[1]Rebecca L,Siegel Kimberly D,Miller K,et al.Cancer statistics,2018[J].CA:A Cancer Journal for Clinicians,2018,68(1):7-30.
[2]Chen W,Zheng R,Baade PD,et al.Cancer statistics in china,2015[J].CA Cancer J Clin,2016,66(2):115-132.
[3]RJ Gillies,AR Anderson,RA Gatenby,et al.The biology underlying molecular imaging in oncology:from genome to anatome and back again[J].Clinical Radiology,2010,65(7):517-521.
[4]LI Shuangshuang,HOU Zhen,LIU Juan,et al.A review of radiomics analysis and modeling tools[J].Chinese Journal of Medical Physics,2018,35(09):1043-1049.[李双双,侯震,刘娟等.影像组学分析与建模工具综述[J].中国医学物理学杂志,2018,35(09):1043-1049.]
[5]R Wilson,A Devaraj.Radiomics of pulmonary nodules and lung cancer[J].Translational Lung Cancer Research,2017,6(1):86-91.
[6]Hu Liping,Wen Qingyi,Liu Lan.The value of MRI dynamic enhancement time-signal curve,diffusion-weighted imaging and incoherent motion in voxels for the diagnosis of breast lesions[J].Practical Cancer Journal,2018,33(09):1508-1511.
[7]VS Parekh,MA Jacobs.Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI[J].NPJ Breast Cancer,2017,14(3):43.
[8]HM Whitney,NS Taylor,K Drukker,et al.Additive benefit of radiomics over size alone in the distinction between benign lesions and luminal a cancers on a large clinical breast MRI dataset[J].Academic Radiology,2018,26(2):202-209.
[9]W Bogner,S Gruber,K Pinker,et al.Diffusion-weighted MR for differentiation of breast lesions at 3.0 T:How does selection of diffusion protocols affect diagnosis[J]?Radiology,2009,253(2):341-351.
[10]Bickelhaupt S,Jaeger PF,Laun FB,et al.Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer[J].Radiology,2018,287(3):p761-770.
[11]Q Zhang,Y Xiao,J Suo,et al.Sonoelastomics for breast tumor classification:A radiomics approach with clustering-based feature selection on sonoelastography[J].Ultrasound in Medicine & Biology,2017,43(5):1058-1069.
[12]Alberto Stefano Tagliafico,Francesca Valdora,Giovanna Mariscotti,et al.An exploratory radiomics analysis on digital breast tomosynthesis in women with mammographically negative dense breasts[J].The Breast,2018(40):92-96.
[13]Mao N,Yin P,Wang Q,et al.Added value of radiomics on mammography for breast cancer diagnosis:a feasibility study[J].J Am Coll Radiol,2019,16(4):485-491.
[14]Li H,Mendel KR,Lan L,et al.Digital mammography in breast cancer:Additive value of radiomics of breast parenchyma[J].Radiology,2019,191(1):15-20.
[15]A Goldhirsch,E Winer,A Coates,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].Annals of Oncology,2013,24(9):2206-2223.
[16]BS Rosenstein,CM West,SM Bentzen,et al.Radiogenomics:Radiobiology enters the era of big data and team science[J].International Journal of Radiation Oncology Biology Physics,2014,89(4):709-713.
[17]Hjwl Aerts,Ee Velazquez,Rth Leijenaar,et al.Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J].Nature Communications,2014(5):4006.
[18]W Guo,H Li,Y Zhu,et al.Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data[J].Journal of Medical Imaging,2015,2(4):041007.
[19]M Fan,H Li,Swang,et al.Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer[J].PLoS One,2017,12(2):e0171683.
[20]Y Guo,Y Hu,M Qiao,et al.Radiomics analysis on ultrasound for prediction of biologic behavior in breast invasive ductal carcinoma[J].Clinical Breast Cancer,2018,18(3):e335-e344.
[21]P Tang,KA Skinner,DG Hicks.Molecular classification of breast carcinomas by immunohistochemical analysis:Are we ready[J]?Diagnostic Molecular Pathology,2009,18(3):125-132.
[22]L Zhang,YJ Liu,SQ Jiang,et al.Ultrasound utility for predicting biological behavior of invasive ductal breast cancers[J].Asian Pacific Journal of Cancer Prevention,2014,15(19):8057-8062.
[23]W Ma,Y Zhao,Y Ji,et al.Breast cancer molecular subtype prediction by mammographic radiomic features[J].Academic Radiology,2018,26(2):196-201.
[24]Lidija Antunovic,Francesca Gallivanone,Martina Sollini,et al.[18F]FDG PET/CT features for the molecular characterization of primary breast tumors[J].Eur J Nucl Med Mol Imaging,2017,44(12):1545-1554.
[25]S Luangdilok,N Samarnthai,K Korphaisarn.Association between pathological complete response and outcome following neoadjuvant chemotherapy in locally advanced breast cancer patients[J].Journal of Breast Cancer,2014,17(4):376-385.
[26]H Earl,E Provenzano,J Abraham,et al.Neoadjuvant trials in early breast cancer:Pathological response at surgery and correlation to longer term outcomes-what does it all mean[J]?BMC Medicine,2015,13(1):234.
[27]NM Braman,M Etesami,P Prasanna,et al.Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI[J].Breast Cancer Research,2017,19(1):57-80.
[28]Lin Zhouyi,Zhang Qunxia,Ran Haitao.Research progress in imaging diagnosis of axillary lymph node metastasis in breast cancer[J].Chinese Journal of Medical Imaging,2018,26(07):552-555.
[29]Yuhao Dong,Qianjin Feng,Wei Yang,et al.Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MR[J].European Radiology,2017,28(2):582-591.
[30]MJ Ellis,VJ Suman,J Hoog,et al.Ki67 proliferation index as a tool for chemotherapy decisions during and after neoadjuvant aromatase inhibitor treatment of breast cancer:Results from the American college of surgeons oncology group Z1031 trial (alliance)[J].Journal of Clinical Oncology,2017,35(10):1061-1069.
[31]Seunggyun Ha,Sohyun Park,Ji-In Bang,et al.Metabolic radiomics for pretreatment 18F-FDG PET/CT to characterize locally advanced breast cancer:histopathologic characteristics,response to neoadjuvant chemotherapy,and prognosis[J].Scientific Report,2017,7(1):1556.
[32]Tao Wan,B Nicolas Bloch,Donna Plecha,et al.A Radio-genomics approach for identifying high risk estrogen receptor-positive breast cancers on DCE-MRI:Preliminary results in predicting oncotypeDX risk scores[J].Scientific Report,2016,18(6):21394.
[33]F Valdora,N Houssami,F Rossi,et al.Rapid review:Radiomics and breast cancer[J].Breast Cancer Research and Treatment,2018,169(2):217-229.