[1] |
Bray F, Ferlay J, Soerjomataram I, et al.
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J ClinCA Cancer J Clin, 2018, 68(6): 394-424.
doi: 10.3322/caac.21492 |
[2] |
Rzechonek A, Grzegrzolka J, Blasiak P, et al.
Correlation of expression of tenascin C and blood vessel density in non-small cell lung cancers[J]. Anticancer ResAnticancer Res, 2018, 38(4): 1987-1991.
doi: 10.21873/anticanres.12436 |
[3] |
Kim I, Lee JS, Kim SJ, et al.
Double-phase 18F-FDG PET-CT for determination of pulmonary tuberculoma activity[J]. Eur J Nucl Med Mol ImagingEur J Nucl Med Mol Imaging, 2008, 35(4): 808-814.
doi: 10.1007/s00259-007-0585-0 |
[4] |
World Health Organization. Global tuberculosis report 2020[EB/OL]. [2020-10-31]. https://apps.who.int/iris/handle/10665/336069. |
[5] |
Holli-Helenius K, Salminen A, Rinta-Kiikka I, et al. MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes—a feasibility study[J/OL]. BMC Med Imaging, 2017, 17(1): 69[2020-10-31]. https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-017-0239-z. DOI: 10.1186/s12880-017-0239-z. |
[6] |
Jeong YJ, Yi CA, Lee KS.
Solitary pulmonary nodules: detection, characterization, and guidance for further diagnostic workup and treatment[J]. AJR Am J RoentgenolAJR Am J Roentgenol, 2007, 188(1): 57-68.
doi: 10.2214/AJR.05.2131 |
[7] |
Lin JZ, Zhang L, Zhang CY, et al.
Application of gemstone spectral computed tomography imaging in the characterization of solitary pulmonary nodules: preliminary result[J]. J Comput Assist TomogrJ Comput Assist Tomogr, 2016, 40(6): 907-911.
doi: 10.1097/RCT.0000000000000469 |
[8] |
Strzelecki M, Szczypinski P, Materka A, et al.
A software tool for automatic classification and segmentation of 2D/3D medical images[J]. Nucl Instrum Methods Phys Res Sect A: Accelerat, Spectrom, Detect Assoc EquipNucl Instrum Methods Phys Res Sect A: Accelerat, Spectrom, Detect Assoc Equip, 2013, 702: 137-140.
doi: 10.1016/j.nima.2012.09.006 |
[9] |
Teramoto A, Tsukamoto T, Kiriyama Y, et al.
Automated classification of lung cancer types from cytological images using deep convolutional neural networks[J]. Biomed Res IntBiomed Res Int, 2017, 2017: 4067832-.
doi: 10.1155/2017/4067832 |
[10] |
Xiao N, Qiang Y, Bilal Zia M, et al.
Ensemble classification for predicting the malignancy level of pulmonary nodules on chest computed tomography images[J]. Oncol LettOncol Lett, 2020, 20(1): 401-408.
doi: 10.3892/ol.2020.11576 |
[11] |
Niyonkuru A, Chen XM, Bakari KH, et al. Evaluation of the diagnostic efficacy of 18F-fluorine-2-deoxy-D-glucose PET/CT for lung cancer and pulmonary tuberculosis in a tuberculosis-endemic country[J/OL]. Cancer Med, 2020, 9(3): 931−942[2020-10-31]. https://onlinelibrary.wiley.com/doi/10.1002/cam4.2770. DOI: 10.1002/cam4.2770. |
[12] |
Thawani R, McLane M, Beig N, et al.
Radiomics and radiogenomics in lung cancer: a review for the clinician[J]. Lung CancerLung Cancer, 2018, 115: 34-41.
doi: 10.1016/j.lungcan.2017.10.015 |
[13] |
Lee G, Lee HY, Park H, et al.
Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: state of the art[J]. Eur J RadiolEur J Radiol, 2017, 86: 297-307.
doi: 10.1016/j.ejrad.2016.09.005 |
[14] |
Labby ZE, Armato Ⅲ SG, Dignam JJ, et al.
Lung volume measurements as a surrogate marker for patient response in malignant pleural mesothelioma[J]. J Thorac OncolJ Thorac Oncol, 2013, 8(4): 478-486.
doi: 10.1097/JTO.0b013e31828354c8 |
[15] |
Lee JH, Lee HY, Ahn MJ, et al.
Volume-based growth tumor kinetics as a prognostic biomarker for patients with EGFR mutant lung adenocarcinoma undergoing EGFR tyrosine kinase inhibitor therapy: a case control study[J]. Cancer ImagingCancer Imaging, 2016, 16: 5-.
doi: 10.1186/s40644-016-0063-7 |
[16] |
Fogel I, Sagi D.
Gabor filters as texture discriminator[J]. Biol CybernBiol Cybern, 1989, 61(2): 103-113.
doi: 10.1007/BF00204594 |
[17] |
Bhargava R, Madabhushi A.
Emerging themes in image informatics and molecular analysis for digital pathology[J]. Annu Rev Biomed EngAnnu Rev Biomed Eng, 2016, 18: 387-412.
doi: 10.1146/annurev-bioeng-112415-114722 |
[18] |
Szczypiński PM, Strzelecki M, Materka A, et al.
MaZda—a software package for image texture analysis[J]. Comput Methods Programs BiomedComput Methods Programs Biomed, 2009, 94(1): 66-76.
doi: 10.1016/j.cmpb.2008.08.005 |
[19] |
Mannil M, Burgstaller JM, Held U, et al.
Correlation of texture analysis of paraspinal musculature on MRI with different clinical endpoints: lumbar Stenosis Outcome Study (LSOS)[J]. Eur RadiolEur Radiol, 2019, 29(1): 22-30.
doi: 10.1007/s00330-018-5552-6 |
[20] |
Hodgdon T, Thornhill RE, James ND, et al.
CT texture analysis of acetabular subchondral bone can discriminate between normal and cam-positive hips[J]. Eur RadiolEur Radiol, 2020, 30(8): 4695-4704.
doi: 10.1007/s00330-020-06781-1 |
[21] |
Holli-Helenius K, Salminen A, Rinta-Kiikka I, et al. MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes—a feasibility study[J/OL]. BMC Med Imaging, 2017, 17(1): 69[2020-10-31]. https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-017-0239-z. DOI: 10. 1186/s12880-017-0239-z. |
[22] |
Hui TCH, Chuah TK, Low HM, et al.
Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: a radiomics study[J]. Clin RadiolClin Radiol, 2018, 73(12): 1056.e11-1056.e16.
doi: 10.1016/j.crad.2018.07.109 |
[23] |
鄂林宁, 张娜, 王荣华, 等.
计算机体层摄影术纹理分析对孤立性肺结节良恶性鉴别诊断的价值[J]. 中华肿瘤杂志中华肿瘤杂志, 2018, 40(11): 847-850.
doi: 10.3760/cma.j.issn.0253-3766.2018.11.010 E LN, Zhang N, Wang RH, et al. Comparative analysis of computed tomography texture features between pulmonary inflammatory nodules and lung cancer[J]. Chin J OncolChin J Oncol, 2018, 40(11): 847-850. doi: 10.3760/cma.j.issn.0253-3766.2018.11.010 |