1 |
李华娟,唐英俊,王赛妮,等. 肺结节临床与CT影像学特征分析及良恶性预测模型构建[J/CD]. 中华肺部疾病杂志(电子版), 2023, 16(3): 318-323.
|
2 |
Lee JH, Park CM, Lee SM, et al. Persistent pulmonary subsolid nodules with solid portions of 5 mm or smaller: Their natural course and predictors of interval growth[J]. Eur Radiol, 2016, 26(6): 1529-1537.
|
3 |
Hsu WC, Huang PC, Pan KT, et al. Predictors of invasive adenocarcinomas among pure ground-glass nodules less than 2 cm in diameter[J]. Cancers (Basel), 2021, 13(16): 3945.
|
4 |
Borghesi A, Michelini S, Golemi S, et al. What′s new on quantitative CT analysis as a tool to predict growth in persistent pulmonary subsolid nodules? A literature review[J]. Diagnostics (Basel), 2020, 10(2): 55.
|
5 |
Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO classification of lung tumors: Impact of advances since 2015[J]. J Thorac Oncol, 2022, 17(3): 362-387.
|
6 |
Qi L, Lu W, Yang L, et al. Qualitative and quantitative imaging features of pulmonary subsolid nodules: Differentiating invasive adenocarcinoma from minimally invasive adenocarcinoma and preinvasive lesions[J]. J ThoracDis, 2019, 11: 4835-4846.
|
7 |
Behera M, Owonikoko TK, Gal AA, et al. Lung adenocarcinoma staging using the 2011 IASLC/ATS/ERS classification: A pooled analysis of adenocarcinoma in situ and minimally invasive adenocarcinoma[J]. Clin Lung Cancer, 2016, 17: e57-64.
|
8 |
Hammer MM, Hatabu H. Subsolid pulmonary nodules: Controversy and perspective[J]. Eur J Radiol Open, 2020, 7: 100267.
|
9 |
Qi LL, Wu BT, Tang W, et al. Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation[J]. Eur Radiol, 2020, 30(2): 744-755.
|
10 |
Shi L, Zhao J, Peng X, et al. CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: Asystematic review[J]. Eur J Radiol, 2021,144: 109956.
|
11 |
Hammer MM, Palazzo LL, Eckel AL, et al. A decision analysis of follow-up and treatment algorithms for nonsolid pulmonary nodules[J]. Radiology, 2019, 290(2): 506-513.
|
12 |
Kou J, Gu X, Kang L. Correlation analysis of computed tomography features and pathological types of multifocal ground-glass nodular lung adenocarcinoma[J]. Comput Math Methods Med, 2022, 2022: 7267036.
|
13 |
Li X, Zhang W, Yu Y, et al. CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction[J]. BMC Cancer, 2020, 20(1): 60.
|
14 |
Fang W, Zhang G, Yu Y, et al. Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs[J]. Biosci Rep, 2022, 42(1): BSR20212416.
|
15 |
Kou J, Gu X, Kang L. Correlation analysis of computed tomography features and pathological types of multifocal ground-glass nodular lung adenocarcinoma[J]. Comput math methods med, 2022, 2022: 7267036.
|
16 |
Wang H, Weng Q, Hui J, et al. Value of TSCT features for differentiating preinvasive and minimally invasive adenocarcinoma from invasive adenocarcinoma presenting as subsolid nodules smaller than 3 cm[J]. Acad radiol, 2020, 27(3): 395-403.
|
17 |
Gao J, Qi Q, Li H, et al. Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules[J]. Front Oncol, 2023, 13: 1096453.
|
18 |
Lee JH, Kim TH, Lee S, et al. High versus low attenuation thresholds to determine the solid component of ground-glass opacity nodules[J]. PLoS One, 2018, 13(10): e0205490.
|
19 |
Li WJ, Lv FJ, Tan YW, et al. Benign and malignant pulmonary part-solid nodules: differentiation via thin-section computed tomography[J]. Quant Imaging Med Surg, 2022, 12(1): 699-710.
|
20 |
Lee HW, Jin KN, Lee JK, et al. Long-term follow-up of ground-glass nodules after 5 years of stability[J]. J Thorac Oncol, 2019, 14(8): 1370-1377.
|
21 |
Gao C, Li J, Wu L, et al. The natural growth of subsolid nodules predicted by quantitative initial CT features: A systematic review[J]. Front Oncol, 2020, 10: 318.
|
22 |
He S, Chen C, Wang Z, et al. The use of the mean computed-tomography value to predict the invasiveness of ground-glass nodules: A meta-analysis[J]. Asian J Surg, 2023, 46(2): 677-682.
|
23 |
Zhang H, Wang D, Li W, et al. Artificial intelligence system-based histogram analysis of computed tomography features to predict tumor invasiveness of ground-glass nodules[J]. Quant Imaging Med Surg, 2023, 13(9): 5783-5795.
|