Diffusion-weighted MRI: association between patient characteristics and apparent diffusion coefficients of normal breast fibroglandular tissue at 3 T. AJR Am J Roentgenol. 2014;202:W496-W502. [PMID: 24758685 DOI: 10.2214/ajr.13.11159]

Predicting mammographic density with linear ultrasound transducers

BACKGROUND

High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments.

METHODS

We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON® 730 Expert system (GE Medical Systems Kretztechnik GmbH & Co OHG, Austria). Gray level bins were calculated from ultrasound images to characterize mammographic density. Percentage mammographic density (PMD) was predicted by gray level bins using various regression models.

RESULTS

Gray level bins and PMD correlated to a certain extent. Spearman's ρ ranged from - 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R2, 0.255). Overall, ultrasound images from the VOLUSON® 730 Expert device in this study showed limited predictive power for PMD when correlated with the corresponding mammograms.

CONCLUSIONS

In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).

Multishot Diffusion-Weighted MRI of the Breasts in the Supine vs. Prone Position

BACKGROUND

Diffusion-weighted imaging (DWI) may allow for breast cancer screening MRI without a contrast injection. Multishot methods improve prone DWI of the breasts but face different challenges in the supine position.

PURPOSE

To establish a multishot DWI (msDWI) protocol for supine breast MRI and to evaluate the performance of supine vs. prone msDWI.

STUDY TYPE

Prospective.

POPULATION

Protocol optimization: 10 healthy women (ages 22-56), supine vs. prone: 24 healthy women (ages 22-62) and five women (ages 29-61) with breast tumors.

FIELD STRENGTH/SEQUENCE

3-T, protocol optimization msDWI: free-breathing (FB) 2-shots, FB 4-shots, respiratory-triggered (RT) 2-shots, RT 4-shots, supine vs. prone: RT 4-shot msDWI, T2-weighted fast-spin echo.

ASSESSMENT

Protocol optimization and supine vs. prone: three observers performed an image quality assessment of sharpness, aliasing, distortion (vs. T2), perceived SNR, and overall image quality (scale of 1-5). Apparent diffusion coefficients (ADCs) in fibroglandular tissue (FGT) and breast tumors were measured.

STATISTICAL TESTS

Effect of study variables on dichotomized ratings (4/5 vs. 1/2/3) and FGT ADCs were assessed with mixed-effects logistic regression. Interobserver agreement utilized Gwet's agreement coefficient (AC). Lesion ADCs were assessed by Bland-Altman analysis and concordance correlation (ρc ). P value <0.05 was considered statistically significant.

RESULTS

Protocol optimization: 4-shots significantly improved sharpness and distortion; RT significantly improved sharpness, aliasing, perceived SNR, and overall image quality. FGT ADCs were not significantly different between shots (P = 0.812), FB vs. RT (P = 0.591), or side (P = 0.574). Supine vs. prone: supine images were rated significantly higher for sharpness, aliasing, and overall image quality. FGT ADCs were significantly higher supine; lesion ADCs were highly correlated (ρc  = 0.92).

DATA CONCLUSION

Based on image quality, supine msDWI outperformed prone msDWI. Lesion ADCs were highly correlated between the two positions, while FGT ADCs were higher in the supine position.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 1.

Breast density is strongly associated with multiparametric magnetic resonance imaging biomarkers and pro-tumorigenic proteins in situ

BACKGROUND

High mammographic density is an independent risk factor for breast cancer by poorly understood molecular mechanisms. Women with dense breasts often undergo conventional magnetic resonance imaging (MRI) despite its limited specificity, which may be increased by diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) and contrast. How these modalities are affected by breast density per se and their association with the local microenvironment are undetermined.

METHODS

Healthy postmenopausal women attending mammography screen with extremely dense or entirely fatty breasts underwent multiparametric MRI for analyses of lean tissue fraction (LTF), ADC and perfusion dynamics. Microdialysis was used for extracellular proteomics in situ.

RESULTS

Significantly increased LTF and ADC and delayed perfusion were detected in dense breasts. In total, 270 proteins were quantified, whereof 124 related to inflammation, angiogenesis, and cellular growth were significantly upregulated in dense breasts. Most of these correlated significantly with LTF, ADC and the perfusion data.

CONCLUSIONS

ADC and perfusion characteristics depend on breast density, which should be considered during the implementation of thresholds for malignant lesions. Dense and nondense breasts are two essentially different biological entities, with a pro-tumorigenic microenvironment in dense breasts. Our data reveal several novel pathways that may be explored for breast cancer prevention strategies.

