影像诊断与介入放射学

参考文献

[1]Bray F,Ferlay J,Soerjomataram I,et al.Global cancer statistics2018:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin,2018,68:394-424.

[2]Leithner D,Horvat JV,Ochoa-Albiztegui RE,et al.Imaging and the completion of the omics paradigm in breast cancer.Radiologe,2018,58 Suppl 1:7-13.

[3]Lambin P,Rios-Velazquez E,Leijenaar R,et al.Radiomics:extracting more information from medical images using advanced feature analysis.Eur J Cancer,2012,48:441-446.

[4]Levy MA,Freymann JB,Kirby JS,et al.Informatics methods to enable sharing of quantitative imaging research data.Magn Reson Imaging,2012,30:1249-1256.

[5]Gillies RJ,Kinahan PE,Hricak H.Radiomics:images are more than pictures,they are data.Radiology,2016,278:563-577.

[6]Aerts HJ,Velazquez ER,Leijenaar RT,et al.Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.Nat Commun,2014,5:4006.

[7]Wilson R,Devaraj A.Radiomics of pulmonary nodules and lung cancer.Transl Lung Cancer Res,2017,6:86-91.

[8]Huang Y,Liu Z,He L,et al.Radiomics signature:a potential biomarker for the prediction of disease-free survival in earlystage(I or II)non-small cell lung cancer.Radiology,2016,281:947-957.

[9]Rahbar H,Mcdonald ES,Lee JM,et al.How can advanced imaging be used to mitigate potential breast cancer overdiagnosis?Acad Radiol,2016,23:768-773.

[10]Parekh V,Jacobs MA.Radiomics:a new application from established techniques.Expert Rev Precis Med Drug Dev,2016,1:207-226.

[11]Parekh VS,Jacobs MA.Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI.NPJ Breast Cancer,2017,3:43.

[12]Whitney HM,Taylor NS,Drukker K,et al.Additive benefit of radiomics over size alone in the distinction between benign lesions and luminal a cancers on a large clinical breast MRIdataset.Acad Radiol,2019,26:202-209.

[13]Hu B,Xu K,Zhang Z,et al.A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings.Chin J Cancer Res,2018,30:432-438.

[14]Bickelhaupt S,Paech D,Kickingereder P,et al.Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.J Magn Reson Imaging,2017,46:604-616.

[15]Bickelhaupt S,Jaeger PF,Laun FB,et al.Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer.Radiology,2018,287:761-770.

[16]Curtis C,Shah SP,Chin SF,et al.The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups.Nature,2012,486:346-352.

[17]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.Ann Oncol,2013,24:2206-2223.

[18]Holli K,Laaperi A L,Harrison L,et al.Characterization of breast cancer types by texture analysis of magnetic resonance images.Acad Radiol,2010,17:135-141.

[19]Li H,Zhu Y,Burnside ES,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.Radiology,2016,281:382-391.

[20]Agner SC,Rosen MA,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.Radiology,2014,272:91-99.

[21]Wang J,Kato F,Oyama-Manabe N,et al.Identifying triplenegative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI:a pilot radiomics study.PloS One,2015,10:e143308.

[22]Blaschke E,Abe H.MRI phenotype of breast cancer:kinetic assessment for molecular subtypes.J Magn Reson Imaging,2015,42:920-924.

[23]Fan M,Li H,Wang S,et al.Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer.PloS One,2017,12:e171683.

[24]Grimm LJ,Zhang J,Mazurowski MA.Computational approach to radiogenomics of breast cancer:luminal A and luminal Bmolecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.J Magn Reson Imaging,2015,42:902-907.

[25]Mazurowski MA,Zhang J,Grimm LJ,et al.Radiogenomic analysis of breast cancer:luminal B molecular subtype is associated with enhancement dynamics at MR imaging.Radiology,2014,273:365-372.

[26]Earl H,Provenzano E,Abraham J,et al.Neoadjuvant trials in early breast cancer:pathological response at surgery and correlation to longer term outcomes-what does it all mean?BMC Med,2015,13:234.

[27]Cain EH,Saha A,Harowicz MR,et al.Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features:a study using an independent validation set.Breast Cancer Res Treat,2019,173:455-463.

[28]Pickles MD,Lowry M,Gibbs P.Pretreatment prognostic value of dynamic contrast-enhanced magnetic resonance imaging vascular,texture,shape,and size parameters compared with traditional survival indicators obtained from locally advanced breast cancer patients.Invest Radiol,2016,51:177-185.

[29]Parikh J,Selmi M,Charles-Edwards G,et al.Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy.Radiology,2014,272:100-112.

[30]Liu Z,Li Z,Qu J,et al.Radiomics of multiparametric MRI for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer:a multicenter study.Clin Cancer Res,2019,25:3538-3547.

[31]Braman NM,Etesami M,Prasanna P,et al.Erratum to:intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.Breast Cancer Res,2017,19:57.

[32]Razek AA,Lattif MA,Denewer A,et al.Assessment of axillary lymph nodes in patients with breast cancer with diffusionweighted MR imaging in combination with routine and dynamic contrast MR imaging.Breast Cancer,2016,23:525-532.

[33]Cui X,Wang N,Zhao Y,et al.Preoperative prediction of axillary lymph node metastasis in breast cancer using radiomics features of DCE-MRI.Sci Rep,2019,9:2240.

[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 Magn Reson Imaging,2019,49:131-140.

[35]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.Eur Radiol,2018,28:582-591.

[36]Han L,Zhu Y,Liu Z,et al.Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer.Eur Radiol,2019,29:3820-3829.

[37]Gallagher EJ,Leroith D.Obesity and diabetes:the increased risk of cancer and cancer-related mortality.Physiol Rev,2015,95:727-748.

[38]Goodwin PJ,Ambrosone CB,Hong CC.Modifiable lifestyle factors and breast cancer outcomes:current controversies and research recommendations.Adv Exp Med Biol,2015,862:177-192.

[39]Obeid JP,Stoyanova R,Kwon D,et al.Multiparametric evaluation of preoperative MRI in early stage breast cancer:prognostic impact of peri-tumoral fat.Clin Transl Oncol,2017,19:211-218.

[40]Ellis MJ,Suman VJ,Hoog J,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 Clin Oncol,2017,35:1061-1069.

[41]Ma W,Ji Y,Qi L,et al.Breast cancer Ki67 expression prediction by DCE-MRI radiomics features.Clin Radiol,2018,73:909.e1-909.e5.

[42]Liang C,Cheng Z,Huang Y,et al.An MRI-based Radiomics Classifier for Preoperative Prediction of Ki-67 Status in Breast Cancer.Acad Radiol,2018,25:1111-1117.

[43]Aleskandarany MA,Sonbul SN,Mukherjee A,et al.Molecular mechanisms underlying lymphovascular invasion in invasive breast cancer.Pathobiology,2015,82:113-123.

[44]Liu Z,Feng B,Li C,et al.Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics.J Magn Reson Imaging,2019,Feb 17.[Epub ahead of print].

[45]Park H,Lim Y,Ko ES,et al.Radiomics signature on magnetic resonance imaging:association with disease-free survival in patients with invasive breast cancer.Clin Cancer Res,2018,24:4705-4714.

[46]Hylton NM,Blume JD,Bernreuter WK,et al.Locally advanced breast cancer:MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPYTRIAL.Radiology,2012,263:663-672.

[47]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.Cancer Imaging,2018,18:12.