乳腺癌磁共振成像的影像组学研究进展
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摘要
<正>1乳腺癌现状国家癌症中心发布的报告显示,发达国家和发展中国家女性乳腺癌发病率均排名第1,女性乳腺癌死亡率在发达国家排名第2,在发展中国家排名第15[1]。城市居民生活方式的不断西化,肥胖率的增高、生育率的降低都是导致城市地区乳腺癌发病率不断增高的危险因素[1]。由于早
引文
[1]Fu Z T, Guo X L, Zhang S W et al.[Incidence and mortality of nasopharyngeal carcinoma in China,2014][J].Zhonghua Zhong Liu Za Zhi,2018, 40(8):566-571.
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[11] Sin ha S,Lucas-Quesada F A, D e B r u h l N D e t a l.Multifeature analysis of Gdenhanced MR images of breast lesions[J]. J Ma gn Reson Imaging, 1997, 7(6):1016-1026.
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[13]Cai Hongmin,Peng Yanxia,Ou Caiwen e t al.Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR:a machine learning approach[J].PLoS ONE,2014, 9:e87387.
[14]Guo Wentian,Li Hui,Zhu Yi tan et al. Predict ion of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data[J].J Med Imaging(Bellingham), 2015, 2(4):041007.
[15]Dong Yuhao, Feng Qianjin,Yang Wei,et al. Preoperative predict ion of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppress ion and diffusion-weighted MRI[J]. Eur Radiol,2018, 28:582-591.
[16]Braman Nathaniel M,Etesami Maryam,Prasanna Prateek et al.Intratumoral and peri tumoralradiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI[J]. Breast Cancer Res., 2017, 19:57.
[17]Obeid J-P,Stoyanova R,Kwon D et al. Multiparametric evaluation of preoperative MR I in early stage breast cancer:prognostic impact of peri-tumoral fat[J].Clin Transl Oncol, 2017, 19:211-218.
[18]Whitney Heather M, Taylor Nathan S,Drukker Karen 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]Acad Radiol,2019, 26:202-209.
[19] Liang Cuishan,Cheng Zixuan,Huang Yanqi et al. An MRI-based Radiomics Classifier for Preoperative Predict ion of Ki-67 Status in Breast Cancer[J].Acad Radiol,2018, 25:1111-1117.
[20]Ma W, Ji Y,Qi L et al. Breast cancer Ki6 7 expression prediction by DCE-MRI radiomics features[J].Cl in Radiol,2018,73:909. e1-909. e5.
[21]Gatenby Robert A,Grove Olya,Gillies Robert J,Quantitative imaging in cancer evolution and ecology[J].Radiology,2013,269:8-15.
[2]Lambin P,Rios-Velazquez E,Leijenaar R,et al.Radiomics:Extracting more information from medical images using advanced feature analysis[J].European journal of cancer(Oxford,England:1 990),2012,48(4):441-446.
[3] L i H,Zhu Y, Burnside E S,et al. Quantitative MR I radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCI A data set[J].npj Breast Cancer, 2016, 2:16012.
[4]Aerts H J W L,Velazquez E R,Leijenaar R T H,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J].Nature Communications,2014,5(1):4006.
[5]Shannon C E. A mathematical theory of communication[J].Bell Systems Technical Journal,1948,27(4):623-656.
[6]Hara li ck R M,Shanmugam K,Dinstein I. Textural Features for Image Classification[J].IEEE Transact ions on Systems Man&Cybernetics, 1973,SMC3(6):610-621.
[7]Lambin P,Zindler J,Vanneste B G L, et al. Decision support systems for personalized and participativer adia tion oncology[J]. Advanced Drug Delivery Reviews, 2017,15(109):131-153.
[8]Gillies R J,Kinahan P E, Hricak H. Radiomics:Images Are More than Pictures,They Are Data[J].Radiology, 2016, 278(2):563-577.
[9]Lambin P,Rth L,Deist T M,et a l.Radiomics:the bridge between medical imaging and personalized medicine[J]. Nature Reviews Clinical Oncology,2017, 14(12):749-762.
[10]Moons Karel G M,Altman Douglas G,Reitsma Johannes B et a l.Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD):explanation and elaboration[J]. Ann. Intern.Med.,2015, 162:W1-73.
[11] Sin ha S,Lucas-Quesada F A, D e B r u h l N D e t a l.Multifeature analysis of Gdenhanced MR images of breast lesions[J]. J Ma gn Reson Imaging, 1997, 7(6):1016-1026.
[12]Wang Teh-Chen,Huang YanHao,Huang Chiun-Sheng,et a l.Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis[J].Magn Reson Imaging,2014, 3(32):197-205.
[13]Cai Hongmin,Peng Yanxia,Ou Caiwen e t al.Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR:a machine learning approach[J].PLoS ONE,2014, 9:e87387.
[14]Guo Wentian,Li Hui,Zhu Yi tan et al. Predict ion of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data[J].J Med Imaging(Bellingham), 2015, 2(4):041007.
[15]Dong Yuhao, Feng Qianjin,Yang Wei,et al. Preoperative predict ion of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppress ion and diffusion-weighted MRI[J]. Eur Radiol,2018, 28:582-591.
[16]Braman Nathaniel M,Etesami Maryam,Prasanna Prateek et al.Intratumoral and peri tumoralradiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI[J]. Breast Cancer Res., 2017, 19:57.
[17]Obeid J-P,Stoyanova R,Kwon D et al. Multiparametric evaluation of preoperative MR I in early stage breast cancer:prognostic impact of peri-tumoral fat[J].Clin Transl Oncol, 2017, 19:211-218.
[18]Whitney Heather M, Taylor Nathan S,Drukker Karen 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]Acad Radiol,2019, 26:202-209.
[19] Liang Cuishan,Cheng Zixuan,Huang Yanqi et al. An MRI-based Radiomics Classifier for Preoperative Predict ion of Ki-67 Status in Breast Cancer[J].Acad Radiol,2018, 25:1111-1117.
[20]Ma W, Ji Y,Qi L et al. Breast cancer Ki6 7 expression prediction by DCE-MRI radiomics features[J].Cl in Radiol,2018,73:909. e1-909. e5.
[21]Gatenby Robert A,Grove Olya,Gillies Robert J,Quantitative imaging in cancer evolution and ecology[J].Radiology,2013,269:8-15.