Portable NMR for quantification of breast density in vivo: Proof-of-concept measurements and comparison with quantitative MRI

Mammographic Density (MD) is the degree of radio-opacity of the breast in an X-ray mammogram. It is determined by the Fibroglandular: Adipose tissue ratio. MD has major implications in breast cancer risk and breast cancer chemoprevention. This study aimed to investigate the feasibility of accurate, low-cost quantification of MD in vivo without ionising radiation. We used single-sided portable nuclear magnetic resonance ("Portable NMR") due to its low cost and the absence of radiation-related safety concerns. Fifteen (N = 15) healthy female volunteers were selected for the study and underwent an imaging routine consisting of 2D X-ray mammography, quantitative breast 3T MRI (Dixon and T₁-based 3D compositional breast imaging), and 1D compositional depth profiling of the right breast using Portable NMR. For each participant, all the measurements were made within 3-4 h of each other. MRI-determined tissue water content was used as the MD-equivalent quantity. Portable NMR depth profiles of tissue water were compared with the equivalent depth profiles reconstructed from Dixon and T₁-based MR images, which were used as the MD-equivalent reference standard. The agreement between the depth profiles acquired using Portable NMR and the reconstructed reference-standard profiles was variable but overall encouraging. The agreement was somewhat inferior to that seen in breast tissue explant measurements conducted in vitro, where quantitative micro-CT was used as the reference standard. The lower agreement in vivo can be attributed to an uncertainty in the positioning of the Portable NMR sensor on the breast surface and breast compression in Portable NMR measurements. The degree of agreement between Portable NMR and quantitative MRI is encouraging. While the results call for further development of quantitative Portable NMR, they demonstrate the in-principle feasibility of Portable NMR-based quantitative compositional imaging in vivo and show promise for the development of safe and low-cost protocols for quantification of MD suitable for clinical applications.

Diffusion Breast MRI: Current Standard and Emerging Techniques

The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

Tri-Compartmental Restriction Spectrum Imaging Breast Model Distinguishes Malignant Lesions from Benign Lesions and Healthy Tissue on Diffusion-Weighted Imaging

Diffusion-weighted MRI (DW-MRI) offers a potential adjunct to dynamic contrast-enhanced MRI to discriminate benign from malignant breast lesions by yielding quantitative information about tissue microstructure. Multi-component modeling of the DW-MRI signal over an extended b-value range (up to 3000 s/mm²) theoretically isolates the slowly diffusing (restricted) water component in tissues. Previously, a three-component restriction spectrum imaging (RSI) model demonstrated the ability to distinguish malignant lesions from healthy breast tissue. We further evaluated the utility of this three-component model to differentiate malignant from benign lesions and healthy tissue in 12 patients with known malignancy and synchronous pathology-proven benign lesions. The signal contributions from three distinct diffusion compartments were measured to generate parametric maps corresponding to diffusivity on a voxel-wise basis. The three-component model discriminated malignant from benign and healthy tissue, particularly using the restricted diffusion C₁ compartment and product of the restricted and intermediate diffusion compartments (C₁ and C₂). However, benign lesions and healthy tissue did not significantly differ in diffusion characteristics. Quantitative discrimination of these three tissue types (malignant, benign, and healthy) in non-pre-defined lesions may enhance the clinical utility of DW-MRI in reducing excessive biopsies and aiding in surveillance and surgical evaluation without repeated exposure to gadolinium contrast.

Development of patient-specific 3D-printed breast phantom using silicone and peanut oils for magnetic resonance imaging

Background

Despite increasing reports of 3D printing in medical applications, the use of 3D printing in breast imaging is limited, thus, personalized 3D-printed breast model could be a novel approach to overcome current limitations in utilizing breast magnetic resonance imaging (MRI) for quantitative assessment of breast density. The aim of this study is to develop a patient-specific 3D-printed breast phantom and to identify the most appropriate materials for simulating the MR imaging characteristics of fibroglandular and adipose tissues.

Methods

A patient-specific 3D-printed breast model was generated using 3D-printing techniques for the construction of the hollow skin and fibroglandular region shells. Then, the T1 relaxation times of the five selected materials (agarose gel, silicone rubber with/without fish oil, silicone oil, and peanut oil) were measured on a 3T MRI system to determine the appropriate ones to represent the MR imaging characteristics of fibroglandular and adipose tissues. Results were then compared to the reference values of T1 relaxation times of the corresponding tissues: 1,324.42±167.63 and 449.27±26.09 ms, respectively. Finally, the materials that matched the T1 relaxation times of the respective tissues were used to fill the 3D-printed hollow breast shells.

