1. |
National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
|
2. |
Mets OM, Schmidt M, Buckens CF, et al. Diagnosis of chronic obstructive pulmonary disease in lung cancer screening computed tomography scans: independent contribution of emphysema, air trapping and bronchial wall thickening. Respir Res, 2013, 14(1): 59.
|
3. |
Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 1980, 36(4): 193-202.
|
4. |
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM, 2012, 60(2): 84-90.
|
5. |
Song QZ, Zhao L, Luo XK, et al. Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng, 2017, 2017: 8314740.
|
6. |
李梦琪, 王琪, 李恩成, 等. 基于大数据的肺癌合并COPD患者的临床特征. 西北国防医学杂志., 2021, 42(5): 285-290.
|
7. |
Sogancioglu E, Murphy K, Th Scholten E, et al. Automated estimation of total lung volume using chest radiographs and deep learning. Med Phys, 2022, 49(7): 4466-4477.
|
8. |
Global Inititative for Chronic Obstructive Lung Disease. Global Strategy For The Diagnosis, Management and Prevention of Chronic Obstuctive Pulmonary disease. 2022 Report. www. goldcopd. org.
|
9. |
中华医学会, 中华医学会肿瘤学分会, 中华医学会杂志社. 中华医学会肺癌临床诊疗指南(2019版). 中华肿瘤杂志, 2020, 42(4): 257-287.
|
10. |
中华医学会呼吸病学分会肺功能专业组. 肺功能检查指南(第二部分)—肺量计检查. 中华结核和呼吸杂志, 2014, 37(7): 481-486.
|
11. |
Isensee F, Jaeger PF, Kohl SAA, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods, 2021, 18(2): 203-211.
|
12. |
de-Torres JP, Marín JM, Casanova C, et al. Identification of COPD patients at high risk for lung cancer mortality using the COPD-LUCSS-DLCO. Chest, 2016, 149(4): 936-942.
|
13. |
Bishawi M, Moore W, Bilfinger T. Severity of emphysema predicts location of lung cancer and 5-y survival of patients with stage I non-small cell lung cancer. J Surg Res, 2013, 184(1): 1-5.
|
14. |
Carr LL, Jacobson S, Lynch DA, et al. Features of COPD as predictors of lung cancer. Chest, 2018, 153(6): 1326-1335.
|
15. |
Tammemägi MC, Katki HA, Hocking WG, et al. Selection criteria for lung-cancer screening. N Engl J Med, 2013, 368(8): 728-736.
|
16. |
Katki HA, Kovalchik SA, Petito LC, et al. Implications of nine risk prediction models for selecting ever-smokers for computed tomography lung cancer screening. Ann Intern Med, 2018, 169(1): 10-19.
|
17. |
Kim KH, Park TY, Lee JY, et al. Prognostic significance of initial platelet counts and fibrinogen level in advanced non-small cell lung cancer. J Korean Med Sci, 2014, 29(4): 507-511.
|
18. |
Labaki WW, Xia M, Murray S, et al. Quantitative emphysema on low-dose CT imaging of the chest and risk of lung cancer and airflow obstruction: an analysis of the National Lung Screening Trial. Chest, 2021, 159(5): 1812-1820.
|
19. |
Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer, Guida F, Sun N, Bantis LE, et al. Assessment of lung cancer risk on the basis of a biomarker panel of circulating proteins. JAMA Oncol, 2018, 4(10): e182078.
|