The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications. Key Points • The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use. • Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters. • Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.

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Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group.

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

Neoadjuvant therapy (NAT) has been used increasingly in patients with locally advanced or early-stage breast cancer. However, the accurate evaluation and prediction of response to NAT remain the great challenge. Biomarkers could prove useful to identify responders or nonresponders, or even to distinguish between early and delayed responses. These biomarkers could include markers from the tumor itself, such as versatile proteins, genes, and ribonucleic acids, various biological factors or peripheral blood cells, and clinical and pathological features. Possible predictive markers could also include multiple features from functional imaging, such as standard uptake values in positron emission tomography, apparent diffusion coefficient in magnetic resonance, or radiomics imaging biomarkers. In addition, cells that indirectly present the immune status of tumor cells and/or their host could also potentially be used as biomarkers, eg, tumor-infiltrating lymphocytes, tumor-associated macrophages, and myeloid-derived suppressor cells. Though numerous biomarkers have been widely investigated, only estrogen and/or progesterone receptors and human epidermal growth factor receptor have been proven to be reliable biomarkers to predict the response to NAT. They are the only biomarkers recommended in several international guidelines. The other aforementioned biomarkers warrant further validation studies. Some multigene profiling assays that are commercially available, eg, Oncotype DX and MammaPrint, should be used with caution when extrapolated to NAT settings. A panel of combined multilevel biomarkers might be able to predict the response to NAT more robustly than individual biomarkers. To establish such a panel and its prediction model, reliable methods and extensive clinical validation are warranted.

/pdf/predicting-the-response-to-neoadjuvant-therapy-for-early-47n2g667af.pdf

Predicting the response to neoadjuvant therapy for early-stage breast cancer: tumor-, blood-, and imaging-related biomarkers.

Diffusion tensor imaging parameters in differentiation recurrent breast cancer from post-operative changes in patients with breast-conserving surgery.

The goals of imaging after neoadjuvant therapy for breast cancer are to monitor the response to therapy and facilitate surgical planning. MRI has been found to be more accurate than mammography, ultrasound, or clinical exam in evaluating treatment response. However, MRI may both overestimate and underestimate residual disease. The accuracy of MRI is dependent on tumor morphology, histology, shrinkage pattern, and molecular subtype. Emerging MRI techniques that combine functional information such as diffusion, metabolism, and hypoxia may improve MR accuracy. In addition, machine-learning techniques including radiomics and radiogenomics are being studied with the goal of predicting response on pretreatment imaging. This article comprehensively reviews response assessment on breast MRI and highlights areas of ongoing research. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3 J. MAGN. RESON. IMAGING 2020;52:1587-1606.

Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer

Background
Diffusion tensor imaging (DTI) yields several parameters that have not been tested in response evaluation to neoadjuvant chemotherapy (NAC).

Purpose
To evaluate and compare in reference to histopathology findings the ability of DTI and dynamic contrast-enhanced (DCE) MRI to monitor response to NAC.

Study Type
Retrospective.

Population
Twenty patients treated with neoadjuvant chemotherapy.

Field Strength/Sequence
1.5T MRI axial, bilateral T2-weighted, DTI, and DCE-MRI.

Assessment
A standardized blinded image analysis at pixel resolution generated color-coded maps of DTI and DCE parameters

Statistical Tests
Pearson's correlation analysis and Bland–Altman plots of the DTI and DCE size changes and of the pathological final residual tumor diameter and DCE or DTI final diameter, from pre- to post-NAC. Spearman coefficient of rank correlation between the DTI and DCE size changes from pre- to post-NAC and Miller and Payne (M&P) pathological response grading. Receiver operating characteristic curve analyses to differentiate between responders to nonresponders on the basis of the DTI and DCE percent size changes and the changes in DTI parameters.

Results
DTI and DCE changes in the cancers' diameter and volume from pre- to post-NAC exhibited high and significant Pearson correlation (r = 0.82 P = 1.2 × 10−5). The DTI volume changes exhibited a significant Spearman coefficient rank correlation (0.68, P = 0.001) with the pathological M&P grading and differentiated between responders and nonresponders with area-under-the-curve (AUC) of 0.83 ± 0.10. A similar AUC for differentiating responders from nonresponders was exhibited by the changes in the highest diffusion coefficient (0.84 ± 0.11) and the mean diffusivity (0.83 ± 0.11). The DTI residual-tumor-diameter showed a high and significant Pearson correlation (r = 0.87 P = 1.2 × 10−6) to pathology tumor diameter.

Data Conclusion
DTI monitors changes in cancer size and diffusion tensor parameters in response to NAC with an accuracy equivalent to that of DCE, enabling differentiation of responders from nonresponders and assessment of residual tumor size in high congruence with pathology.

Level of Evidence: 1

Technical Efficacy: Stage 4

J. Magn. Reson. Imaging 2017.

Carmen Martinez‐Rubio

Papers

Quantitative evaluation of breast cancer response to neoadjuvant chemotherapy by diffusion tensor imaging: Initial results