Results

The silicone and peanut oils were found to closely resemble the T1 relaxation times and imaging characteristics of these two tissues, which are 1,515.8±105.5 and 405.4±15.1 ms, respectively. The agarose gel with different concentrations, ranging from 0.5 to 2.5 wt%, was found to have the longest T1 relaxation times.

Conclusions

A patient-specific 3D-printed breast phantom was successfully designed and constructed using silicone and peanut oils to simulate the MR imaging characteristics of fibroglandular and adipose tissues. The phantom can be used to investigate different MR breast imaging protocols for the quantitative assessment of breast density.

High-Spatial-Resolution Multishot Multiplexed Sensitivity-encoding Diffusion-weighted Imaging for Improved Quality of Breast Images and Differentiation of Breast Lesions: A Feasibility Study

Multishot multiplexed sensitivity-encoding diffusion-weighted imaging is a feasible and easily implementable routine breast MRI protocol that yields high-quality diffusion-weighted breast images.Purpose: To compare multiplexed sensitivity-encoding (MUSE) diffusion-weighted imaging (DWI) and single-shot DWI for lesion visibility and differentiation of malignant and benign lesions within the breast.Materials and Methods: In this prospective institutional review board-approved study, both MUSE DWI and single-shot DWI sequences were first optimized in breast phantoms and then performed in a group of patients. Thirty women (mean age, 51.1 years ± 10.1 [standard deviation]; age range, 27-70 years) with 37 lesions were included in this study and underwent scanning using both techniques. Visual qualitative analysis of diffusion-weighted images was accomplished by two independent readers; images were assessed for lesion visibility, adequate fat suppression, and the presence of artifacts. Quantitative analysis was performed by calculating apparent diffusion coefficient (ADC) values and image quality parameters (signal-to-noise ratio [SNR] for lesions and fibroglandular tissue; contrast-to-noise ratio) by manually drawing regions of interest within the phantoms and breast tumor tissue. Interreader variability was determined using the Cohen κ coefficient, and quantitative differences between MUSE DWI and single-shot DWI were assessed using the Mann-Whitney U test; significance was defined at P < .05.Results: MUSE DWI yielded significantly improved image quality compared with single-shot DWI in phantoms (SNR, P = .001) and participants (lesion SNR, P = .009; fibroglandular tissue SNR, P = .05; contrast-to-noise ratio, P = .008). MUSE DWI ADC values showed a significant difference between malignant and benign lesions (P < .001). No significant differences were found between MUSE DWI and single-shot DWI in the mean, maximum, and minimum ADC values (P = .96, P = .28, and P = .49, respectively). Visual qualitative analysis resulted in better lesion visibility for MUSE DWI over single-shot DWI (κ = 0.70).Conclusion: MUSE DWI is a promising high-spatial-resolution technique that may enhance breast MRI protocols without the need for contrast material administration in breast screening.Keywords: Breast, MR-Diffusion Weighted Imaging, OncologySupplemental material is available for this article.© RSNA, 2020.

Correlations of female hormone levels with background parenchymal enhancement and apparent diffusion coefficient values in premenopausal breast cancer patients: Effects on cancer visibility

PURPOSE

To evaluate the relationships between female hormone levels and background parenchymal enhancement (BPE) or apparent diffusion coefficient (ADC) values of breast parenchyma, as well as the effects of BPE and ADC values on cancer visibility.

METHODS

This prospective study was performed in 164 consecutive premenopausal patients who were diagnosed with invasive breast cancer from November 2016 to December 2018. Two radiologists analyzed the qualitative, quantitative BPE and ADC values of normal contralateral breast parenchyma. We also analyzed the cancer visibility using a three-point scale (0: no visibility, 1: slight visibility, 2: excellent visibility).

RESULTS

The progesterone level was significantly correlated with qualitative BPE grade and quantitative values of the BPE, as well as with the mean ADC. On contrast enhanced image (CEI), the visibility score was significantly associated with tumor size, qualitative and quantitative BPE. On diffusion weighted image (DWI), tumor size was significantly associated with the visibility score, whereas the ADC value was not. Of four lesions with a score of 0 on CEI, three had a score of 2 and one a score of 1 on DWI. Regarding the visibility score on DWI, tumor size and histologic type were significantly different among the three groups.

