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Intravoxel Incoherent Motion Magnetic Resonance Imaging For Breast Cancer: A Comparison With Benign Lesions And Evaluation Of Heterogeneity In Different Tumor Regions With Prognostic Factors And Molecular Classification

M. Zhao, K. Fu, L. Zhang, Wenhui Guo, Q. Wu, Xue Bai, Ziyao Li, Q. Guo, Jiawei Tian
Published 2018 · Medicine

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The objective of the present study was to compare the differentiation between breast cancer and benign breast lesions and study regional distribution characteristics in various subtypes of breast cancer using intravoxel incoherent motion (IVIM) parameters. This retrospective study involved 119 patients with breast cancer and 22 patients with benign breast lesions, who underwent 3.0T breast magnetic resonance imaging examinations. The apparent diffusion coefficient (ADC) and IVIM parameters (slow ADC, fast ADC and fraction of fast ADC) were obtained from patients with breast cancer and benign lesions using diffusion-weighted imaging (DWI) with b-values of 0, 50, 100, 150, 200, 400, 500, 1,000 and 1,500 sec/mm2. Compared with patients with benign breast lesions, patients with breast cancer exhibited decreased ADC (P<0.001), slow ADC (P<0.001) and fast ADC (P<0.001) values, and higher fraction of fast ADC (P<0.001) values. Tumors with metastatic axillary lymph nodes demonstrated increased fraction of fast ADC values (P<0.001) and decreased slow ADC values (P<0.001) compared with tumors without metastatic axillary lymph nodes. The Fast ADC values of tumor tissues in estrogen receptor (ER) and progesterone receptor (PR) negative groups were higher than in positive groups (P<0.001), and the slow ADC values of tumor tissues were lower in ER and PR negative groups than positive groups (P<0.001). Luminal B (HER2- negative) tumor (P<0.001) and peritumor (P<0.001) tissues exhibited decreased fraction of fast ADC values, in comparison with other subtypes. Triple-negative breast cancer (TNBC) tumor tissue exhibited increased fast ADC (P<0.001) and fraction of fast ADC values (P<0.001), and decreased slow ADC values (P<0.001), when compared with other subtypes. The TNBC tumor edge tissues had increased fraction of fast ADC values compared with other subtypes (P<0.01) and TNBC tumor tissues (P<0.05). Therefore, the IVIM parameters of tumor, tumor edge and peritumor tissues in various subtypes of breast cancer may be useful for differentiation of breast cancer subtypes and to assess the invasive extent of the tumors.
This paper references
10.5858/arpa.2013-0953-SA
Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update.
A. Wolff (2014)
Intratumor microvessel density as a prognostic factor in cancer.
N. Weidner (1995)
10.1038/srep06566
Integrative investigation on breast cancer in ER, PR and HER2-defined subgroups using mRNA and miRNA expression profiling
X. Dai (2014)
10.1200/JCO.2009.24.9284
Breast cancer subtypes and the risk of local and regional relapse.
K. D. Voduc (2010)
10.1007/s12282-010-0236-3
MR imaging of triple-negative breast cancer
T. Uematsu (2011)
10.1148/RADIOL.2353041760
Imaging and cancer: research strategy of the American College of Radiology Imaging Network.
D. Aberle (2005)
10.1007/s00330-016-4241-6
Intravoxel incoherent motion MR imaging for breast lesions: comparison and correlation with pharmacokinetic evaluation from dynamic contrast-enhanced MR imaging
C. Liu (2016)
10.1148/radiol.2503081054
Triple-negative breast cancer: correlation between MR imaging and pathologic findings.
T. Uematsu (2009)
10.1148/RADIOLOGY.161.2.3763909
MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders.
D. Le Bihan (1986)
10.1016/j.diii.2014.01.002
Correlation between MR imaging - prognosis factors and molecular classification of breast cancers.
C. Alili (2014)
10.1093/jnci/djp082
Ki67 Index, HER2 Status, and Prognosis of Patients With Luminal B Breast Cancer
M. Cheang (2009)
10.1002/NBM.1940080707
Inferring microstructural features and the physiological state of tissues from diffusion‐weighted images
P. Basser (1995)
10.1002/jmri.10116
In vivo diffusion‐weighted MRI of the breast: Potential for lesion characterization
S. Sinha (2002)
Correlation of diffusion-weighted MR imaging with cellularity of renal tumours.
E. Squillaci (2004)
In vio MRI and histopathological assessment of tumor microenvironment in luminal - like and basal - like breast cancer xenografts
EM Huuse (2012)
10.1007/s11547-010-0602-4
Diffusion-weighted magnetic resonance imaging in focal breast lesions: analysis of 78 cases with pathological correlation
F. Fornasa (2010)
10.2214/AJR.08.2139
Quantitative diffusion-weighted imaging as an adjunct to conventional breast MRI for improved positive predictive value.
S. Partridge (2009)
10.1007/s12253-012-9525-9
