1 |
杨 丽,钱桂生. 肺结节临床精准诊断的新理念[J/CD]. 中华肺部疾病杂志(电子版), 2022, 15(1): 1-5.
|
2 |
Mazzone PJ, Lam L. Evaluating the Patient With a Pulmonary Nodule:A Review[J]. JAMA, 2022, 327(3): 264-273.
|
3 |
范子文,谢 冬,姜格宁,等. 肺磨玻璃结节自然生长史研究进展[J]. 中国胸心血管外科临床杂志,2019, 26(2): 175-179.
|
4 |
Chen K, Bai J, Reuben A, et al. Multiomics analysis reveals distinct immunogenomic features of lung cancer with ground-glass opacity[J]. Am J Respir Crit Care Med, 2021, 204(10): 1180-1192.
|
5 |
Li Q, Fan L, Cao ET, et al. Quantitative CT analysis of pulmonary pure ground-glass nodule predicts histological invasiveness[J]. Eur J Radiol, 2017, 89: 67-71.
|
6 |
Li CD, Huang ZG, Sun HL, et al. CT-guided preoperative localization of ground glass nodule: comparison between the application of embolization microcoil and the locating needle designed for pulmonary nodules[J]. Br J Radiol, 2021, 94(1123): 20210193.
|
7 |
Mirka H, Ferda J, Krakorova G, et al. The use of CT pattern in differentiating non-invasive, minimally invasive and invasive variants of lung adenocarcinoma[J]. Anticancer Res, 2021, 41(9): 4479-4482.
|
8 |
薛丽敏,李 瀛,强金伟,等. 基于靶扫描和常规扫描CT图像的影像组学模型预测肺磨玻璃结节的2年生长[J]. 复旦学报:医学版,2021, 48(6): 739-747.
|
9 |
Anyasodor AE, Nwose EU, Bwititi PT, et al. Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria[J]. Front Public Health, 2022, 10: 932631.
|
10 |
孟俊祥,柏正尧,计雪伟. 基于CT影像分析的肺部疾病辅助诊断系统应用研究[J]. 中国数字医学,2019, 14(3): 92-96.
|
11 |
杨兴云,宋立江,王 涛,等. 多层螺旋CT图像后处理技术在肺部结节鉴别诊断中的应用研究[J]. 中国医学装备,2019, 16(8): 33-36.
|
12 |
潘江峰,邝平定,应明亮,等. 肺部纯磨玻璃结节浸润性肺腺癌与浸润前病变的高分辨靶扫描CT鉴别诊断[J]. 浙江医学,2016, 38(11): 826-828.
|
13 |
Kobayashi Y, Sakao Y, Deshpande GA, et al. The association between baseline clinical-radiological characteristics and growth of pulmonary nodules with ground-glass opacity[J]. Lung Cancer, 2014, 83(1): 61-66.
|
14 |
尹 柯,伍建林,邱太春. 高分辨率CT征象诊断浸润性肺腺癌的模型建立[J]. 中国医学影像学杂志,2019, 27(11): 824-828.
|
15 |
Kakinuma R, Muramatsu Y, Kusumoto M, et al. Solitary pure ground-glass nodules 5 mm or smaller: Frequency of growth[J]. Radiology, 2015, 276(3): 873-882.
|
16 |
Cho J, Kim ES, Kim SJ, et al. Long-term follow-up of small pulmonary ground-glass nodules stable for 3 years: implications of the proper follow-up period and risk factors for subsequent growth[J]. J Thorac Oncol, 2016, 11(9): 1453-1459.
|
17 |
Shi Z, Deng J, She Y, et al. Quantitative features can predict further growth of persistent pure ground-glass nodule[J]. Quant Imaging Med Surg, 2019, 9(2): 283-291.
|
18 |
Miyanaga N, Akaza H, Yamakawa M, et al.Tissue elasticity imaging for diagnosis of prostate cancer: a preliminary report[J]. Int J Urol, 2006, 13(12): 1514-1518.
|
19 |
Zhang Y, Shen Y, Qiang JW, et al. HRCT features distinguishing pre-invasive from invasive pulmonary adenocarcinomas appearing as ground-glass nodules[J]. Eur Radiol, 2016, 26(9): 2921-2928.
|
20 |
Thawani R, McLane M, Beig N, et al. Radiomics and radiogenomics in lung cancer: A review for the clinician[J]. Lung Cancer, 2018, 115: 34-41.
|
21 |
Chen G, Jiao D, Peng S, et al. Peritumoral abnormalities on dynamic-enhanced CT after brachytherapy for hepatic malignancies: local progression or benign changes?[J]. Eur Radiol, 2022, 32(10): 7307-7319.
|
22 |
Sun Y, Li C, Jin L, et al. Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction[J]. Eur Radiol, 2020, 30(7): 3650-3659.
|
23 |
Zheng H, Zhang H, Wang S, et al. Invasive Prediction of Ground Glass Nodule Based on Clinical Characteristics and Radiomics Feature[J]. Front Genet, 2022, 12: 783391.
|
24 |
Fan L, Fang M, Li Z, et al. Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule[J]. Eur Radiol, 2019, 29(2): 889-897.
|
25 |
Sun Q, Huang Y, Wang J, et al. Applying CT texture analysis to determine the prognostic value of subsolid nodules detected during low-dose CT screening[J]. Clin Radiol, 2019, 74(1): 59-66.
|
26 |
Hu X, Ye W, Li Z, et al. Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤1 cm) based on CT texture analysis[J]. Br J Radiol, 2020, 93(1114): 20190762.
|
27 |
Bandara MS, Gurunayaka B, Lakraj G, et al. Ultrasound Based Radiomics Features of Chronic Kidney Disease[J]. Acad Radiol, 2022, 29(2): 229-235.
|
28 |
Yaşar S, Voyvoda N, Voyvoda B, et al. Using texture analysis as a predictive factor of subtype, grade and stage of renal cell carcinoma[J]. Abdom Radiol (NY), 2020, 45(11): 3821-3830.
|
29 |
Yan M, Wang W. A Non-invasive Method to Diagnose Lung Adenocarcinoma[J]. Front Oncol, 2020, 10: 602.
|
30 |
Tao G, Yin L, Shi D, et al. Dependence of radiomic features on pixel size affects the diagnostic performance of radiomic signature for the invasiveness of pulmonary ground-glass nodule[J]. Br J Radiol, 2021, 94(1118): 20200089.
|