CONCLUSIONS

Qualitative or quantitative BPE of breast parenchyma was positively correlated with the progesterone level and the mean ADC was negatively correlated. The cancer visibility was affected by BPE on CEI, but not by ADC on DWI. Small-sized cancer and invasive lobular cancer could be the causes of false-negative diagnoses on DWI.

Multiparametric 18F-FDG PET/MRI of the Breast: Are There Differences in Imaging Biomarkers of Contralateral Healthy Tissue Between Patients With and Without Breast Cancer?

The rationale was to assess whether there are differences in multiparametric 18F-FDG PET/MRI biomarkers of contralateral healthy breast tissue in patients with benign and malignant breast tumors. Methods: In this institutional review board-approved prospective single-institution study, 141 women with imaging abnormalities on mammography or sonography (BI-RADS 4/5) underwent combined 18F-FDG PET/MRI of the breast at 3T with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and the radiotracer 18F-FDG. In all patients, the following imaging biomarkers were recorded for the contralateral (tumor-free) breast: breast parenchymal uptake (BPU) (from 18F-FDG PET), mean apparent diffusion coefficient (from diffusion-weighted imaging), background parenchymal enhancement (BPE), and amount of fibroglandular tissue (FGT) (from MRI). Appropriate statistical tests were used to assess differences in 18F-FDG PET/MRI biomarkers between patients with benign and malignant lesions. Results: There were 100 malignant and 41 benign lesions. BPE was minimal in 61 patients, mild in 56, moderate in 19, and marked in 5. BPE differed significantly (P < 0.001) between patients with benign and malignant lesions, with patients with cancer demonstrating decreased BPE in the contralateral tumor-free breast. FGT approached but did not reach significance (P = 0.055). BPU was 1.5 for patients with minimal BPE, 1.9 for mild BPE, 2.2 for moderate BPE, and 1.9 for marked BPE. BPU differed significantly between patients with benign lesions (mean, 1.9) and patients with malignant lesions (mean, 1.8) (P < 0.001). Mean apparent diffusion coefficient did not differ between groups (P = 0.19). Conclusion: Differences in multiparametric 18F-FDG PET/MRI biomarkers, obtained from contralateral tumor-free breast tissue, exist between patients with benign and patients with malignant breast tumors. Contralateral BPE, BPU, and FGT are decreased in breast cancer patients and may potentially serve as imaging biomarkers for the presence of malignancy.

A multiparametric [18F]FDG PET/MRI diagnostic model including imaging biomarkers of the tumor and contralateral healthy breast tissue aids breast cancer diagnosis

Purpose

To develop a multiparametric [¹⁸F]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue.

Methods

In this prospective study and retrospective data analysis, 141 patients (mean 57 years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [¹⁸F]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3 T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [¹⁸F]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC).

Results

There were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5–10 cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P = 0.0003), tumor ADCmean (P < 0.001), and BPE of the contralateral healthy breast (P = 0.0019) as independent predictors for breast cancer diagnosis. Other biomarkers did not reach significance. Combination of the three significant biomarkers achieved an AUC value of 0.98 for breast cancer diagnosis.

Conclusion

A multiparametric [¹⁸F]FDG PET/MRI diagnostic model incorporating both qualitative and quantitative parameters of the tumor and the healthy contralateral tissue aids breast cancer diagnosis.

Quantitative Measurements of Breast Density Using Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis

Breast density, a measure of dense fibroglandular tissue relative to non-dense fatty tissue, is confirmed as an independent risk factor of breast cancer. Although there has been an increasing interest in the quantitative assessment of breast density, no research has investigated the optimal technical approach of breast MRI in this aspect. Therefore, we performed a systematic review and meta-analysis to analyze the current studies on quantitative assessment of breast density using MRI and to determine the most appropriate technical/operational protocol. Databases (PubMed, EMBASE, ScienceDirect, and Web of Science) were searched systematically for eligible studies. Single arm meta-analysis was conducted to determine quantitative values of MRI in breast density assessments. Combined means with their 95% confidence interval (CI) were calculated using a fixed-effect model. In addition, subgroup meta-analyses were performed with stratification by breast density segmentation/measurement method. Furthermore, alternative groupings based on statistical similarities were identified via a cluster analysis employing study means and standard deviations in a Nearest Neighbor/Single Linkage. A total of 38 studies matched the inclusion criteria for this systematic review. Twenty-one of these studies were judged to be eligible for meta-analysis. The results indicated, generally, high levels of heterogeneity between study means within groups and high levels of heterogeneity between study variances within groups. The studies in two main clusters identified by the cluster analysis were also subjected to meta-analyses. The review confirmed high levels of heterogeneity within the breast density studies, considered to be due mainly to the applications of MR breast-imaging protocols and the use of breast density segmentation/measurement methods. Further research should be performed to determine the most appropriate protocol and method for quantifying breast density using MRI.

