1 |
Zuo TT, Zheng RS, Zeng HM, et al. Female breast cancer incidence and mortality in China, 2013 [J]. Thorac Cancer, 2017, 8(3): 214-218.
|
2 |
Gillies RJ, Anderson AR, Gatenby RA, et al. The biology underlying molecular imaging in oncology: from genome to anatome and back again [J]. Clin Radiol, 2010, 65(7): 517-521.
|
3 |
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.
|
4 |
马晓雯,罗娅红. 影像组学在乳腺癌应用中的研究进展 [J]. 磁共振成像, 2018, 9(8): 637-640.
|
5 |
Liu C, Ding J, Spuhler K, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI [J]. J Magn Reson Imaging, 2019, 49(1): 131-140.
|
6 |
Sutton EJ, Huang EP, Drukker K, et al. Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes [J]. Eur Radiol Exp, 2017, 1(1): 1-10.
|
7 |
Ma W, Zhao Y, Ji Y, et al. Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features [J]. Acad Radiol, 2019, 26(2): 196-201.
|
8 |
Ou X, Wang J, Zhou R, et al. Ability of 18F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma [J]. Contrast Media Mol Imaging, 2019, 2019: 4507694.
|
9 |
梁翠霞,李明强,边兆英, 等. 基于深度学习特征的乳腺肿瘤分类模型评估 [J]. 南方医科大学学报, 2019, 39(1): 88-92.
|
10 |
Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data [J]. Radiology, 2016, 278(2): 563-577.
|
11 |
Yip SS, Aerts HJ. Applications and limitations of radiomics [J]. Phys Med Biol, 2016, 61(13): R150-R166.
|
12 |
Mercado CL. BI-RADS update [J]. Radiol Clin N Am, 2014, 52(3): 481-487.
|
13 |
孙梅,严传波,张雨, 等. 数据挖掘算法对乳腺肿瘤超声图像特征的优化及良恶性分类研究[J]. 科技通报, 2017, 33(10): 67-72.
|
14 |
Daoud MI, Bdair TM, Al-Najar M, et al. A Fusion-Based Approach for Breast Ultrasound Image Classification Using Multiple-ROI Texture and Morphological Analyses [J]. Comput Math Methods Med, 2016, 2016: 6740956.
|
15 |
Zhou Y, Xu J, Liu Q, et al. A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification [J]. IEEE Trans Biomed Eng, 2018, 65(9): 1935-1942.
|
16 |
Theek B, Opacic T, Magnuska Z, et al. Radiomic analysis of contrast-enhanced ultrasound data [J]. Sci Rep, 2018, 8(1): 11359.
|
17 |
Axelsson CK, Düring M, Christiansen PM, et al. Impact on regional recurrence and survival of axillary surgery in women with node-negative primary breast cancer [J]. Br J surg, 2009, 96(1): 40-46.
|
18 |
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.
|
19 |
Keller PJ, Lin AF, Arendt LM, et al. Mapping the cellular and molecular heterogeneity of normal and malignant breast tissues and cultured cell lines [J]. Breast Cancer Res, 2010, 12(5): R87.
|
20 |
Goldhirsch A, Winer EP, Coates AS, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013 [J]. Ann Oncol, 2013, 24(9): 2206-2223.
|
21 |
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.
|
22 |
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.
|
23 |
李佳伟,方舟,周瑾, 等. 浸润性三阴性乳腺癌超声影像组学特征与肿瘤生物学特性的关系研究 [J]. 中华超声影像学杂志, 2019, 28(2): 137-143.
|
24 |
Lee SE, Han K, Kwak JY, et al. Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma [J]. Sci Rep, 2018, 8(1): 13546.
|
25 |
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): 335-344.
|
26 |
Zhang Q, Xiao Y, Chen S, et al. Quantification of Elastic Heterogeneity Using Contourlet-Based Texture Analysis in Shear-Wave Elastography for Breast Tumor Classification [J]. Ultrasound Med Biol, 2015, 41(2): 588-600.
|
27 |
Xiao Y, Zeng J, Niu L, et al. Computer-Aided Diagnosis Based on Quantitative Elastographic Features with Supersonic Shear Wave Imaging [J]. Ultrasound Med Biol, 2014, 40(2): 275-286.
|