Microvessel Density and Status of p53 Protein as Potential Prognostic Factors for Adjuvant Anthracycline Chemotherapy in Retrospective Analysis of Early Breast Cancer Patients Group
B. Biesaga (2012)
10.1148/rg.314105160
Diffusion-weighted imaging of the breast: principles and clinical applications.
R. Woodhams (2011)
IS: In vio MRI and histopathological assessment of tumor microenvironment in luminal-like and basal-like breast cancer xenografts
EM Huuse (2012)
10.2214/AJR.10.5515
Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges.
D. Koh (2011)
Expression and Significance of ER , PR , VEGF , CA 15-3 , CA 125 and CEA in Judging the Prognosis of Breast Cancer
Su-jie Zhang
10.1007/s13277-013-1588-z
Expression levels of serine/glycine metabolism-related proteins in triple negative breast cancer tissues
Songmi Noh (2013)
10.1038/nature12624
Tumour heterogeneity and cancer cell plasticity
Corbin E. Meacham (2013)
10.1148/RADIOLOGY.168.2.3393671
Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging.
D. Le Bihan (1988)
10.1007/s00428-011-1144-4
Angiogenesis in triple-negative adenoid cystic carcinomas of the breast
S. Vranic (2011)
This work is licensed under a Creative Commons Attribution-NonCommercial- NoDerivs 3.0 Licence. To view a copy of the licence please see: http://creativecommons.0rg/licenses/by-nc-nd/3.0/ INEQIMTES IN THE DELIVERY OF SERVICES TO A FEMALE FARM CLIENTELE: SOME~~
(2010)
10.1259/bjr.20160140
Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer: association with histopathological features and subtypes.
Yunju Kim (2016)
10.1002/cam4.757
HER2 status and disparities in luminal breast cancers
Andreana N Holowatyj (2016)
10.1016/j.ejro.2017.07.002
Intravoxel incoherent motion (IVIM) histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients
G. Cho (2017)
10.1016/j.ejrad.2013.08.006
Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI.
C. Liu (2013)
10.1593/NEO.81328
Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations.
A. Padhani (2009)
10.1172/JCI60534
Heterogeneity in breast cancer.
K. Polyak (2011)
Diffusion-weighted magnetic resonance imaging as a cancer
AR Padhani (2009)
10.2463/MRMS.2012-0095
Correlations between apparent diffusion coefficient values and prognostic factors of breast cancer.
T. Kamitani (2013)
10.1148/radiol.14132641
Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
M. Mazurowski (2014)
10.1007/s11604-007-0218-3
Relation between cancer cellularity and apparent diffusion coefficient values using diffusion-weighted magnetic resonance imaging in breast cancer
Miho I. Yoshikawa (2007)
10.1093/annonc/mdt303
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
A. Goldhirsch (2013)
10.1002/jmri.24799
Intravoxel incoherent motion diffusion‐weighted MR imaging of breast cancer at 3.0 tesla: Comparison of different curve‐fitting methods
S. Suo (2015)
10.1016/j.mric.2013.01.002
Breast magnetic resonance imaging: diffusion-weighted imaging.
Alice C Brandão (2013)
10.1002/mrm.22740
Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer
E. Sigmund (2011)
10.1093/annonc/mdr304
Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011
A. Goldhirsch (2011)
10.1002/jmri.21884
Diffusion‐weighted imaging of breast cancer: Correlation of the apparent diffusion coefficient value with prognostic factors
S. Kim (2009)
10.1002/jmri.24462
Intravoxel incoherent motion diffusion‐weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma
L. Bokacheva (2014)
10.1038/srep11085
Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision
L. Zhang (2015)
10.1148/radiol.14140283
Luminal-type breast cancer: correlation of apparent diffusion coefficients with the Ki-67 labeling index.
Naoko Mori (2015)
Uematsu T: MR imaging of triple-negative breast cancer breast cancer
(2011)
10.7314/APJCP.2013.14.6.3937
Expression and significance of ER, PR, VEGF, CA15-3, CA125 and CEA in judging the prognosis of breast cancer.
Su-jie Zhang (2013)
10.1016/j.diii.2013.04.010
Correlation between imaging and molecular classification of breast cancers.
M. Boisserie-Lacroix (2013)
10.1200/JCO.2013.54.1870
Luminal B breast cancer: molecular characterization, clinical management, and future perspectives.
F. Ades (2014)
10.1038/bjc.2012.581
Cancer heterogeneity: implications for targeted therapeutics
R. Fisher (2013)
10.1007/s00330-015-4087-3
Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors
G. Cho (2015)
10.1148/rg.316115515
Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up.
A. Malayeri (2011)
10.1002/jmri.22400
Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma
S. K. Jeh (2011)
10.1097/RLI.0000000000000094
Quantitative Non-Gaussian Diffusion and Intravoxel Incoherent Motion Magnetic Resonance Imaging: Differentiation of Malignant and Benign Breast Lesions
M. Iima (2015)
10.1002/mrm.23203
Integration of diffusion‐weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy
N. Atuegwu (2011)



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