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

Background Various techniques are available to assess diffusion properties of breast lesions as a marker of malignancy at MRI. The diagnostic performance of these diffusion markers has not been comprehensively assessed. Purpose To compare by meta-analysis the diagnostic performance of parameters from diffusion-weighted imaging (DWI), diffusion-tensor imaging (DTI), and intravoxel incoherent motion (IVIM) in the differential diagnosis of malignant and benign breast lesions. Materials and Methods PubMed and Embase databases were searched from January to March 2018 for studies in English that assessed the diagnostic performance of DWI, DTI, and IVIM in the breast. Studies were reviewed according to eligibility and exclusion criteria. Publication bias and heterogeneity between studies were assessed. Pooled summary estimates for sensitivity, specificity, and area under the curve were obtained for each parameter by using a bivariate model. A subanalysis investigated the effect of MRI parameters on diagnostic performance by using a Student t test or a one-way analysis of variance. Results From 73 eligible studies, 6791 lesions (3930 malignant and 2861 benign) were included. Publication bias was evident for studies that evaluated apparent diffusion coefficient (ADC). Significant heterogeneity (P < .05) was present for all parameters except the perfusion fraction (f). The pooled sensitivity, specificity, and area under the curve for ADC was 89%, 82%, and 0.92, respectively. The highest performing parameter for DTI was the prime diffusion coefficient (λ1), and pooled sensitivity, specificity, and area under the curve was 93%, 90%, and 0.94, respectively. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity, specificity, and area under the curve was 88%, 79%, and 0.90. Choice of MRI parameters had no significant effect on diagnostic performance. Conclusion Diffusion-weighted imaging, diffusion-tensor imaging, and intravoxel incoherent motion have comparable diagnostic accuracy with high sensitivity and specificity. Intravoxel incoherent motion is comparable to apparent diffusion coefficient. Diffusion-tensor imaging is potentially promising but to date the number of studies is limited. © RSNA, 2019 Online supplemental material is available for this article.

/pdf/a-meta-analysis-of-the-diagnostic-performance-of-diffusion-2m0fdd43tm.pdf

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization

The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions. MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions. The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10− 3 mm2/s and the mean value of the benign lesions was 1.5 × 10− 3 mm2/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10− 3 mm2/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area). An ADC threshold of 1.00 × 10− 3 mm2/s can be recommended for distinguishing breast cancers from benign lesions.

/pdf/can-apparent-diffusion-coefficient-adc-distinguish-breast-236nklan3d.pdf

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

Differentiated thyroid carcinoma (DTC) is usually associated with a favorable prognosis. Nevertheless, up to 30% of patients present a local or distant recurrence. The aim of this study was to assess the incidence of recurrence after surgery for DTC and to identify predictive factors of recurrence. We included in this retrospective study 579 consecutive patients who underwent thyroidectomy for DTC from 2011 to 2016 at our institution. We observed biochemical or structural recurrent disease in 36 (6.2%) patients; five-year disease-free survival was 94.1%. On univariate analysis, male sex, histotype, lymph node yield, lymph node metastasis, extrathyroidal invasion and multicentricity were associated with significantly higher risk of recurrence, while microcarcinoma was correlated with significantly lower risk of recurrence. On multivariate analysis, only lymph node metastases (OR 4.724, p = 0.012) and microcarcinoma (OR 0.328, p = 0.034) were detected as independent predictive factors of recurrence. Postoperative management should be individualized and commensurate with the risk of recurrence: Patients with high-risk carcinoma should undergo strict follow-up and aggressive treatment. Furthermore, assessment of the risk should be repeated over time, considering individual response to therapy.

https://www.mdpi.com/2072-6694/11/9/1230/pdf

Predictive Factors of Recurrence in Patients with Differentiated Thyroid Carcinoma: A Retrospective Analysis on 579 Patients.

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.

Background: The aim of this study was to determine whether the indicators obtained from intravoxel incoherent motion (IVIM) imaging can improve the characterizati

/pdf/contribution-of-ivim-to-conventional-dynamic-contrast-1s9uv0joxy.pdf

Contribution of IVIM to Conventional Dynamic Contrast-Enhanced and Diffusion-Weighted MRI in Differentiating Benign from Malignant Breast Masses

BACKGROUND
Deep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated.


PURPOSE
To implement a DL method for breast cancer classification and detection using feature extraction and combination from multiple sequences.


STUDY TYPE
Retrospective.


POPULATION
A total of 569 local cases as internal cohort (50.2 ± 11.2 years; 100% female), divided among training (218), validation (73) and testing (278); 125 cases from a public dataset as the external cohort (53.6 ± 11.5 years; 100% female).


