全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

DWI、IVIM-DWI及相关新技术在乳腺癌中的应用进展
Application Progress of DWI, IVIM-DWI and Related New Technologies in Breast Cancer

DOI: 10.12677/ACM.2022.126725, PP. 5001-5006

Keywords: 扩散加权成像,体素不相干运动,小视野,乳腺癌
Diffusion Weighted Imaging
, Intravoxel Incoherent Motion, Small Field of View, Breast Cancer

Full-Text   Cite this paper   Add to My Lib

Abstract:

常规的扩散成像(diffusion weighted imaging, DWI)采用单e指数模型,近似地认为每个体素内的扩散为单一成分,反映了扩散的总体情况,不能反映生物组织内复杂的扩散信息。小视野成像(reduced field of view, r-FOV)和体素不相干运动(intravoxel incoherent motion , IVIM)这两种新的扩散加权成像技术在提高图像质量的同时,反映组织弥散和灌注信息,提供了乳腺肿瘤组织更多的生理病理信息。
Conventional diffusion weighted imaging (DWI) uses a single e-exponential model. Approximately, the diffusion of each voxel is considered to be a single component, which reflects the overall situa-tion of diffusion and cannot reflect the complex diffusion information in biological tissues. Reduced field of view (r-FOV) and intravoxel incoherent motion (IVIM), two new diffusion-weighted imaging techniques, can improve image quality while reflecting tissue diffusion and perfusion information, providing more physiological and pathological information of breast tumor tissue.