Transverse relaxation-based assessment of mammographic density and breast tissue composition by single-sided portable NMR

PURPOSE

Elevated mammographic density (MD) is an independent risk factor for breast cancer (BC) as well as a source of masking in X-ray mammography. High-frequency longitudinal monitoring of MD could also be beneficial in hormonal BC prevention, where early MD changes herald the treatment's success. We present a novel approach to quantification of MD in breast tissue using single-sided portable NMR. Its development was motivated by the low cost of portable-NMR instrumentation, the suitability for measurements in vivo, and the absence of ionizing radiation.

METHODS

Five breast slices were obtained from three patients undergoing prophylactic mastectomy or breast reduction surgery. Carr-Purcell-Meiboom-Gill (CPMG) relaxation curves were measured from (1) regions of high and low MD (HMD and LMD, respectively) in the full breast slices; (2) the same regions excised from the full slices; and (3) excised samples after H₂ O-D₂ O replacement. T₂ distributions were reconstructed from the CPMG decays using inverse Laplace transform.

RESULTS

Two major peaks, identified as fat and water, were consistently observed in the T₂ distributions of HMD regions. The LMD T₂ distributions were dominated by the fat peak. The relative areas of the two peaks exhibited statistically significant (P < .005) differences between HMD and LMD regions, enabling their classification as HMD or LMD. The relative-area distributions exhibited no statistically significant differences between full slices and excised samples.

CONCLUSION

T₂ -based portable-NMR analysis is a novel approach to MD quantification. The ability to quantify tissue composition, combined with the low cost of instrumentation, make this approach promising for clinical applications.

Diffusion-weighted imaging of the breast: current status as an imaging biomarker and future role

Diffusion-weighted imaging (DWI) of the breast is a MRI sequence that shows several advantages when compared to the dynamic contrast-enhanced sequence: it does not need intravenous contrast, it is relatively quick and easy to implement (artifacts notwithstanding). In this review, the current applications of DWI for lesion characterization and prognosis as well as for response evaluation are analyzed from the point of view of the necessary steps to become a useful surrogate of underlying biological processes (tissue architecture and cellularity): from the proof of concept, to the proof of mechanism, the proof of principle and finally the proof of effectiveness. Future applications of DWI in screening, DWI modeling and radiomics are also discussed.

Apparent diffusion coefficient mapping using diffusion-weighted MRI: impact of background parenchymal enhancement, amount of fibroglandular tissue and menopausal status on breast cancer diagnosis

OBJECTIVES

To investigate the impact of background parenchymal enhancement (BPE), amount of fibroglandular tissue (FGT) and menopausal status on apparent diffusion coefficient (ADC) values in differentiation between malignant and benign lesions.

METHODS

In this HIPAA-compliant study, mean ADC values of 218 malignant and 130 benign lesions from 288 patients were retrospectively evaluated. The differences in mean ADC values between benign and malignant lesions were calculated within groups stratified by BPE level (high/low), amount of FGT (dense/non-dense) and menopausal status (premenopausal/postmenopausal). Sensitivities and specificities for distinguishing malignant from benign lesions within different groups were compared for statistical significance.

RESULTS

The mean ADC value for malignant lesions was significantly lower compared to that for benign lesions (1.07±0.21 x 10^-3 mm²/s vs. 1.53±0.26 x 10^-3 mm²/s) (p<0.0001). Using the optimal cut-off point of 1.30 x 10^-3 mm²/s, an area under the curve of 0.918 was obtained, with sensitivity and specificity both of 87 %. There was no statistically significant difference in sensitivities and specificities of ADC values between different groups stratified by BPE level, amount of FGT or menopausal status.

CONCLUSIONS

Differentiation between benign and malignant lesions on ADC values is not significantly affected by BPE level, amount of FGT or menopausal status.

KEY POINTS

• ADC allows differentiation between benign and malignant lesions. • ADC is useful for breast cancer diagnosis despite different patient characteristics. • BPE, FGT or menopause do not significantly affect sensitivity and specificity.

Diffusion-weighted MRI characteristics associated with prognostic pathological factors and recurrence risk in invasive ER+/HER2- breast cancers

BACKGROUND

Hormone receptor-positive breast cancer is the most common subtype; better tools to identify which patients in this group would derive clear benefit from chemotherapy are needed.

PURPOSE

To evaluate the prognostic potential of diffusion-weighted MRI (DWI) by investigating associations with pathologic biomarkers and a genomic assay for 10-year recurrence risk.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 107 consecutive patients (from 2/2010 to 1/2013) with estrogen receptor (ER)-positive/HER2neu-negative invasive breast cancer who had the 21-gene recurrence score (RS) test (Oncotype DX, Genomic Health).

FIELD STRENGTH/SEQUENCE

Each subject underwent presurgical 3T breast MRI, which included DWI (b = 0, 800 s/mm² ).

ASSESSMENT

Apparent diffusion coefficient (ADC) and contrast-to-noise ratio (CNR) were measured for each lesion by a fifth year radiology resident. Pathological markers (Nottingham histologic grade, Ki-67, RS) were determined from pathology reports. Medical records were reviewed to assess recurrence-free survival.

STATISTICAL TESTS

RS was stratified into low (<18), moderate (18-30), and high (>30)-risk groups. Associations of DWI characteristics with pathologic biomarkers were evaluated by binary or ordinal logistic regression, as appropriate, with adjustment for multiple comparisons. Post-hoc comparisons between specific groups were also performed.

RESULTS

ADCmean (odds ratio [OR] = 0.61 per 1 standard deviation [SD] increase, adj. P = 0.044) and CNR (OR = 1.76 per 1-SD increase, adj. P = 0.026) were significantly associated with increasing tumor grade. DWI CNR was also significantly associated with a high (Ki-67 ≥14%) proliferation rate (OR = 2.55 per 1-SD increase, adj. P = 0.026). While there were no statistically significant linear associations in ADC (adj. P = 0.80-0.85) and CNR (adj. P = 0.56) across all three RS groups by ordinal logistic regression, post-hoc analyses suggested that high RS lesions exhibited lower ADCmean (P = 0.037) and ADCmax (P = 0.004) values and higher CNR (P = 0.008) compared to lesions with a low or moderate RS.

DATA CONCLUSION

DWI characteristics correlated with tumor grade, proliferation index, and RS, and may potentially help to identify those with highest recurrence risk and most potential benefit from chemotherapy.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage 3 J. Magn. Reson. Imaging 2017.

Impact of Different Analytic Approaches on the Analysis of the Breast Fibroglandular Tissue Using Diffusion Weighted Imaging

Purpose. This study investigated the impact of the different region of interest (ROI) approaches on measurement of apparent diffusion coefficient (ADC) values in the breast firbroglandular tissue (FT). Methods. Breast MR images of 38 women diagnosed with unilateral breast cancer were studied. Percent density (PD) and ADC were measured from the contralateral normal breast. Four different ROIs were used for ADC measurement. The measured PD and ADC were correlated. Results. Among the four ROIs, the manually placed small ROI on FT gave the highest mean ADC (ADC = 1839 ± 343 [×10⁻⁶ mm²/s]), while measurement from the whole breast gave the lowest mean ADC (ADC = 933 ± 383 [×10⁻⁶ mm²/s]). The ADC measured from the whole breast was highly correlated with PD with r = 0.95. In slice-to-slice comparison, the central slices with more FT had higher ADC values than the peripheral slices did, presumably due to less partial volume effect from fat. Conclusions. Our results indicated that the measured ADC heavily depends on the composition of breast tissue contained in the ROI used for the ADC measurements. Women with low breast density showing lower ADC values were most likely due to the partial volume effect of fatty tissues.

Diffusion-weighted breast MRI: Clinical applications and emerging techniques

Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.

Diffusion-Weighted Breast Magnetic Resonance Imaging: A Semiautomated Voxel Selection Technique Improves Interreader Reproducibility of Apparent Diffusion Coefficient Measurements

OBJECTIVE

To determine whether a semiautomated voxel selection technique improves interreader reproducibility for breast apparent diffusion coefficient (ADC) measurements.

METHODS

Three readers retrospectively performed ADC measurements of 31 breast lesions (16 malignant, 15 benign) and contralateral normal tissue in 26 women both unassisted (manual method) and assisted by a semiautomated software tool that excludes voxels below a dynamically specified signal intensity threshold. Reproducibility between readers for each technique was assessed by Bland-Altman analysis and concordance correlation coefficients (CCCs).

RESULTS

Differences between readers' measured ADCs of lesions were smaller with the semiautomated tool vs the manual method. Concordance correlation coefficients for each reader pair were greater with the semiautomated tool for lesions (mean CCC difference, 0.11; 95% confidence interval, 0.04-0.26). For normal tissue, reader agreement was lower than for lesions and did not differ based on software tools (mean CCC difference, 0.00; 95% confidence interval, -0.14 to 0.13).

CONCLUSIONS

A semiautomated voxel selection tool can improve interreader reproducibility of breast lesion ADC measures.

Performance of DWI as a Rapid Unenhanced Technique for Detecting Mammographically Occult Breast Cancer in Elevated-Risk Women With Dense Breasts

OBJECTIVE

The objective of our study was to evaluate the performance of DWI to detect mammographically occult breast cancer in elevated-risk women with dense breasts.

MATERIALS AND METHODS

We retrospectively reviewed all women who underwent screening breast MRI at our institution from January 2007 through May 2013. We created a case-control cohort composed of 48 subjects with mammographically dense breasts: 24 with mammographically occult cancer detected on MRI and 24 healthy women with negative MRI findings who were matched to the subjects with breast cancer patients for age, breast density, and MRI protocol. The contrast-to-noise ratio (CNR), apparent diffusion coefficient (ADC), and conspicuity score (range, 1-5) were assessed on DWI for all malignancies. Lesions and normal tissue were compared using the Wilcoxon signed rank test; associations with clinical characteristics were evaluated using the Mann-Whitney U test. Three experienced breast imagers who were blinded to medical records and contrast-enhanced MRI findings independently evaluated the unenhanced MRI scans of the 48 women for the presence of cancer.

RESULTS

Mammographically occult breast cancers (invasive carcinoma, n = 16; ductal carcinoma in situ, n = 8) in women with dense breasts typically exhibited higher signal intensity on DWI than normal parenchyma (median CNR of cancers, 1.4; median conspicuity score of cancers, 4) and a lower ADC (median, 1.31 vs 1.79 × 10(-3) mm(2)/s, respectively) (p < 0.0001). The conspicuity score, CNR, and ADC were not associated with patient age, menopausal status, lesion size, morphologic type, or histology (p > 0.05). Average reader performance using unenhanced MRI was 45% sensitivity, 91% specificity, 62% positive predictive value, and 83% negative predictive value.

CONCLUSION

In elevated-risk women with dense breasts, DWI can reveal cancers in addition to those detected on mammography alone with a low false-positive rate; these results suggest that DWI may have potential as a rapid supplemental screening tool.

Diffusion-weighted magnetic resonance imaging in cancer: Reported apparent diffusion coefficients, in-vitro and in-vivo reproducibility

There is considerable disparity in the published apparent diffusion coefficient (ADC) values across different anatomies. Institutions are increasingly assessing repeatability and reproducibility of the derived ADC to determine its variation, which could potentially be used as an indicator in determining tumour aggressiveness or assessing tumour response. In this manuscript, a review of selected articles published to date in healthy extra-cranial body diffusion-weighted magnetic resonance imaging is presented, detailing reported ADC values and discussing their variation across different studies. In total 115 studies were selected including 28 for liver parenchyma, 15 for kidney (renal parenchyma), 14 for spleen, 13 for pancreatic body, 6 for gallbladder, 13 for prostate, 13 for uterus (endometrium, myometrium, cervix) and 13 for fibroglandular breast tissue. Median ADC values in selected studies were found to be 1.28 × 10(-3) mm(2)/s in liver, 1.94 × 10(-3) mm(2)/s in kidney, 1.60 × 10(-3) mm(2)/s in pancreatic body, 0.85 × 10(-3) mm(2)/s in spleen, 2.73 × 10(-3) mm(2)/s in gallbladder, 1.64 × 10(-3) mm(2)/s and 1.31 × 10(-3) mm(2)/s in prostate peripheral zone and central gland respectively (combined median value of 1.54×10(-3) mm(2)/s), 1.44 × 10(-3) mm(2)/s in endometrium, 1.53 × 10(-3) mm(2)/s in myometrium, 1.71 × 10(-3) mm(2)/s in cervix and 1.92 × 10(-3) mm(2)/s in breast. In addition, six phantom studies and thirteen in vivo studies were summarized to compare repeatability and reproducibility of the measured ADC. All selected phantom studies demonstrated lower intra-scanner and inter-scanner variation compared to in vivo studies. Based on the findings of this manuscript, it is recommended that protocols need to be optimised for the body part studied and that system-induced variability must be established using a standardized phantom in any clinical study. Reproducibility of the measured ADC must also be assessed in a volunteer population, as variations are far more significant in vivo compared with phantom studies.

