1. |
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
|
2. |
Baker SR, Patel RH, Yang L, et al. Malpractice suits in chest radiology: An evaluation of the histories of 8265 radiologists. J Thorac Imaging, 2013, 28(6): 388-391.
|
3. |
Del Ciello A, Franchi P, Contegiacomo A, et al. Missed lung cancer: When, where, and why? Diagn Interv Radiol, 2017, 23(2): 118-126.
|
4. |
Prabhakar B, Shende P, Augustine S. Current trends and emerging diagnostic techniques for lung cancer. Biomed Pharmacother, 2018, 106: 1586-1599.
|
5. |
陈俊任, 曾瑜, 张超, 等. 人工智能医学应用的文献传播的可视化研究. 中国循证医学杂志, 2021, 21(8): 973-979.
|
6. |
Baldwin DR, Gustafson J, Pickup L, et al. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax, 2020, 75(4): 306-312.
|
7. |
Liang M, Tang W, Xu DM, et al. Low-dose CT screening for lung cancer: Computer-aided detection of missed lung cancers. Radiology, 2016, 281(1): 279-288.
|
8. |
Vassallo L, Traverso A, Agnello M, et al. A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies. Eur Radiol, 2019, 29(1): 144-152.
|
9. |
Li X, Guo F, Zhou Z, et al. Performance of deep-learning-based artificial intelligence on detection of pulmonary nodules in chest CT. Zhongguo Fei Ai Za Zhi, 2019, 22(6): 336-340.
|
10. |
Cai J, Xu D, Liu S, et al. The added value of computer-aided detection of small pulmonary nodules and missed lung cancers. J Thorac Imaging, 2018, 33(6): 390-395.
|
11. |
Zhang G, Jiang S, Yang Z, et al. Automatic nodule detection for lung cancer in CT images: A review. Comput Biol Med, 2018, 103: 287-300.
|
12. |
Weikert T, Akinci D, Antonoli T, et al. Evaluation of an AI-powered lung nodule algorithm for detection and 3D segmentation of primary lung tumors. Contrast Media Mol Imaging, 2019, 2019: 1545747.
|
13. |
Halder A, Dey D, Sadhu AK. Lung nodule detection from feature engineering to deep learning in thoracic CT images: A comprehensive review. J Digit Imaging, 2020, 33(3): 655-677.
|
14. |
Marcovici PA, Taylor GA. Journal club: Structured radiology reports are more complete and more effective than unstructured reports. AJR Am J Roentgenol, 2014, 203(6): 1265-1271.
|
15. |
Sahni VA, Silveira PC, Sainani NI, et al. Impact of a structured report template on the quality of MRI reports for rectal cancer staging. AJR Am J Roentgenol, 2015, 205(3): 584-588.
|
16. |
Jha S, Topol EJ. Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA, 2016, 316(22): 2353-2354.
|