Literature DB >> 30260589

Diffusion Kurtosis at 3.0T as an in vivo Imaging Marker for Breast Cancer Characterization: Correlation With Prognostic Factors.

Yao Huang1, Yan Lin1, Wei Hu1, Changchun Ma2, Weixun Lin3, Zhening Wang1, Jiahao Liang1, Wei Ye1, Jiayun Zhao1, Renhua Wu1.   

Abstract

BACKGROUND: Diffusion-kurtosis imaging (DKI) has preliminarily shown promise as a relatively new MRI technique to provide useful information regarding breast lesions, but the diagnostic performance of DKI has not been fully evaluated.
PURPOSE: To compare the diagnostic accuracy of DKI, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI) and proton MR spectroscopy (1 H-MRS) in differentiating malignant from benign breast lesions independently or jointly, and explore the correlation between DKI-derived parameters and prognostic factors. STUDY TYPE: Prospective.
SUBJECTS: Seventy-one patients with breast lesions (50 malignant, 26 benign). SEQUENCE: DKI, DWI, DCE-MRI, and 1 H-MRS were performed at 3.0T. ASSESSMENT: Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), BI-RADS category, and choline peaks were analyzed by two experienced radiologists. STATISTICAL TESTS: Student's t-test was used for continuous variables; receiver operating characteristic (ROC) analysis for assessing the diagnostic accuracy of imaging parameters; Spearman or Pearson correlations for assessing the associations between imaging parameters and prognostic factors.
RESULTS: MK exhibited higher area under the curves (AUCs) for differentiating malignant from benign lesions than did MD, ADC, DCE, and tCho (0.979 vs. 0.928, 0.911, 0.777, and 0.833, respectively, P < 0.05). MK showed a positive association with Ki-67 expression (r = 0.508) and histologic grades (r = 0.551), whereas MD and ADC were negatively correlated with Ki-67 expression (r = -0.416 and r = -0.458) and histologic grades (r = -0.411 and r = -0.319). Moreover, MK showed relatively higher AUCs compared with MD and ADC in detecting breast cancers with lymph nodal involvement, histologic grades, and Ki-67 expression. DATA
CONCLUSION: MK has higher diagnostic accuracy compared with ADC, DCE, and tCho regarding detection of breast cancer. Moreover, DKI shows promise as a quantitative imaging technique for characterizing breast lesions, highlighting the potential utility of MK as a promising imaging marker for predicting tumor aggressiveness. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:845-856.
© 2018 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  MRI; breast cancer; diffusion kurtosis; risk stratification

Year:  2018        PMID: 30260589     DOI: 10.1002/jmri.26249

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  15 in total

1.  Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer.

Authors:  Eun Cho; Hye Jin Baek; Filip Szczepankiewicz; Hyo Jung An; Eun Jung Jung; Ho-Joon Lee; Joonsung Lee; Sung-Min Gho
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2.  Three-dimensional pulsed continuous arterial spin labeling and intravoxel incoherent motion imaging of nasopharyngeal carcinoma: correlations with Ki-67 proliferation status.

Authors:  Wenxiu Wu; Guihua Jiang; Zhifeng Xu; Ruoning Wang; Aizhen Pan; Mingyong Gao; Tian Yu; Linwen Huang; Qiang Quan; Jin Li
Journal:  Quant Imaging Med Surg       Date:  2021-04

3.  Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions.

Authors:  Alexey Surov; Hans Jonas Meyer; Andreas Wienke
Journal:  BMC Cancer       Date:  2019-10-15       Impact factor: 4.430

4.  XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses.

Authors:  Wan Tang; Han Zhou; Tianhong Quan; Xiaoyan Chen; Huanian Zhang; Yan Lin; Renhua Wu
Journal:  Front Oncol       Date:  2022-03-17       Impact factor: 6.244

5.  Prediction of Prognostic Factors and Genotypes in Patients With Breast Cancer Using Multiple Mathematical Models of MR Diffusion Imaging.

Authors:  Weiwei Wang; Xindong Zhang; Laimin Zhu; Yueqin Chen; Weiqiang Dou; Fan Zhao; Zhe Zhou; Zhanguo Sun
Journal:  Front Oncol       Date:  2022-01-31       Impact factor: 6.244

6.  Evaluation of intratumoral heterogeneity by using diffusion kurtosis imaging and stretched exponential diffusion-weighted imaging in an orthotopic hepatocellular carcinoma xenograft model.

Authors:  Ran Guo; Shuo-Hui Yang; Fang Lu; Zhi-Hong Han; Xu Yan; Cai-Xia Fu; Meng-Long Zhao; Jiang Lin
Journal:  Quant Imaging Med Surg       Date:  2019-09

7.  Preliminary study on identification of estrogen receptor-positive breast cancer subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) texture analysis.

Authors:  Hui Wang; Yunting Hu; Hui Li; Yuanliang Xie; Xiang Wang; Weijia Wan
Journal:  Gland Surg       Date:  2020-06

8.  The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis.

Authors:  Zhipeng Li; Xinming Li; Chuan Peng; Wei Dai; Haitao Huang; Xie Li; Chuanmiao Xie; Jianye Liang
Journal:  Front Oncol       Date:  2020-10-27       Impact factor: 6.244

9.  Influence of residual fat signal on diffusion kurtosis MRI of suspicious mammography findings.

Authors:  Anna Mlynarska-Bujny; Sebastian Bickelhaupt; Frederik Bernd Laun; Franziska König; Wolfgang Lederer; Heidi Daniel; Mark Edward Ladd; Heinz-Peter Schlemmer; Stefan Delorme; Tristan Anselm Kuder
Journal:  Sci Rep       Date:  2020-08-06       Impact factor: 4.379

10.  Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study.

Authors:  Isaac Daimiel Naranjo; Alexis Reymbaut; Patrik Brynolfsson; Roberto Lo Gullo; Karin Bryskhe; Daniel Topgaard; Dilip D Giri; Jeffrey S Reiner; Sunitha B Thakur; Katja Pinker-Domenig
Journal:  Cancers (Basel)       Date:  2021-03-31       Impact factor: 6.639

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