Diffusion tensor imaging in the normal breast: influences of fibroglandular tissue composition and background parenchymal enhancement

OBJECTIVE

To evaluate effects of fibroglandular tissue (FGT) composition and background parenchymal enhancement (BPE) on diffusion tensor imaging (DTI) parameters in normal breast tissue.

METHODS

DTI analysis was performed on 35 breasts with regions of interest drawn to include only normal tissue. Breasts were dichotomized by FGT composition and by BPE; DTI parameters were compared.

RESULTS

The λ1 principal diffusion coefficient was lower in breasts with moderate/marked BPE versus those with minimal/mild BPE (P=.039). All other parameters were unaffected.

CONCLUSION

λ1 is sensitive to differences in BPE within normal breast tissue that should be taken into account in DTI evaluation.

Removing silicone artifacts in diffusion-weighted breast MRI by means of shift-resolved spatiotemporally encoding

PURPOSE

Evaluate the usefulness of diffusion-weighted spatiotemporally encoded (SPEN) methods to obtain apparent diffusion coefficient (ADC) maps of fibroglandular human breast tissue, in the presence of silicone implants.

METHODS

Seven healthy volunteers with breast augmentation were scanned at 3 Tesla (T) using customized SPEN sequences yielding separate silicone and water (1) H images in one scan, together with their corresponding diffusion-weightings.

RESULTS

SPEN's ability to deliver multiple spectrally resolved images in a single scan, coupled to the method's substantial robustness to magnetic field heterogeneities, served to acquire ADC maps that could be freed from contributions that did not belong to fibroglandular tissue.

CONCLUSION

SPEN-based sequences incorporating spectral discrimination and diffusion-weighting enable the acquisition of reliable ADC maps despite the presence of dominant signals from silicone implants, thereby opening new screening possibilities for the identification of malignancies in breast augmented patients.

Alternative screening for dense breasts: MRI

OBJECTIVE. The purpose of this article is to review the use of MRI in breast density measurement and breast cancer risk estimation and to discuss the role of MRI as an alternative screening to mammography for screening women with dense breasts. CONCLUSION. The potential of MRI for screening women with dense breasts remains controversial because of the paucity of clinical evidence, the possibility of overdiagnosis, and the cost-effectiveness of the technique in this population. Although methods of MRI measurement require standardization and automation, future addition of MRI density to risk models may positively impact their value.

Dynamic properties of water in breast pathology depend on the histological compounds: distinguishing tissue malignancy by water diffusion coefficients

Background

The parameters that characterize the intricate water diffusion in tumors may also reveal their distinct pathology. Specifically, characterization of breast cancer could be aided by diffusion magnetic resonance.

The present in vitro study aimed to discover connections between the NMR biexponential diffusion parameters [fast diffusion phase (D_FDP ), slow diffusion phase (D_SDP ), and spin population of fast diffusion phase (P₁)] and the histological constituents of nonmalignant (control) and malignant human breast tissue. It also investigates whether the diffusion coefficients indicate tissue status.

Methods

Post-surgical specimens of control (mastopathy and peritumoral tissues) and malignant human breast tissue were placed in an NMR spectrometer and diffusion sequences were applied. The resulting decay curves were analyzed by a biexponential model, and slow and fast diffusion parameters as well as percentage signal were identified. The same samples were also histologically examined and their percentage composition of several tissue constituents were measured: parenchyma (P), stroma (St), adipose tissue (AT), vessels (V) , pericellular edema (PCE), and perivascular edema (PVE). Correlations between the biexponential model parameters and tissue types were evaluated for different specimens. The effects of tissue composition on the biexponential model parameters, and the effects of histological and model parameters on cancer probability, were determined by non-linear regression.

Results

Meaningful relationships were found among the in vitro data. The dynamic parameters of water in breast tissue are stipulated by the histological constituents of the tissues (P, St, AT, PCE, and V). High coefficients of determination (R²) were obtained in the non-linear regression analysis: D_FDP (R² = 0.92), D_SDP (R² = 0.81), and P₁(R² = 0.93).

In the cancer probability analysis, the informative value (R²) of the obtained equations of cancer probability in distinguishing tissue malignancy depended on the parameters input to the model. In order of increasing value, these equations were: cancer probability (P, St, AT, PCE, V) (R² = 0.66), cancer probability (D_FDP, D_SDP)(R² = 0.69), cancer probability (D_FDP, D_SDP, P₁) (R² = 0.85).

Conclusion

Histological tissue components are related to the diffusion biexponential model parameters. From these parameters, the relative probability of cancer in a given specimen can be determined with some certainty.