[1] Sung H,
Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates
of incidence and mortality worldwide for 36 cancers in 185 countries[J].CA
Cancer J Clin,2021,71(3):209-249. DOI:10.3322/caac.21660.
[2] Lambin P, Rios-Velazquez E, Leijenaar R,
et al. Radiomics: extracting more information from medical images using
advanced feature analysis[J].Eur J Cancer,2012,48(4):441-446.
DOI:10.1016/j.ejca.2011.11.036.
[3] 周世崇,刘桐桐,周瑾,等. 影像组学在甲状腺癌应用的初步研究[J]. 肿瘤影像学,2017,26(2):102-105.
DOI:10.3969/j.issn.1008-617X.2017.02.004.
[4] Zhang Q, Xiao Y, Suo J, et al.
Sonoelastomics for breast tumor classification: a radiomics approach with
clustering-based feature selection on sonoelastography[J].Ultrasound Med
Biol,2017,43(5):1058-1069. DOI:10.1016/j.ultrasmedbio.2016.12.016.
[5] 张富强,杨辉,陈恺,等. SPECT/CT显像、常规超声及钼靶成像对乳腺癌腋窝淋巴结转移的诊断效能分析[J]. 现代肿瘤医学,2021,29(15):2704-2708.
DOI:10.3969/j.issn.1672- 4992.2021.15.029.
[6] 段淑英,杨学东,孙黎,等. 钼靶X线假阴性乳腺癌的MRI特征[J]. 中国医学影像学杂志,2018,26(9):654-657. DOI:10.3969/j.issn.1005-5185.2018.09.004.
[7] Garra BS, Krasner BH, Horii SC, et al.
Improving the distinction between benign and malignant breast lesions: the
value of sonographic texture analysis[J]. Ultrason Imaging,1993,15(4):267-285.
DOI:10.1177/016173469301500401.
[8] Tan T, Platel B, Huisman H, et al.
Computer-aided lesion diagnosis in automated 3-D breast ultrasound using
coronal spiculation[J].IEEE Trans Med Imaging,2012,31(5):1034-1042.
DOI:10.1109/TMI.2012.2184549.
[9] Goldberg V, Manduca A, Ewert DL, et al.
Improvement in specificity of ultrasonography for diagnosis of breast tumors by
means of artificial intelligence[J].Med Phys,1992,19(6):1475-1481.
DOI:10.1118/1.596804.
[10] Chang RF, Wu WJ, Moon WK, et al.
Automatic ultrasound segmentation and morphology based diagnosis of solid
breast tumors[J].Breast Cancer Res Treat,2005,89(2):179-185.
DOI:10.1007/s10549-004-2043-z.
[11] Qiao M, Hu Y, Guo Y, et al. Breast tumor
classification based on a computerized breast imaging reporting and data system
feature system[J].J Ultrasound Med,2018,37(2):403-415. DOI:10.1002/jum.14350.
[12] Niu S, Huang J, Li J, et al. Application
of ultrasound artificial intelligence in the differential diagnosis between
benign and malignant breast lesions of BI-RADS 4A[J].BMC Cancer,2020,20(1):959.
DOI:10.1186/s12885-020- 07413-z.
[13] Youk JH, Kwak JY, Lee E, et al.
Grayscale ultrasound radiomic features and shear-wave elastographyradiomic
features in benign and malignant breast masses[J].Ultraschall
Med,2020,41(4):390-396. DOI:10.1055/a-0917-6825.
[14] Byra M, Galperin M, Ojeda-Fournier H, et
al. Breast mass classification in sonography with transfer learning using a
deep convolutional neural network and color conversion[J].Med
Phys,2019,46(2):746-755. DOI:10.1002/mp. 13361.
[15] Ochsner A. Diseases of the
breast[J].Postgrad Med,1975,57(3):77-84. DOI:10.1080/00325481.1975.11713986.
[16] 刘表虎,江峰,闫娜,等. 乳腺癌激素受体表达与超声影像组学关系的临床研究[J]. 临床超声医学杂志,2019,21(11):834-836.
DOI:10.3969/j.issn.1008-6978.2019.11.014.
[17] 李佳伟,时兆婷,郭翌,等. 超声影像组学对浸润性乳腺癌激素受体表达预测价值的探索性研究[J]. 肿瘤影像学,2017,26(2):128-135.
DOI:10.3969/j.issn.1008- 617X.2017.02.008.
[18] Guo Y, Hu Y, Qiao M, et al. Radiomics
analysis on ultrasound for prediction of biologic behavior in breast invasive
ductal carcinoma[J].Clin Breast Cancer,2018,18(3):e335-e344.
DOI:10.1016/j.clbc.2017.08.002.
[19] 刘桐桐,李佳伟,胡雨舟,等. 基于影像组学预测乳腺癌雌激素受体表达情况的可行性分析[J]. 生物医学工程学杂志,2017,34(4):597-601.
DOI:10.7507/1001-5515. 201611033.
[20] 李宝江,朱志华,王军业,等. Ki67、P53、VEGF和C-erbB-2在乳腺癌组织中表达的相关性研究及其临床意义[J]. 癌症,2004,23(10):1176-1179.