References

[1]  Lynch, B.M., Meilson, H.K. and Friedenreich, C.M. (2011) Physical Activity and Breast Cancer Prevention. Recent Re-sults in Cancer Research, 186, 13-42.
https://doi.org/10.1007/978-3-642-04231-7_2
[2]  Bihan, D.L., Breton, E., Lallemand, D., et al. (1986) MR Imaging of Intravoxel Incoherent Motions: Application to Diffusion and Perfusion in Neurologic Disorders. Radiology, 161, 401.
https://doi.org/10.1148/radiology.161.2.3763909
[3]  Bihan, D.L., Breton, E., Lallemand, D., et al. (1988) Separa-tion of Diffusion and Perfusion in Intravoxel Incoherent Motion MR Imaging. Radiology, 168, 497-505.
https://doi.org/10.1148/radiology.168.2.3393671
[4]  Saritas, E.U., Cunningham, C.H., Jin, H.L., et al. (2008) DWI of the Spinal Cord with Reduced FOV Single-Shot EPI. Magnetic Resonance in Medicine, 60, 468-473.
https://doi.org/10.1002/mrm.21640
[5]  Iima, M., Honda, M., Sigmund, E.E., et al. (2019) Diffusion MRI of the Breast: Current Status and Future Directions. Journal of Magnetic Resonance Imaging, 52, 70-90.
https://doi.org/10.1002/jmri.26908
[6]  Song, S.E., Park, E.K., Cho, K.R., et al. (2017) Additional Value of Diffu-sion-Weighted Imaging to Evaluate Multifocal and Multicentric Breast Cancer Detected Using Pre-Operative Breast MRI. European Radiology, 27, 4819-4827.
https://doi.org/10.1007/s00330-017-4898-5
[7]  Chen, X., Li, W.L., Zhang, Y.L., et al. (2010) Meta-Analysis of Quantitative Diffusion-Weighted MR Imaging in the Differential Diagnosis of Breast Lesions. BMC Cancer, 10, Article No. 693.
https://doi.org/10.1186/1471-2407-10-693
[8]  Woodhams, R., Ramadan, S., Stanwell, P., et al. (2011) Diffusion Weighted Imaging of the Breast: Principles and Clinical Applications. Radiographics, 31, 1059-1084.
https://doi.org/10.1148/rg.314105160
[9]  Zhang, J.L., Sigmund, E.E., Chandarana, H., et al. (2010) Variability of Renal Apparent Diffusion Coefficients: Limitations of the Monoexponential Model for Diffusion Quantification. Radiol-ogy, 254, 783-792.
https://doi.org/10.1148/radiol.09090891
[10]  车树楠. 扩散加权成像体素内不相干运动模型在乳腺病变中的应用价值研究[D]: [博士学位论文]. 北京: 北京协和医学院, 2016.
[11]  Liu, C., Liang, C., Liu, Z., et al. (2013) In-travoxel Incoherent Motion (IVIM) in Evaluation of Breast Lesions: Comparison with Conventional DWI. European Journal of Radiology, 82, e782-e789.
https://doi.org/10.1016/j.ejrad.2013.08.006
[12]  Liang, J., Zeng, S., Li, Z., et al. (2020) Intravoxel Incoherent Mo-tion Diffusion-Weighted Imaging for Quantitative Differentiation of Breast Tumors: A Meta-Analysis. Frontiers in On-cology, 10, Article ID: 585486.
https://doi.org/10.3389/fonc.2020.585486
[13]  Sigmund, E.E., Cho, G.Y., Kim, S., et al. (2011) Intravoxel Inco-herent Motion (IVIM) Imaging of Tumor Microenvironment in Locally Advanced Breast Cancer. Magnetic Resonance in Medicine, 65, 1437-1447.
https://doi.org/10.1002/mrm.22740
[14]  Wm, A., Jm, B., Tw, A., et al. (2021) Distinguishing between Benign and Malignant Breast Lesions Using Diffusion Weighted Imaging and Intravoxel Incoherent Motion: A Systematic Review and Meta-Analysis. European Journal of Radiology, 141, Article ID: 109809.
[15]  董海波, 俞伉, 李亚迪, 等. 小视野DWI在乳腺癌中的应用研究[J]. 临床放射学杂志, 2015, 34(3): 360-363.
[16]  韩冰. 3.0 T核磁小视野多b值弥散加权成像在非肿块乳腺癌的应用研究[D]: [硕士学位论文]. 长春: 吉林大学, 2017.
[17]  阿娜尔. 乳腺癌新辅助化疗疗效预测相关因素的研究进展[J]. 中国医药, 2018, 13(6): 957-960.
[18]  Li, X.R., Cheng, L., Liu, M., et al. (2012) DW-MRI ADC Values Can Predict Treatment Response in Patients with Locally Advanced Breast Cancer Un-dergoing Neoadjuvant Chemotherapy. Medical Oncology, 29, 425-431.
https://doi.org/10.1007/s12032-011-9842-y
[19]  Iacconi, C., Giannelli, M., Marini, C., et al. (2010) The Role of Mean Diffusivity (MD) as a Predictive Index of the Response to Chemotherapy in Locally Advanced Breast Cancer: A Preliminary Study. European Radiology, 20, 303-308.
https://doi.org/10.1007/s00330-009-1550-z
[20]  Park, S.H., Moon, W.K., Cho, N., et al. (2010) Diffusion-Weighted MR Imaging: Pretreatment Prediction of Response to Neoadju-vant Chemotherapy in Patients with Breast Cancer. Radiology, 257, 56-63.
https://doi.org/10.1148/radiol.10092021
[21]  耿小川, 张庆, 华佳, 等. 比较DWI体素不相干运动模型与单指数模型对乳腺癌新辅助化疗疗效评估的应用价值研究[J]. 磁共振成像, 2017, 8(3): 176-181.
[22]  周佳丽, 刘玉凤, 卜阳阳, 等. 乳腺癌多模态MRI基线图像预测新辅助化疗疗效[J]. 临床放射学杂志, 2018, 37(12): 1989-1993.
[23]  La Porta, E. and Welsh, J.E. (2014) Modeling Vitamin D Actions in Triple Negative/Basal-Like Breast Cancer. The Journal of Steroid Biochemistry and Molecular Biology, 144, 65-73.
https://doi.org/10.1016/j.jsbmb.2013.10.022
[24]  刘鸿利, 位寒, 娄鉴娟, 等. 3.0 T MRI扩散加权成像表观扩散系数直方图与乳腺浸润性导管癌预后因素的相关性研究[J]. 临床放射学杂志, 2018, 37(4): 600-606.
[25]  Choi, S.Y., Chang, Y.W., Park, H.J., et al. (2012) Correlation of the Apparent Diffusion Co-Efficiency Values on Diffu-sion-Weighted Imaging with Prognostic Factors for Breast Cancer. The British Journal of Radiology, 85, e474-e479.
https://doi.org/10.1259/bjr/79381464
[26]  Ali, S.H., O’Donnell, A.L., Balu, D., et al. (2001) Estrogen Receptor-α in the Inhibition of Cancer Growth and Angiogenesis. Cancer Research, 60, 7094-7098.
[27]  Wang, W., Zhang, X., Zhu, L., Chen, Y., Dou, W., Zhao, F., et al. (2022) Prediction of Prognostic Factors and Genotypes in Patients with Breast Cancer Using Multiple Mathematical Models of MR Diffusion Imaging. Frontiers in Oncology, 12, Article ID: 825264.
https://doi.org/10.3389/fonc.2022.825264
[28]  胡善林, 罗建芳, 聂云凤, 等. DWI表观扩散系数值预测乳腺癌预后的价值[J]. 放射学实践, 2021, 36(5): 601-605.
[29]  袁玉山, 张杨, 彭彬, 等. DWI定量值对乳腺癌分子分型的鉴别诊断[J]. 中国中西医结合影像学杂志, 2020, 18(1): 36-39.
[30]  Zhao, M., Wu, Q., Guo, L., et al. (2020) Magnetic Resonance Imaging Features for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer. European Journal of Radiology, 129, Article ID: 109093.
https://doi.org/10.1016/j.ejrad.2020.109093

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133