[1] |
SIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2020[J]. CA: A Cancer Journal for Clinicians, 2020, 70(1): 7−30. doi: 10.3322/caac.21590
|
[2] |
KASSEM K, SHAPIRO M, GORENSTEIN L, et al. Evaluation of high-risk pulmonary nodules and pathologic correlation in patients enrolled in a low-dose computed tomography (LDCT) program[J]. Journal of Thoracic Disease, 2019, 11(4): 1165−1169. doi: 10.21037/jtd.2019.04.31
|
[3] |
MANOS D, SEELY J M, TAYLOR J, et al. The lung reporting and data system (LU-RADS): A proposal for computed tomography screening[J]. Canadian Association of Radiologists Journal, 2014, 65(2): 121−134. doi: 10.1016/j.carj.2014.03.004
|
[4] |
LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis[J]. European Journal of Cancer, 2012, 48(4): 441−446. doi: 10.1016/j.ejca.2011.11.036
|
[5] |
LAMBIN P, LEIJENAAR R T H, DEIST T M, et al. Radiomics: The bridge between medical imaging and personalized medicine[J]. Nature Reviews Clinical Oncology, 2017, 14(12): 749−762. doi: 10.1038/nrclinonc.2017.141
|
[6] |
PRATX G, XING L. GPU computing in medical physics: A review[J]. Medical Physics, 2011, 38(5): 2685−2697. doi: 10.1118/1.3578605
|
[7] |
LIU Z, ZHANG X Y, SHI Y J, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer[J]. Clinical Cancer Research, 2017, 23(23): 7253−7262. doi: 10.1158/1078-0432.CCR-17-1038
|
[8] |
HUANG Y Q, LIANG C H, HE L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer[J]. Journal of Clinical Oncology, 2016, 34(18): 2157−2164. doi: 10.1200/JCO.2015.65.9128
|
[9] |
RIOS-VELAZQUEZ E, PARMAR C, LIU Y, et al. Somatic mutations drive distinct imaging phenotypes in lung cancer[J]. Cancer Research, 2017, 77(14): 3922−3930. doi: 10.1158/0008-5472.CAN-17-0122
|
[10] |
张晓菊. 《肺结节诊治中国专家共识(2018版)》解读[J]. 中华实用诊断与治疗杂志, 2019,33(1): 1−3. doi: 10.13507/j.issn.1674-3474.2019.01.001ZHANG X J. Interpretation of “Chinese Expert Consensus on Diagnosis and Treatment of Pulmonary Nodules (2018 Edition)”[J]. Chinese Journal of Practical Diagnosis and Treatment, 2019, 33(1): 1−3. (in Chinese). doi: 10.13507/j.issn.1674-3474.2019.01.001
|
[11] |
National Lung Screening Trial Research Team, CHURCH T R, BLACK W C, et al. Results of initial low-dose computed tomographic screening for lung cancer[J]. New England Journal of Medicine, 2013, 368(21): 1980−1991. doi: 10.1056/NEJMoa1209120
|
[12] |
BACH P B, MIRKIN J N, OLIVER T K, et al. Benefits and harms of CT screening for lung cancer: A systematic review[J]. Journal of the American Medical Association, 2012, 307(22): 2418−2429. doi: 10.1001/jama.2012.5521
|
[13] |
NAIDICH D P, BANKIER A A, MACMAHON H, et al. Recommendations for the management of subsolid pulmonary nodules detected at CT: A statement from the Fleischner Society[J]. Radiology, 2013, 266(1): 304−317. doi: 10.1148/radiol.12120628
|
[14] |
LOVERDOS K, FOTIADIS A, KONTOGIANNI C, et al. Lung nodules: A comprehensive review on current approach and management[J]. Annals of Thoracic Medicine, 2019, 14(4): 226−238. doi: 10.4103/atm.ATM_110_19
|
[15] |
DUAN X Q, WANG X L, ZHANG L F, et al. Establishment and validation of a prediction model for the probability of malignancy in solid solitary pulmonary nodules in Northwest China[J]. Journal of Surgical Oncology, 2021, 123(4): 1134−1143. doi: 10.1002/jso.26356
|
[16] |
欧阳雨晴, 倪莲芳, 刘新民. 肺结节多学科联合诊治价值[J]. 北京大学学报(医学版), 2021,53(3): 628−631. doi: 10.19723/j.issn.1671-167X.2021.03.032OUYANG Y Q, NI L F, LIU X M. The value of multidisciplinary joint diagnosis and treatment of pulmonary nodules[J]. Journal of Peking University (Medical Edition), 2021, 53(3): 628−631. (in Chinese). doi: 10.19723/j.issn.1671-167X.2021.03.032
|
[17] |
WIELPÜTZ M O, HEUßEL C P, HERTH F J F, et al. Radiological diagnosis in lung disease: Factoring treatment options into the choice of diagnostic modality[J]. Deutsches Arzteblatt International, 2014, 111(11): 181−187.