DOI:10.3969/j.issn.1000-467X.2004.10.014.
[21] Wang XY, Zhang B, He Y, et al. Relation
between qualitative and quantitative 3-dimensional ultrasound and ki-67
expression in breast cancer[J].Int J ClinExp Med,2015,8(10):18538-18542.
[22] Harbeck N, Gnant M. Breast
cancer[J].Lancet,2017,389(10074):1134-1150. DOI:10.1016/S0140-6736(16)31891-8.
[23] Lucci A, McCall LM, Beitsch PD, et al.
Surgical complications associated with sentinel lymph node dissection (SLND)
plus axillary lymph node dissection compared with SLND alone in the American
College of Surgeons Oncology Group Trial Z0011[J].J
ClinOncol,2007,25(24):3657-3663. DOI:10.1200/JCO.2006.07.4062.
[24] Abass MO, Gismalla MDA, Alsheikh AA, et
al. Axillary lymph node dissection for breast cancer: efficacy and complication
in developing countries[J].J Glob Oncol,2018,4:1-8. DOI:10.1200/JGO.18.00080.
[25] Gao Y, Luo Y, Zhao C, et al. Nomogram
based on radiomics analysis of primary breast cancer ultrasound images:
prediction of axillary lymph node tumor burden in patients[J].Eur
Radiol,2021,31(2):928-937. DOI:10.1007/s00330-020-07181-1.
[26] Zhou LQ, Wu XL, Huang SY, et al. Lymph
node metastasis prediction from primary breast cancer US images using deep
learning[J].Radiology,2020,294(1):19-28. DOI:10.1148/radiol.2019190372.
[27] 暴珞宁,王瑛,陈东,等. 超声影像组学标签预测乳腺癌前哨淋巴结转移的价值[J]. 实用医学杂志,2021,37(15):2007-2011.
DOI:10.3969/j.issn.1006-5725.2021.15.020.
[28] Tadayyon H, Sannachi L, Gangeh MJ, et
al. A priori prediction of neoadjuvant chemotherapy response and survival in
breast cancer patients using quantitative ultrasound[J].Sci Rep,2017,7:45733.
DOI:10.1038/srep45733.
[29] DiCenzo D, Quiaoit K, Fatima K, et al.
Quantitative ultrasound radiomics in predicting response to neoadjuvant
chemotherapy in patients with locally advanced breast cancer: results from
multi-institutional study[J].Cancer Med,2020,9(16):5798-5806.
DOI:10.1002/cam4.3255.
[30] Lowerison MR, Tse JJ, Hague MN, et al.
Compound speckle model detects anti-angiogenic tumor response in preclinical
nonlinear contrast-enhanced ultrasonography[J]. Med Phys,2017,44(1):99-111.
DOI:10.1002/mp. 12030.
[31] 李蔓英,李彬,罗佳,等. 基于灰阶超声的影像组学模型预测乳腺癌新辅助化疗效果[J]. 中国医学影像技术,2019,35(9):1331-1335.
DOI:10.13929/j.1003-3289.201903034.
[32] Jiang M, Li CL, Luo XM, et al.
Ultrasound-based deep learning radiomics in the assessment of pathological
complete response to neoadjuvant chemotherapy in locally advanced breast
cancer[J].Eur J Cancer,2021,147:95-105. DOI:10.1016/j.ejca.2021.01.028.
[33] Zhang Q, Yuan C, Dai W, et al.
Evaluating pathologic response of breast cancer to neoadjuvant chemotherapy
with computer-extracted features from contrast- enhanced ultrasound
videos[J].Phys Med,2017,39:156-163. DOI:10.1016/j.ejmp.2017.06.023.
[34] Youk JH, Kwak JY, Lee E, et al.
Grayscale ultrasound radiomic features and shear-wave elastographyradiomic
features in benign and malignant breast masses[J].Ultraschall Med,2020,41(4):390-396.
DOI:10.1055/a-0917-6825.
[35] Theek B, Opacic T, Magnuska Z, et al.
Radiomic analysis of contrast-enhanced ultrasound data[J].Sci
Rep,2018,8(1):11359. DOI:10.1038/s41598-018-29653-7.
[36] Choi JS, Han BK, Ko EY, et al.
Additional diagnostic value of shear-wave elastography and color Doppler US for
evaluation of breast non-mass lesions detected at B-mode US[J].Eur
Radiol,2016,26(10):3542-3549. DOI:10.1007/s00330-015-4201-6.
[37] 索静峰,张麒,常婉英,等. 依托弹性与B型双模态超声影像组学的 腋窝淋巴结转移评价[J]. 中国医疗器械杂志,2017,41(5):313-316,326. DOI:10.3969/j.issn.1671-7104. 2017. 05.001.
[38] Kapetas P, Clauser P, Woitek R, et al.
Quantitative multiparametric breast ultrasound: application of
contrast-enhanced ultrasound and elastography leads to an improved
differentiation of benign and malignant lesions[J].Invest
Radiol,2019,54(5):257-264. DOI:10. 1097/RLI.0000000000000543.
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