Share：
Share this content in WeChat
X
[Chinese][PDF]70172
• Review •
Application progress of radiomics in breast cancer
MA Xiao-wen  LUO Ya-hong 

DOI:10.12015/issn.1674-8034.2018.08.015.


[Abstract] With the increasing incidence and mortality of breast cancer, improving the accuracy of its early diagnosis has become a clinical problem to be solved. Radiomics is a noninvasive technology of high-throughput extraction features. In recent years, its application in clinical diagnosis and treatment has drawn wide attention and research. This article reviews a large number of literatures and reviews the progress of the research on the application of radiomics in breast cancer.
[Keywords] Radiomics；Breast neoplasms；Magnetic resonance imaging

MA Xiao-wen Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, China

LUO Ya-hong* Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, China

*Corresponding to: Luo YH, E-mail: luoyahong8888@hotmail.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of National Public Welfare Industry Special Fund No.201402020
Received  2018-04-08
Accepted  2018-06-15
DOI: 10.12015/issn.1674-8034.2018.08.015
DOI:10.12015/issn.1674-8034.2018.08.015.

[1]
Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin, 2011, 61(2): 69.
[2]
Gillies RJ, Anderson AR, Gatenby RA, et al. The biology underlying molecular imaging in oncology: from genome to anatome and back again. 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. Eur J Cancer, 2012, 48(4): 441.
[4]
Levy MA, Freymann JB, Kirby JS, et al. Informatics methods to enable sharing of quantitative imaging research data. Magn Reson Imaging, 2012, 30(9): 1249-1256.
[5]
王敏，宋彬，黄子星，等．大数据时代的精准影像医学：放射组学.中国普外基础与临床杂志, 2016, 23(6): 752-755.
[6]
Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev, 2016, 1(2): 207-226.
[7]
Yamamoto S, Maki DD, Korn RL, et al. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. AJR Am J Roentgenol, 2012, 199(3): 654-663.
[8]
Elbanan MG, Amer AM, Zinn PO, et al. Imaging genomics of glioblastoma: state of the art bridge between genomics and neuroradiology. Neuroimaging Clin N Am, 2015, 25(1): 141-153.
[9]
Hesketh RL, Zhu AX, Oklu R. Hepatocellular carcinoma: can circulating tumor cells and radiogenomics deliver personalized care?. Am J Clin Oncol, 2015, 38(4): 431-436.
[10]
Nair VS, Gevaert O, Davidzon G, et al. NF-κB protein expression associates with (18)F-FDG PET tumor uptake in non-small cell lung cancer: a radiogenomics validation study to understand tumor metabolism. Lung Cancer, 2014, 83(2): 189-196.
[11]
Karlo CA, Di PP, Chaim J, et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology, 2014, 270(2): 464-471.
[12]
Michoux N, Vand BS, Lacoste L, et al. Texture analysis on MR images helps predicting non-response to NAC in breast cancer. BMC Cancer, 2015, 15(1): 574.
[13]
Teruel JR, Heldahl MG, Goa PE, et al. Dynamic contrast-enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. NMR Biomed, 2014, 27(8): 887-896.
[14]
Chen W, Giger ML, Li H, et al. Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med, 2007, 58(3): 562-571.
[15]
Holli K, Lääperi AL, Harrison L, et al. Characterization of breast cancer types by texture analysis of magnetic resonance images. Acad Radiol, 2010, 17(2): 135-141.
[16]
Ganeshan B, Strukowska O, Skogen K, et al. Heterogeneity of focal breast lesions and surrounding tissue assessed by mammographic texture analysis: preliminary evidence of an association with tumor invasion and estrogen receptor status. Front Oncol, 2011(1): 33.
[17]
Bo LY, Cho N, Li M, et al. Intratumoral heterogeneity of breast cancer xenograft models: texture analysis of diffusion-weighted MR imaging. Korean J Radiol, 2014, 15(5): 591-604.
[18]
Goldhirsch A, Winer EP, Coatas AS, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary of Early breast Cancer 2013. Ann Oncol, 2013, 24(9): 2206-2223.
[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. Breast Cancer Res, 2010, 12(5): R87.
[20]
Waugh SA, Purdie CA, Jordan LB, et al. Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol, 2016, 26(2): 322-330.
[21]
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(1): 91-99.
[22]
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. PLoS One, 2015, 10(11): e0143308.
[23]
Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging, 2015, 42(4): 902-907.
[24]
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(2):365-372.
[25]
Ma W, Zhao Y, Ji Y, et al. Breast cancer molecular subtype prediction by mammographic radiomic features. Acad Radiol. 2018 DOI: 10.1016/j.acra.2018.01.023.
[26]
Ahmed A, Gibbs P, Pickles M, et al. Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging, 2013, 38(1): 89-101.
[27]
Bhooshan N, Giger ML, Jansen SA, et al. Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology, 2010, 254(3): 680-690.
[28]
Hui L, Zhu Y, Burnside ES, et al. MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of mamma print, oncotype DX, and PAM50 gene assays. Radiology, 2016, 281(2): 382-391.
[29]
Sutton EJ, Jung Hun O, Dashevsky BZ, et al. Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay. J Magn Reson Imaging, 2015, 42(5): 1398-1406.
[30]
Zhu Y, Li H, Guo W, et al. Deciphering genomic underpinnings of quantitative MRI-based radiomic phenotypes of invasive breast carcinoma. Med Phys, 2015, 42(Part32): 17787.
[31]
Li H, Giger ML, Sun C, et al. Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. Med Phys, 2014, 41(3): 31917.
[32]
Guo W, Li H, Zhu Y, et al. Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Med Imaging, 2015, 2(4): 41007.
[33]
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 DOI: 10.1016/j.acra.2018.01.006.

PREV Advances in intracranial aneurysms by high resolution magnetic resonance vessel wall imaging
NEXT Diagnostic value of cerebrospinal fluid 1H-MRS for mitochondrial encephalopathy
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn