Kinetic curves of malignant lesions are not consistent across MRI systems: need for improved standardization of breast dynamic contrast-enhanced MRI acquisition

Adding quantitative T1rho-weighted imaging to conventional MRI improves specificity and sensitivity for differentiating malignant from benign breast lesions

OBJECTIVES

To investigate the feasibility of T1rho-weighted imaging in differentiating malignant from benign breast lesions and to explore the additional value of T1rho to conventional MRI.

MATERIALS AND METHODS

We prospectively enrolled consecutive women with breast lesions who underwent preoperative T1rho-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) between November 2021 and July 2023. The T1rho, apparent diffusion coefficient (ADC), and semi-quantitative parameters from DCE-MRI were obtained and compared between benign and malignant groups. The diagnostic performance was analyzed and compared using receiver operating characteristic (ROC) curves and the Delong Test.

RESULTS

This study included 113 patients (74 malignant and 39 benign lesions). The mean T1rho value in the benign group (92.61 ± 22.10 ms) was significantly higher than that in the malignant group (72.18 ± 16.37 ms) (P < 0.001). The ADC value and time to peak (TTP) value in the malignant group (1.13 ± 0.45 and 269.06 ± 106.01, respectively) were lower than those in the benign group (1.57 ± 0.45 and 388.30 ± 81.13, respectively) (all P < 0.001). T1rho combined with ADC and TTP showed good diagnostic performance with an area under the curve (AUC) of 0.896, a sensitivity of 81.0%, and a specificity of 87.1%. The specificity and sensitivity of the combination of T1rho, ADC, and TTP were significantly higher than those of the combination of ADC and TTP (87.1% vs. 84.6%, P < 0.005; 81.0% vs. 77.0%, P < 0.001).

CONCLUSION

T1rho-weighted imaging was a feasible MRI sequence for differentiating malignant from benign breast lesions. The combination of T1rho, ADC and TTP could achieve a favorable diagnostic performance with improved specificity and sensitivity, T1rho could serve as a supplementary approach to conventional MRI.

Dynamic Contrast-Enhanced (DCE) MRI

The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.

Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.

Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.

Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters

The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [K^trans ], fractional volume of the extravascular extracellular space [v_e ], and blood plasma volume fraction [v_p ]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of K^trans and v_e , and less than 11% in the estimation of v_p . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.

Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.

Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning

Objective

This study was conducted in order to investigate the feasibility of using radiomics analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) images for discriminating malignant from benign MR-detected additional lesions in patients with primary breast cancer.

Materials and Methods

One hundred seventy-four MR-detected additional lesions (benign, n = 86; malignancy, n = 88) from 158 patients with ipsilateral primary breast cancer from a tertiary medical center were included in this retrospective study. The entire data were randomly split to training (80%) and independent test sets (20%). In addition, 25 patients (benign, n = 21; malignancy, n = 15) from another tertiary medical center were included for the external test. Radiomics features that were extracted from three regions-of-interest (ROIs; intratumor, peritumor, combined) using fat-saturated T1-weighted images obtained by subtracting pre- from postcontrast images (SUB) and T2-weighted image (T2) were utilized to train the support vector machine for the binary classification. A decision tree method was utilized to build a classifier model using clinical imaging interpretation (CII) features assessed by radiologists. Area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity were used to compare the diagnostic performance.

Results

The RA models trained using radiomics features from the intratumor-ROI showed comparable performance to the CII model (accuracy, AUROC: 73.3%, 69.6% for the SUB RA model; 70.0%, 75.1% for the T2 RA model; 73.3%, 72.0% for the CII model). The diagnostic performance increased when the radiomics and CII features were combined to build a fusion model. The fusion model that combines the CII features and radiomics features from multiparametric MRI data demonstrated the highest performance with an accuracy of 86.7% and an AUROC of 91.1%. The external test showed a similar pattern where the fusion models demonstrated higher levels of performance compared with the RA- or CII-only models. The accuracy and AUROC of the SUB+T2 RA+CII model in the external test were 80.6% and 91.4%, respectively.

Conclusion

Our study demonstrated the feasibility of using RA with machine learning approach based on multiparametric MRI for quantitatively characterizing MR-detected additional lesions. The fusion model demonstrated an improved diagnostic performance over the models trained with either RA or CII alone.

Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths

Radiomic features extracted from breast lesion images have shown potential in diagnosis and prognosis of breast cancer. As medical centers transition from 1.5 T to 3.0 T magnetic resonance (MR) imaging, it is beneficial to identify potentially robust radiomic features across field strengths because images acquired at different field strengths could be used in machine learning models. Dynamic contrast-enhanced MR images of benign breast lesions and hormone receptor positive/HER2-negative (HR+/HER2-) breast cancers were acquired retrospectively, yielding 612 unique cases: 150 and 99 benign lesions imaged at 1.5 T and 3.0 T, and 223 and 140 HR+/HER2- cancerous lesions imaged at 1.5 T and 3.0 T, respectively. In addition, an independent set of seven lesions imaged at both field strengths, three benign lesions and four HR+/HER2- cancers, was analyzed separately. Lesions were automatically segmented using a 4D fuzzy c-means method; thirty-eight radiomic features were extracted. Feature value distributions were compared by cancer status and imaging field strength using the Kolmogorov-Smirnov test. Features that did not demonstrate a statistically significant difference were considered to be potentially robust. The area under the receiver operating characteristic curve (AUC), for the task of classifying lesions as benign or HR+/HER2- cancer, was determined for each feature at each field strength. Three features were found to be both potentially robust across field strength and of high classification performance, i.e., AUCs statistically greater than 0.5 in the classification task: one shape feature (irregularity), one texture feature (sum average) and one enhancement variance kinetics features (enhancement variance increasing rate). In the demonstration set of lesions imaged at both field strengths, two of the three potentially robust features showed qualitative agreement across field strength. These findings may contribute to the development of computer-aided diagnosis models that are robust across field strength for this classification task.

Characterization of breast cancer subtypes based on quantitative assessment of intratumoral heterogeneity using dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging

OBJECTIVE

To investigate whether intratumoral heterogeneity, assessed via dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI), reflects the molecular subtypes of invasive breast cancers.

MATERIAL AND METHODS

We retrospectively evaluated data from 248 consecutive women (mean age ± standard deviation, 54.6 ± 12.2 years) with invasive breast cancer who underwent preoperative DCE-MRI and DWI between 2019 and 2020. To evaluate intratumoral heterogeneity, kinetic heterogeneity (a measure of heterogeneity in the proportions of tumor pixels with delayed washout, plateau, and persistent components within a tumor) was assessed with DCE-MRI using a commercially available computer-aided diagnosis system. Apparent diffusion coefficients (ADCs) were obtained using a region-of-interest technique, and ADC heterogeneity was calculated using the following formula: (ADC_max-ADC_min)/ADC_mean. Possible associations between imaging-based heterogeneity values and breast cancer subtypes were analyzed.

RESULTS

Of the 248 invasive breast cancers, 61 (24.6%) were classified as luminal A, 130 (52.4%) as luminal B, 25 (10.1%) as HER2-enriched, and 32 (12.9%) as triple-negative breast cancer (TNBC). There were significant differences in the kinetic and ADC heterogeneity values among tumor subtypes (p < 0.001 and p = 0.023, respectively). The TNBC showed higher kinetic and ADC heterogeneity values, whereas the HER2-enriched subtype showed higher kinetic heterogeneity values compared to the luminal subtypes. Multivariate linear analysis showed that the HER2-enriched (p < 0.001) and TNBC subtypes (p < 0.001) were significantly associated with higher kinetic heterogeneity values. The TNBC subtype (p = 0.042) was also significantly associated with higher ADC heterogeneity values.

CONCLUSIONS

Quantitative assessments of heterogeneity in enhancement kinetics and ADC values may provide biological clues regarding the molecular subtypes of breast cancer.

KEY POINTS

• Higher kinetic heterogeneity was associated with HER2-enriched and triple-negative breast cancer. • Higher ADC heterogeneity was associated with triple-negative breast cancer. • Aggressive breast cancer subtypes exhibited higher intratumoral heterogeneity based on MRI.

Combined Use of Texture Features and Morphological Classification Based on Dynamic Contrast-enhanced MR Imaging: Differentiating Benign and Malignant Breast Masses with High Negative Predictive Value

Purpose: We evaluated the diagnostic performance of the texture features of dynamic contrast-enhanced (DCE) MRI for breast cancer diagnosis in which the discriminator was optimized, so that the specificity was maximized via the restriction of the negative predictive value (NPV) to greater than 98%.

Methods: Histologically proven benign and malignant mass lesions of DCE MRI were enrolled retrospectively. Training and testing sets consist of 166 masses (49 benign, 117 malignant) and 50 masses (15 benign, 35 malignant), respectively. Lesions were classified via MRI review by a radiologist into 4 shape types: smooth (S-type, 34 masses in training set and 8 masses in testing set), irregular without rim-enhancement (I-type, 60 in training and 14 in testing), irregular with rim-enhancement (R-type, 56 in training and 22 in testing), and spicula (16 in training and 6 in testing). Spicula were immediately classified as malignant. For the remaining masses, 298 texture features were calculated using a parametric map of DCE MRI in 3D mass regions. Masses were classified into malignant or benign using two thresholds on a feature pair. On the training set, several feature pairs and their thresholds were selected and optimized for each mass shape type to maximize specificity with the restriction of NPV > 98%. NPV and specificity were computed using the testing set by comparison with histopathologic results and averaged on the selected feature pairs.

Results: In the training set, 27, 12, and 15 texture feature pairs are selected for S-type, I-type, and R-type masses, respectively, and thresholds are determined. In the testing set, average NPV and specificity using the selected texture features were 99.0% and 45.2%, respectively, compared to the NPV (85.7%) and specificity (40.0%) in visually assessed MRI category-based diagnosis.

Conclusion: We, therefore, suggest that the NPV of our texture-based features method described performs similarly to or greater than the NPV of the MRI category-based diagnosis.

Correct determination of the enhancement curve is critical to ensure accurate diagnosis using the Kaiser score as a clinical decision rule for breast MRI

OBJECTIVES

the Kaiser score is increasingly recognized as a valuable tool to improve breast MRI interpretation. Contrast enhancement kinetics are the second most important diagnostic criterion, thus defining the curve type plays a crucial role in Kaiser score assessment. We investigate whether the timepoint used to determine the initial enhancement (earlyor peak) for the signal-intensity time curve analysis affects the diagnostic performance of the Kaiser score.

METHODS

This IRB-approved, retrospective, single-center study included 70 consecutives histologically verified breast MRI cases. Two off-site breast radiologists independently read all examinations using the Kaiser score, assessing the initial enhancement using three approaches: -first (1 st), second (2nd) and peak (maximum) of either 1 st or 2nd post-contrast timepoints. The initial enhancement was then compared to the last timepoint (delayed enhancement) to determine the curve type. Visual assessment of curve types was used for this study. Diagnostic performance was evaluated by receiver operating characteristics (ROC) analysis.

RESULTS

Kaiser score reading results using the peak enhancement of either the first or second timepoint performed significantly better than the other approaches (P < 0.05, respectively) and specifically achieved higher sensitivity. Diagnostic accuracy (AUC area under the curve) ranged between 85.4 % and 91.6 %, without significant differences between the two readers (P < 0.5).

CONCLUSIONS

Diagnostic performance of the Kaiser score is significantly influenced by how the initial enhancement timepoint is determined. Peak enhancement should be used as initial timepoint to avoid pitfalls due to timing or physiological differences.

Factors Affecting Image Quality and Lesion Evaluability in Breast Diffusion-weighted MRI: Observations from the ECOG-ACRIN Cancer Research Group Multisite Trial (A6702)

OBJECTIVE

The A6702 multisite trial confirmed that apparent diffusion coefficient (ADC) measures can improve breast MRI accuracy and reduce unnecessary biopsies, but also found that technical issues rendered many lesions non-evaluable on diffusion-weighted imaging (DWI). This secondary analysis investigated factors affecting lesion evaluability and impact on diagnostic performance.

METHODS

The A6702 protocol was IRB-approved at 10 institutions; participants provided informed consent. In total, 103 women with 142 MRI-detected breast lesions (BI-RADS assessment category 3, 4, or 5) completed the study. DWI was acquired at 1.5T and 3T using a four b-value, echo-planar imaging sequence. Scans were reviewed for multiple quality factors (artifacts, signal-to-noise, misregistration, and fat suppression); lesions were considered non-evaluable if there was low confidence in ADC measurement. Associations of lesion evaluability with imaging and lesion characteristics were determined. Areas under the receiver operating characteristic curves (AUCs) were compared using bootstrapping.

