胞癌中的诊断和预后预测作用[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 659-662.
[6] 中华医学会器官移植学分会. 肝移植术后缺血性胆道病变诊断与治疗中国实践指南[J/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(06): 739-748.
[7] 郑大雯, 王健东. 胆囊癌辅助诊断研究进展[J/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(06): 769-773.
[8] 陆镜明, 韩大为, 任耀星, 黄天笑, 向俊西, 张谞丰, 吕毅, 王傅民. 基于术前影像组学的肝内胆管细胞癌淋巴结转移预测的系统性分析[J/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(06): 852-858.
[9] 袁雨涵, 杨盛力. 体液和组织蛋白质组学分析在肝癌早期分子诊断中的研究进展[J/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(06): 883-888.
[10] 李浩, 陈棋帅, 费发珠, 张宁伟, 李元东, 王硕晨, 任宾. 慢性肝病肝纤维化无创诊断的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(09): 863-867.
[11] 谭瑞义. 小细胞骨肉瘤诊断及治疗研究现状与进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 781-784.
[12] 孙铭远, 褚恒, 徐海滨, 张哲. 人工智能应用于多发性肺结节诊断的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 785-790.
[13] 王子阳, 王宏宾, 刘晓旌. 血清标志物对甲胎蛋白阴性肝细胞癌诊断的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(07): 677-681.
[14] 陈慧, 邹祖鹏, 周田田, 张艺丹, 张海萍. 皮肤镜对头皮红斑性皮肤病辅助鉴别诊断的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(07): 692-698.
[15] 胡云鹤, 周玉焯, 付瑞瑛, 于凡, 李爱东. CHS-DRG付费制度下GB1分组住院费用影响因素分析与管理策略探讨[J/OL]. 中华临床医师杂志(电子版), 2024, 18(06): 568-574.
阅读次数
全文


摘要


AI


AI小编
你好!我是《中华医学电子期刊资源库》AI小编,有什么可以帮您的吗?