[1] SIEGEL R L,MILLER K D,JEMAL A,et al.Cancer statistics,2020[J].CA Cancer J Clin,2020,70(1):7-30.
[2] VALDORA F,HOUSSAMI N,ROSSI F,et al.Rapid review:radiomics and breast cancer[J].Breast Cancer Res Treat,2018,169(2):217-229.
[3] YIP S S F,AERTS H J W L.Applications and limitations of radiomics[J].Phys Med Biol,2016,61(13):R150-R166.
[4] WEAVER O,LEUNG J W T.Biomarkers and imaging of breast cancer[J].Am J Roentgenol,2018,210(2):271-278.
[5] GILLIES R J,KINAHAN P E,HRICAK H.Radiomics:images are more than pictures,they are data[J].Radiology,2015,278(2):563-577.
[6] LAMBIN P,LEIJENAAR R T H,DEIST T M,et al.Radiomics:the bridge between medical imaging and personalized medicine[J].Nat Rev Clin Oncol,2017,14:749-762.
[7] KHANGURA S,KONNYU K,CUSHMAN R,et al.Evidence summaries:the evolution of a rapid review approach[J].Syst Rev,2012,1(1):10-18.
[8] LIMKIN E J,SUN R,DERCLE L,et al.Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology[J].Ann Oncol,2017,28(6):1191-1206.
[9] 郭翌,周世崇,余锦华,等.影像组学的前沿研究与未来挑战[J].肿瘤影像学,2017,26(2):81-90.
[10] 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.
[11] LEE S E,HAN K,KWAK J Y,et al.Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma[J].Sci Rep,2018,8(1):13546-13553.
[12] 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,2018,16(4,Part A):485-491.
[13] LI Z,YU L,WANG X,et al.Diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors[J].Clin Breast Cancer,2017,18(4):e621-e627.
[14] CHEN W,GIGER M L,BICK U,et al.Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI[J].Med Phys,2006,33(8):2878-2887.
[15] BHOOSHAN N,GIGER M L,JANSEN S A,et al.Cancerous breast lesions on dynamic contrast-enhanced MR images:computerized characterization for image-based prognostic markers[J].Radiology,2010,254(3):680-690.
[16] PAREKH V S,JACOBS M A .Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI[J].NPJ Breast Cancer,2017,3(1):43-51.
[17] BICKELHAUPT S,JAEGER P F,LAUN F B,et al.Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer[J].Radiology,2018,287(3):761-770.
[18] THOMPSON A M,MOULDER-THOMPSON S L .Neoadjuvant treatment of breast cancer[J].Ann Oncol,2012,23(suppl_10):x231-x236.
[19] BRAMAN N M,ETESAMI M,PRASANNA P,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 Res,2017,19(1):57-70.
[20] LIU Z,LI Z,QU J,et al.Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer:a multicenter study[J].Clin Cancer Res,2019,25(12):3538-3547.
[21] DRUKKER K,LI H,ANTROPOVA N,et al.Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival "early on" in neoadjuvant treatment of breast cancer[J].Cancer Imaging,2018,18(1):12-20.
[22] WU J,CAO G,SUN X,et al.Intratumoral spatial heterogeneity at perfusion MR imaging predicts,recurrence-free survival in locally advanced breast cancer treated with,neoadjuvant chemotherapy[J].Radiology,2018,288(1):26-35.
[23] AGNER S C,ROSEN M A,ENGLANDER S,et al.Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images:a feasibility study[J].Radiology,2014,272(1):91-99.
[24] LI H,ZHU Y,BURNSIDE E S,et al.Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set[J].NPJ Breast Cancer,2016,2:16012-16021.
[25] MAZUROWSKI M A,ZHANG J,GRIMM L J,et al.Radiogenomic analysis of breast cancer:luminal b molecular subtype is associated with enhancement dynamics at MR imaging[J].Radiology,2014,273(2):365-372.
[26] WANG J,KATO F,OYAMAMANABE N,et al.Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI:a pilot radiomics study[J].PLoS One,2015,10(11):e0143308.
[27] FAN M,LI H,WANG S,et al.Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer[J].PLoS One,2017,12(2):e0171683.
[28] 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.
[29] TAMEZPENA J,RODRIGUEZROJAS J,GOMEZRUEDA H,et al.Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer[J].PLoS One,2018,13(3):e0193871.
[30] GUO Y,HU Y,QIAO M,et al.Radiomics analysis on ultrasound for prediction biological behavior in breast invasive ductal carcinoma[J].Clin Breast Cancer,2017,18(3):e335-e344.
[31] 张杰,宋耀国,史丽民,等.乳腺癌前哨淋巴结转移的相关因素分析[J].现代医学,2019,47(3):320-324.
[32] BHOOSHAN N,GIGER M L,JANSEN S A,et al.Cancerous breast lesions on dynamic contrast-enhanced MR images:computerized characterization for image-based prognostic markers[J].Radiology,2010,254(3):680-690.
[33] DONG Y,FENG Q,YANG W,et al.Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI[J].Eur Radiol,2018,28(2):582-591.
[34] 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.
[35] 郭敏,张亚男,吕建鑫,等.乳腺病理Ki67指数和磁共振ADC值在乳腺癌诊疗中的应用研究进展[J].东南大学学报(医学版),2019,38(6):1099-1103.
[36] MA W,JI Y,QI L,et al.Breast cancer Ki67 expression prediction by DCE-MRI radiomics features[J].Clin Radiol,2018,73(10):909.e1-909.e5.
[37] LIANG C,CHENG Z,HUANG Y,et al.An MRI-based radiomics classifier for preoperative prediction of Ki-67 status in breast cancer[J].Acad Radiol,2018,25(9):1111-1117.
[38] TAGLIAFICO A S,BIGNOTTI B,ROSSI F,et al.Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features[J].Eur Radiol Exp,2019,3(1):36.
[39] GALLAGHER E J,LEROITH D.Obesity and diabetes:the increased risk of cancer and cancer-related mortality[J].Physiol Rev,2015,95(3):727-748.
[40] GIERACH G L,LAURA I,KARLA K,et al.Relationship between mammographic density and breast cancer death in the breast cancer surveillance consortium[J].J Natl Cancer Inst,2012,104(16):1218-1227.
[41] FALK R T,GENTZSCHEIN E,STANCZYK F Z,et al.Sex steroid hormone levels in breast adipose tissue and serum in postmenopausal women[J].Breast Cancer Res Treat,2012,131(1):287-294.
[42] OBEID J,STOYANOVA R,KWON D,et al.Multiparametric evaluation of preoperative MRI in early stage breast cancer:prognostic impact of peri-tumoral fat[J].Clin Transl Oncol,2017,19(2):211-218.
[43] 辛灵,刘倩,徐玲,等.乳腺癌基因检测与临床实践[J].中国实用外科杂志,2014,34(1):73-76.
[44] WOODARD G A,RAY K M,JOE B N,et al.Qualitative radiogenomics:association between Oncotype DX test recurrence score and BI-RADS mammographic and breast mr imaging features[J].Radiology,2018,286(1):60-70.
[45] LI H,ZHU Y,BURNSIDE E S,et al.MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint,Oncotype DX,and PAM50 gene assays[J].Radiology,2016,281(2):382-391.
[46] ASHRAF A B,DAYE D,GAVENONIS S,et al.Identification of intrinsic imaging phenotypes for breast cancer tumors:preliminary associations with gene expression profiles[J].Radiology,2014,272(2):374-384. |