RESULTS

Thirty percent (42/142) of lesions were non-evaluable on DWI; 23% (32/142) with image quality issues, 7% (10/142) with conspicuity and/or localization issues. Misregistration was the only factor associated with non-evaluability (P = 0.001). Smaller (≤10 mm) lesions were more commonly non-evaluable than larger lesions (p <0.03), though not significant after multiplicity correction. The AUC for differentiating benign and malignant lesions increased after excluding non-evaluable lesions, from 0.61 (95% CI: 0.50-0.71) to 0.75 (95% CI: 0.65-0.84).

CONCLUSION

Image quality remains a technical challenge in breast DWI, particularly for smaller lesions. Protocol optimization and advanced acquisition and post-processing techniques would help to improve clinical utility.

Kinetic volume analysis on dynamic contrast-enhanced MRI of triple-negative breast cancer: associations with survival outcomes

OBJECTIVE

To evaluate the associations between computer-aided diagnosis (CAD)-generated kinetic volume parameters and survival in triple-negative breast cancer (TNBC) patients.

METHODS

40 patients with TNBC who underwent pre-operative MRI between March 2008 and March 2014 were included. We analyzed CAD-generated parameters on dynamic contrast-enhanced MRI, visual MRI assessment, and histopathological data. Cox proportional hazards models were used to determine associations with survival outcomes.

RESULTS

12 of the 40 (30.0%) patients experienced recurrence and 7 died of breast cancer after a median follow-up of 73.6 months. In multivariate analysis, higher percentage volume (%V) with more than 200% initial enhancement rate correlated with worse disease-specific survival (hazard ratio, 1.12; 95% confidence interval, 1.02-1.22; p-value, 0.014) and higher %V with more than 100% initial enhancement rate followed by persistent curve type at 30% threshold correlated with worse disease-specific survival (hazard ratio, 1.33; 95% confidence interval, 1.10-1.61; p-value, 0.004) and disease-free survival (hazard ratio, 1.27; 95% confidence interval, 1.12-1.43; p-value, 0.000).

CONCLUSION

CAD-generated kinetic volume parameters may correlate with survival in TNBC patients. Further study would be necessary to validate our results on larger cohorts.

ADVANCES IN KNOWLEDGE

CAD generated kinetic volume parameters on breast MRI can predict recurrence and survival outcome of patients in TNBC. Varying the enhancement threshold improved the predictive performance of CAD generated kinetic volume parameter.

Quantitative Analysis of Lower Leg Muscle Enhancement Measured From Dynamic Computed Tomographic Angiography for Diagnosis of Peripheral Arterial Occlusive Disease

OBJECTIVES

The purpose of this study was to evaluate whether quantitative analysis of lower leg muscle enhancement measured from dynamic computed tomographic angiography (dyn-CTA) could be used for diagnosis of peripheral arterial occlusive disease.

METHODS

Patients (N = 35) with known peripheral arterial occlusive disease underwent the dyn-CTA of calves first. Five minutes later, standard CTA of the peripheral runoff from the diaphragm to the toes was performed. A runoff score was assigned by radiologists as a reference standard for each of 4 lower leg artery segments. The lower leg muscle enhancement measured from the dyn-CTA was analyzed by using quantitative kinetic parameters, including initial enhancement (E1), peak enhancement (Epeak), and enhancement ratio (ER) calculated from average time attenuation curves. In addition, histogram of lower leg muscle enhancement was evaluated by using the first enhanced phase images.

RESULTS

Lower extremities were diagnosed as a normal group (n = 22) with each vessel segment score equals to 1 or lower and runoff score, 7 or lower, and otherwise as an ischemia group (n = 48). Average ± SD E1 is 91.4% ± 8.5% and 82.3% ± 10.7%, Epeak is 122.7% ± 10.4% and 115.6% ± 11.1%, and ER is 0.75 ± 0.05 and 0.72 ± 0.09 for normal and ischemia group, respectively. Statistical analysis showed that average E1 and Epeak for the ischemia group were significantly lower (P < 0.05) than the normal group. The histogram analysis demonstrated that mean and median of muscle enhancement in the ischemia group were significantly smaller (P < 0.05), and coefficient of variation (CV) was significantly larger (P < 0.05) than the normal group. There were weak negative correlations (r = -0.42, P < 0.05) between runoff scores and E1 and Epeak, and weak positive correlation (r = 0.40, P < 0.05) between runoff scores and CV. The receiver operating characteristics analysis between the 2 groups had area under the curve of 0.77 and 0.76 for E1 and CV, respectively.

