Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

Diffusion MRI of the breast: Current status and future directions

During their development, cancers acquire several functional capabilities, which are defined as the hallmarks of cancer. For a deeper understanding of the hallmarks of cancer, and, consequently, improved personalized patient care, diagnostic tests must be multilayered and complex to identify the relevant underlying processes of cancer development and progression. In this context, magnetic resonance imaging (MRI) has emerged as an exceptionally powerful, versatile, and precise imaging technique. MRI of the breast is an essential tool in breast imaging, with multiple indications. Dynamic contrast-enhanced MRI (CE-MRI) is the most sensitive test for breast cancer detection, with a good specificity. CE-MRI provides mainly morphological, and, to some extent, functional information about tumor perfusion and vascularity. Recently, several functional imaging techniques in MRI, such as diffusion-weighted imaging and spectroscopy, have been assessed for breast imaging and this combined application is defined as multiparametric imaging. Furthermore, the application of higher field strengths (≥3T) has demonstrated improved sensitivity and specificity of breast cancer detection. Multiparametric imaging with different functional MRI parameters (mpMRI) visualizes and quantifies the functional processes of cancer development and progression at multiple levels, and provides specific information about the hallmarks of cancer. MpMRI of the breast improves diagnostic accuracy in breast cancer, obviates unnecessary breast biopsies, and enables an improved assessment and prediction of response to neoadjuvant therapy. This review will provide a comprehensive overview of the current possibilities and emerging techniques for mpMRI of the breast.

Level of Evidence: 5

Technical Efficacy: Stage 5

J. Magn. Reson. Imaging 2017.

Multiparametric MRI of the breast: A review

Quantitative evaluation of intravoxel incoherent motion diffusion-weighted imaging (IVIM) for differential diagnosis and grading prediction of benign and malignant breast lesions.

Differentiation Between Luminal-A and Luminal-B Breast Cancer Using Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

Objectives: The diagnostic performance of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the differential diagnosis of breast tumors remains debatable among published studies. Therefore, this meta-analysis aimed to pool relevant evidence regarding the diagnostic performance of IVIM-DWI in the differential diagnosis of breast tumors. Methods: Studies on the differential diagnosis of breast lesions using IVIM-DWI were systemically searched in the PubMed, Embase and Web of Science databases in recent 10 years. The standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f) were calculated using Review Manager 5.3, and Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as assess publication bias and heterogeneity. Fagan's nomogram was used to predict the posttest probabilities. Results: Sixteen studies comprising 1,355 malignant and 362 benign breast lesions were included. Most of these studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer had significant lower ADC (SMD = -1.38, P < 0.001) and D values (SMD = -1.50, P < 0.001), and higher f value (SMD = 0.89, P = 0.001) than benign lesions, except D* value (SMD = -0.30, P = 0.20). Invasive ductal carcinoma showed lower ADC (SMD = 1.34, P = 0.01) and D values (SMD = 1.04, P = 0.001) than ductal carcinoma in situ. D value demonstrated the best diagnostic performance (sensitivity = 86%, specificity = 86%, AUC = 0.91) and highest post-test probability (61, 48, 46, and 34% for D, ADC, f, and D* values) in the differential diagnosis of breast tumors, followed by ADC (sensitivity = 76%, specificity = 79%, AUC = 0.85), f (sensitivity = 80%, specificity = 76%, AUC = 0.85) and D* values (sensitivity = 84%, specificity = 59%, AUC = 0.71). Conclusion: IVIM-DWI parameters are adequate and superior to the ADC in the differentiation of breast tumors. ADC and D values can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. IVIM-DWI is also superior in identifying lymph node metastasis, histologic grade, and hormone receptors, and HER2 and Ki-67 status.

/pdf/intravoxel-incoherent-motion-diffusion-weighted-imaging-for-3zf17q8m0j.pdf

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

Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yangyang Li

Author Tools

Chat about Author

Papers

Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions