| 1 |
犹成亿,尤恒,叶东樊,等. 基于3D Res U-Net-Faster RCNN技术和CT影像学特征的肺结节性质预测模型的建立[J/OL]. 中华肺部疾病杂志(电子版), 2024,17(5): 673-679.
|
| 2 |
Zhong D, Sidorenkov G, Jacobs C, et al. Lung nodule management in low-dose ct screening for lung cancer: Lessons from the NELSON trial[J]. Radiology, 2024, 313(1): e240535.
|
| 3 |
Yip R, Mulshine JL, Oudkerk M, et al. Current evidence of low-dose CT screening benefit[J]. Eur J Cancer, 2025, 225: 115570.
|
| 4 |
Henderson LM, Rivera MP, Basch E. Broadened eligibility for lung cancer screening: Challenges and uncertainty for implementation and equity[J]. JAMA, 2021, 325(10): 939-941.
|
| 5 |
钱春蕊,周燕,张晶,等. 高分辨率CT与多层螺旋CT在肺结节及早期肺癌中的应用[J/OL]. 中华肺部疾病杂志(电子版), 2024, 17(5): 827-830.
|
| 6 |
Yao Y, Guo B, Li J, et al. 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[J]. Quant Imaging Med Surg, 2022,12(5): 2777-2791.
|
| 7 |
Krist AH, Davidson KW, Mangione CM, et al. Screening for lung cancer: US preventive services task force recommendation statement[J]. JAMA, 2021, 325(10): 962-970.
|
| 8 |
Huang S, Yang J, Shen N, et al. Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective[J]. Semin Cancer Biol, 2023, 89: 30-37.
|
| 9 |
Huang G, Wei X, Tang H, et al. A systematic review and meta-analysis of diagnostic performance and physicians' perceptions of artificial intelligence (AI)-assisted CT diagnostic technology for the classification of pulmonary nodules[J]. J Thorac Dis, 2021, 13(8): 4797-4811.
|
| 10 |
Yin Y, Lu J, Tong J, et al. Relationship between early lung adenocarcinoma and multiple driving genes based on artificial intelligence medical images of pulmonary nodules[J]. Front Genet, 2023, 14: 1142795.
|
| 11 |
Tang TW, Lin WY, Liang JD, et al. Artificial intelligence aided diagnosis of pulmonary nodules segmentation and feature extraction[J]. Clin Radiol, 2023, 78(6): 437-443.
|
| 12 |
Liu M, Wu J, Wang N, et al. The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis[J]. PLoS One, 2023, 18(3): e0273445.
|
| 13 |
Lv J, Li J, Liu Y, et al. Artificial intelligence-aided diagnosis software to identify highly suspicious pulmonary nodules[J]. Front Oncol, 2021, 11: 749219.
|
| 14 |
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.
|
| 15 |
中华医学会呼吸病学分会. 早期肺癌诊断中国专家共识(2023年版)[J].中华结核和呼吸杂志,2023, 46(1): 1-18.
|
| 16 |
Iwano S, Kamiya S, Ito R, et al. Measurement of solid size in early-stage lung adenocarcinoma by virtual 3D thin-section CT applied artificial intelligence[J]. Sci Rep, 2023, 13(1): 21709.
|
| 17 |
Creamer AW, Horst C, Prendecki R, et al. Performance of volume and diameter thresholds in malignancy prediction of solid nodules in lung cancer screening[J]. Thorax, 2025, 80(9): 624-631.
|
| 18 |
Jiang B, Li N, Shi X, et al. Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT[J]. Radiology, 2022, 303(1): 202-212.
|
| 19 |
Yoon SH, Kim J, Lee KJ, et al. Volumetric analysis of pulmonary nodules: reducing the discrepancy between the diameter-based volume calculation and voxel-counting method[J]. Quant Imaging Med Surg, 2022, 12(3): 1674-1683.
|
| 20 |
Al-senan R, Newhouse JH. CT Volumetry of convoluted objects-A simple method using volume averaging[J]. Tomography, 2021, 7(2): 120-129.
|
| 21 |
朱红伟,马士华,康文杰,等. 人工智能早期肺小结节筛查在胸部低剂量CT体检的大样本数据调查研究[J]. 中国实验诊断学,2022, 26(8): 1128-1132.
|
| 22 |
Mostafa R, Abdelsamie KandeeLA, Abd ElkareemM, et al. Pretherapy 18F-fluorodeoxyglucose positron emission tomography/computed tomography robust radiomic features predict overall survival in non-small cell lung cancer[J]. Nucl Med Commun, 2022, 43(5): 540-548.
|
| 23 |
Wang Q, Xu S, Zhang G, et al. Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis[J]. J Appl Clin Med Phys, 2022, 23(11): e13759.
|
| 24 |
Adams S J, Mikhael P, Wohlwend J, et al. Artificial intelligence and machine learning in lung cancer screening[J]. Thorac Surg Clin, 2023, 33(4): 401-409.
|
| 25 |
Lyu X, Dong L, Fan Z, et al. Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students[J]. BMC Med Educ, 2024, 24(1): 740.
|
| 26 |
Ye K, Xu L, Pan B, et al. Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V[J]. Eur Radiol, 2025, 35(7): 3739-3749.
|
| 27 |
Bocquet W, Bouzerar R, François G, et al. Detection of pulmonary nodules on ultra-low dose chest computed tomography with deep-learning image reconstruction algorithm[J]. J Thorac Imaging, 2025, 40(3): e0806.
|
| 28 |
Gao S, Xu Z, Kang W, et al. Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors′evaluation in lung cancer screening[J]. BMC Med Imaging, 2024, 24(1): 141.
|
| 29 |
Chaudhary M, Gerard SE, Christensen GE, et al. LungViT: Ensembling cascade of texture sensitive hierarchical vision transformers for cross-volume chest CT image-to-image translation[J]. IEEE Trans Med Imaging, 2024, 43(7): 2448-2465.
|
| 30 |
Wang J, Zhu Z, Pan Z, et al. Deep learning reconstruction improves computer-aided pulmonary nodule detection and measurement accuracy for ultra-low-dose chest CT[J]. BMC Med Imaging, 2025, 25(1): 200.
|