Literature DB >> 30238533

Intravoxel incoherent motion diffusion-weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer-aided diagnosis.

Sung Eun Song1, Kyu Ran Cho1, Bo Kyoung Seo2, Ok Hee Woo3, Kyong Hwa Park4, Yo Han Son5, Robert Grimm6.   

Abstract

BACKGROUND: As both intravoxel incoherent motion (IVIM) modeling and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide perfusion parameters, IVIM-derived perfusion parameters might be expected to correlate with the kinetic features from DCE-MRI.
PURPOSE: To investigate the association between IVIM parameters and prognostic factors and to evaluate the correlation between IVIM parameters and kinetic features in invasive breast cancer patients using computer-aided diagnosis (CAD). STUDY TYPE: Retrospective. POPULATION: Eighty-five patients (invasive cancers; mean size, 1.8 cm; range, 0.8-4.8 cm) who underwent diffusion-weighted imaging with 12 b-values (0-1000 s/mm2 ). FIELD STRENGTH/SEQUENCE: 3.0T MRI axial, IVIM-DWI epi-sequence, and DCE-MRI. ASSESSMENT: Two radiologists measured the apparent diffusion coefficient (ADC), diffusion coefficient, pseudodiffusion coefficient, and perfusion fraction (f) using IVIM modeling. Kinetic features such as peak enhancement and early and delayed enhancement profiles were acquired using CAD. STATISTICAL TESTS: The correlation between the IVIM parameters and kinetic features and the association between the IVIM parameters and prognostic factors were investigated using Mann-Whitney test and Spearman correlation test.
RESULTS: There were no significant associations between IVIM parameters and prognostic factors. When IVIM parameters were correlated with kinetic features by CAD, both the ADC and f values showed correlations with delayed enhancement profiles. The ADC values were lower in tumors with lower persistent components (P = 0.013) and higher washout components (P = 0.045) and showed a positive correlation with persistent proportion (Spearman's rho (r) = 0.222, P = 0.041). The f value was higher in tumors with higher persistent components (P = 0.021) and showed a positive correlation with persistent proportion (r = 0.227, P = 0.029). DATA
CONCLUSION: This analysis revealed that IVIM-derived ADC and f values showed correlations with kinetic features at the delayed phase as assessed by CAD. These results indicate the potential of IVIM imaging biomarkers to provide information on the biological and kinetic properties of breast cancers without a contrast agent. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:118-130.
© 2018 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  breast neoplasm; diffusion magnetic resonance imaging; perfusion

Mesh:

Substances:

Year:  2018        PMID: 30238533     DOI: 10.1002/jmri.26221

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


  9 in total

1.  Diffusion-weighted MRI for Unenhanced Breast Cancer Screening.

Authors:  Nita Amornsiripanitch; Sebastian Bickelhaupt; Hee Jung Shin; Madeline Dang; Habib Rahbar; Katja Pinker; Savannah C Partridge
Journal:  Radiology       Date:  2019-10-08       Impact factor: 11.105

2.  Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis.

Authors:  Hans-Jonas Meyer; Andreas Wienke; Alexey Surov
Journal:  Breast Care (Basel)       Date:  2021-02-23       Impact factor: 2.860

3.  Characterization of the diffusion signal of breast tissues using multi-exponential models.

Authors:  Ana E Rodríguez-Soto; Maren M Sjaastad Andreassen; Lauren K Fang; Christopher C Conlin; Helen H Park; Grace S Ahn; Hauke Bartsch; Joshua Kuperman; Igor Vidić; Haydee Ojeda-Fournier; Anne M Wallace; Michael Hahn; Tyler M Seibert; Neil Peter Jerome; Agnes Østlie; Tone Frost Bathen; Pål Erik Goa; Rebecca Rakow-Penner; Anders M Dale
Journal:  Magn Reson Med       Date:  2021-12-14       Impact factor: 3.737

4.  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

Review 5.  Diffusion-Weighted Magnetic Resonance Imaging of the Breast: Standardization of Image Acquisition and Interpretation.

Authors:  Su Hyun Lee; Hee Jung Shin; Woo Kyung Moon
Journal:  Korean J Radiol       Date:  2020-08-28       Impact factor: 3.500

6.  Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes.

Authors:  Zhiqi Yang; Xiaofeng Chen; Tianhui Zhang; Fengyan Cheng; Yuting Liao; Xiangguan Chen; Zhuozhi Dai; Weixiong Fan
Journal:  Front Oncol       Date:  2021-09-16       Impact factor: 6.244

7.  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

Review 8.  Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review.

Authors:  Toshiki Kazama; Taro Takahara; Jun Hashimoto
Journal:  Life (Basel)       Date:  2022-03-28

9.  Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Quantitative Differentiation of Breast Tumors: A Meta-Analysis.

Authors:  Jianye Liang; Sihui Zeng; Zhipeng Li; Yanan Kong; Tiebao Meng; Chunyan Zhou; Jieting Chen; YaoPan Wu; Ni He
Journal:  Front Oncol       Date:  2020-10-20       Impact factor: 6.244

  9 in total

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