FIELD STRENGTH/SEQUENCE
T1-weighted imaging and dynamic contrast-enhanced MRI (DCE-MRI) with gradient echo sequences, T2-weighted imaging (T2WI) with spin-echo sequences, diffusion-weighted imaging with single-shot echo-planar sequence and at 1.5-T.


ASSESSMENT
A convolutional neural network and long short-term memory cascaded network was implemented for lesion classification with histopathology as the ground truth for malignant and benign categories and contralateral breasts as healthy category in internal/external cohorts. BI-RADS categories were assessed by three independent radiologists as comparison, and class activation map was employed for lesion localization in internal cohort. The classification and localization performances were assessed with DCE-MRI and non-DCE sequences, respectively.


STATISTICAL TESTS
Sensitivity, specificity, area under the curve (AUC), DeLong test, and Cohen's kappa for lesion classification. Sensitivity and mean squared error for localization. A P-value <0.05 was considered statistically significant.


RESULTS
With the optimized mpMRI combinations, the lesion classification achieved an AUC = 0.98/0.91, sensitivity = 0.96/0.83 in the internal/external cohorts, respectively. Without DCE-MRI, the DL-based method was superior to radiologists' readings (AUC 0.96 vs. 0.90). The lesion localization achieved sensitivities of 0.97/0.93 with DCE-MRI/T2WI alone, respectively.


DATA CONCLUSION
The DL method achieved high accuracy for lesion detection in the internal/external cohorts. The classification performance with a contrast agent-free combination is comparable to DCE-MRI alone and the radiologists' reading in AUC and sensitivity.


EVIDENCE LEVEL
3.


TECHNICAL EFFICACY
Stage 2.

MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning.

Purpose 
The aim of the study was to evaluate the role of high b-value (2000 sec/mm2) diffusion-weighted imaging (DWI) by using Readout Segmentation of Long Variable Echo-trains (RESOLVE) in differentiating papillary thyroid carcinomas (PTCs) and papillary thyroid microcarcinomas (PTMCs) from benign thyroid nodules.


Materials and methods 
Consecutive patients with thyroid nodules scheduled for surgery underwent high b-value DWI with 3 b-values: 0, 800 and 2000 sec/mm2. Signal intensity ratios (SIRs) of thyroid nodules to adjacent normal thyroid tissue on DWI were measured as: SIRb0, SIRb800 and SIRb2000. Apparent diffusion coefficient (ADC) values based on the 3 different b-values were acquired as: ADCb0-800, ADCb0-2000, and ADCb0-800-2000. The 6 diagnostic indicators were evaluated by receiver operating characteristic (ROC) and diagnostic ability was compared between high b-value DWI and Ultrasound (US).


Results 
A total of 52 PTCs including 33 PTMCs (38 patients, 8 men and 30 women, aged 45.68 ± 11.93 years) and 62 benign thyroid nodules (46 patients, 7 men and 39 women, aged 48.73 ± 11.98 years) were enrolled into the final statistical analysis. ADCb0-800-2000 had the highest diagnostic ability in differentiating PTCs from benign thyroid nodules with area under curve (AUC) of 0.944, sensitivity of 96.15% and specificity of 85.48%, and PTMCs from benign thyroid nodules with AUC of 0.940, sensitivity of 93.94% and specificity of 85.48%. On the strength of lower false-positive rates than US (14.52% vs. 32.26% for PTCs and 14.52% vs. 32.26% for PTMCs), ADCb0-800-2000 had significantly better diagnostic ability in PTCs (P = 0.002) and PTMCs (P = 0.005).


Conclusion 
High b-value (2000 sec/mm2) DWI can contribute to differentiating PTCs and PTMCs from benign thyroid nodules and can be potentially used as an active surveillance imaging method for PTMCs.

/pdf/utility-of-high-b-value-2000-sec-mm2-dwi-with-resolve-in-1ocuqxyjkw.pdf

Utility of high b-value (2000 sec/mm2) DWI with RESOLVE in differentiating papillary thyroid carcinomas and papillary thyroid microcarcinomas from benign thyroid nodules

Diagnostic value of high b-value (2000 s/mm2) DWI for thyroid micronodules

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Papers

Contribution of IVIM to Conventional Dynamic Contrast-Enhanced and Diffusion-Weighted MRI in Differentiating Benign from Malignant Breast Masses

MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning.

Utility of high b-value (2000 sec/mm2) DWI with RESOLVE in differentiating papillary thyroid carcinomas and papillary thyroid microcarcinomas from benign thyroid nodules

Diagnostic value of high b-value (2000 s/mm2) DWI for thyroid micronodules