|
[18] |
高美娟. X线与CT在肺结节诊断中的对比研究[J]. 影像研究与医学应用, 2018,12(2): 2096−3807. doi: 10.3969/j.issn.2096-3807.2018.12.020GAO M J. Comparative study of X-ray and CT in the diagnosis of pulmonary nodules[J]. Imaging Research and Medical Application, 2018, 12(2): 2096−3807. (in Chinese). doi: 10.3969/j.issn.2096-3807.2018.12.020
|
[19] |
MA J, WANG Q, REN Y, et al. Automatic lung nodule classification with radiomics approach[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(1): 194−204. doi: 10.1109/JBHI.2019.2902298
|
[20] |
XIE Y, ZHANG J, XIA Y, et al. Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT[J]. Information Fusion, 2018, 42: 102−110. doi: 10.1016/j.inffus.2017.10.005
|
[21] |
BALAGURUNATHAN Y, SCHABATH M B, WANG H, et al. Quantitative imaging features improve discrimination of malignancy in pulmonary nodules[J]. Scientific Reports, 2019, 9(1): 8528. doi: 10.1038/s41598-019-44562-z
|
[22] |
WU W, PIERCE L A, ZHANG Y, et al. Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: A case-control study[J]. European Radiology, 2019, 29(11): 6100−6108. doi: 10.1007/s00330-019-06213-9
|
[23] |
ZENG H, CHEN W, ZHENG R, et al. Changing cancer survival in China during 2003-15: A pooled analysis of 17 population-based cancer registries[J]. The Lancet Global Health, 2018, 6(5): e555−e567. doi: 10.1016/S2214-109X(18)30127-X
|
[24] |
崔效楠, 刘颖, 叶兆祥, 等. 影像组学特征对肺纯磨玻璃结节侵袭性腺癌与非侵袭性腺癌的鉴别价值[J]. 国际医学放射学, 2018,41(4): 375−378.CUI X N, LIU Y, YE Z X, et al. Differentiation value of radiomic features in invasive adenocarcinoma and non-invasive adenocarcinoma of pure ground glass nodules of the lung[J]. International Medical Radiology, 2018, 41(4): 375−378. (in Chinese).
|
[25] |
LI W, WANG X, ZHANG Y, et al. Radiomic analysis of pulmonary ground-glass opacity nodules for distinction of preinvasive lesions, invasive pulmonary adenocarcinoma and minimally invasive adenocarcinoma based on quantitative texture analysis of CT[J]. Chinese Journal of Cancer Research, 2018, 30(4): 415−424. doi: 10.21147/j.issn.1000-9604.2018.04.04
|
[26] |
CAI J, LIU H, YUAN H, et al. A radiomics study to predict invasive pulmonary adenocarcinoma appearing as pure ground-glass nodules[J]. Clinical Radiology, 2021, 76(2): 143−151. doi: 10.1016/j.crad.2020.10.005
|
[27] |
XIA X, GONG J, HAO W, et al. Comparison and fusion of deep learning and radiomics features of ground-glass nodules to predict the invasiveness risk of stage-I lung adenocarcinomas in CT scan[J]. Frontiers in Oncology, 2020, 31(10): 418.
|
[28] |
WANG B, TANG Y H, CHEN Y N, et al. Joint use of the radiomics method and frozen sections should be considered in the prediction of the final classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules[J]. Lung Cancer, 2020, 139(1): 103−110.
|
[29] |
CHEN X, FENG B, CHEN Y, et al. A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma[J]. European Journal of Radiology, 2021, 145: 110041. doi: 10.1016/j.ejrad.2021.110041
|
[30] |
CHADDAD A, DESROSIERS C, TOEWS M, et al. Predicting survival time of lung cancer patients using radiomic analysis[J]. Oncotarget, 2017, 8(61): 104393−104407. doi: 10.18632/oncotarget.22251
|
[31] |
王鑫超, 崔曹哲, 胡奕奕, 等. 不同影像组学特征筛选方法对早期NSCLC患者生存预测效能的比较研究[J]. 肿瘤影像学, 2021,30(6): 459−465. doi: 10.19732/j.cnki.2096-6210.2021.06.005WANG X C, CUI C Z, HU Y Y, et al. Comparative study on the survival prediction efficacy of different radiomics feature screening methods in patients with early stage NSCLC[J]. Oncology Imaging, 2021, 30(6): 459−465. (in Chinese). doi: 10.19732/j.cnki.2096-6210.2021.06.005
|