CONCLUSIONS

Lower leg muscle enhancement measured from the dyn-CTA could be assessed quantitatively to assist diagnosis of ischemia in clinical practice.

Use of diffusion kurtosis imaging and quantitative dynamic contrast-enhanced MRI for the differentiation of breast tumors

BACKGROUND

Breast MRI is a sensitive imaging technique to assess breast cancer but its effectiveness still remains to be improved.

PURPOSE

To evaluate the diagnostic performance of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and quantitative dynamic contrast-enhanced (DCE)-MRI in differentiating malignant from benign breast lesions independently or jointly and to explore whether correlations exist among these parameters.

STUDY TYPE

Retrospective.

POPULATION

In all, 106 patients with breast lesions (47 malignant, 59 benign).

SEQUENCE

DKI sequence with seven b values and quantitative DCE sequence on 3.0T MRI.

ASSESSMENT

Diffusion parameters (mean diffusivity [MD], mean diffusivity [MK], and apparent diffusion coefficient [ADC]) from DKI and DWI and perfusion parameters from DCE (K^trans , k_ep , v_e , and v_p ) were calculated by two experienced radiologists after postprocessing. Disagreement between the two observers was resolved by consensus.

STATISTICAL TESTS

The parameters in benign and malignant lesions were compared by Student's t-test. The diagnostic performances of DKI and quantitative DCE, either alone or in combination, were evaluated by receiver operating characteristic (ROC) analysis. The Spearman correlation test was used to evaluate correlations among the diffusion parameters and perfusion parameters.

RESULTS

MK, MD, ADC, K^trans , and k_ep values were significantly different between breast cancer and benign lesions (P < 0.05). MK from DKI demonstrated the highest AUC of 0.849, which is significantly higher than ADC derived from conventional DWI (z = 3.345, P = 0.0008). The specificity of DCE-MRI-derived parameters was improved when combining diffusion parameters, such as ADC and MK. The highest diagnostic specificity (93.2%) was obtained when k_ep and ADC were combined. k_ep was correlated moderately positively with MK (r = 0.516) and moderately negatively with MD (r = -0.527). K^trans was weakly positively correlated with MK with an r of 0.398 and weakly negatively correlated with MD with an r of -0.450.

DATA CONCLUSION

DKI is more valuable than conventional DWI in distinguishing between benign and malignant breast lesions. DKI exhibits promise as a quantitative technique to augment quantitative DCE-MRI. Diffusion parameters derived from DKI were statistically correlated with perfusion parameters from quantitative DCE-MRI.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1358-1366.

Preoperative dynamic breast magnetic resonance imaging kinetic features using computer-aided diagnosis: Association with survival outcome and tumor aggressiveness in patients with invasive breast cancer

Objectives

To evaluate whether preoperative breast dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging kinetic features, assessed using computer-aided diagnosis (CAD), can predict survival outcome and tumor aggressiveness in patients with invasive breast cancer.

Materials and methods

Between March and December 2011, 301 women who underwent preoperative DCE MR imaging for invasive breast cancer, with CAD data, were identified. All MR images were retrospectively evaluated using a commercially available CAD system. The following kinetic parameters were prospectively recorded for each lesion: initial peak enhancement, the proportion of early phase medium and rapid enhancement, and the proportion of delayed phase persistent, plateau, and washout enhancement. The Cox proportional hazards model was used to determine the association between the kinetic features assessed by CAD and disease-free survival (DFS). The peak signal intensity and kinetic enhancement profiles were compared with the clinical-pathological variables.

Results

There were 32 recurrences during a mean follow-up time of 55.2 months (range, 5–72 months). Multivariate analysis revealed that a higher peak enhancement (DFS hazard ratio, 1.004 [95% confidence interval (CI): 1.001, 1.006]; P = .013) on DCE MR imaging and a triple-negative subtype (DFS hazard ratio, 21.060 [95% CI: 2.675, 165.780]; P = .004) were associated with a poorer DFS. Higher peak enhancement was significantly associated with a higher tumor stage, clinical stage, and histologic grade.

Conclusions

Patients with breast cancer who showed higher CAD-derived peak enhancement on breast MR imaging had worse DFS. Peak enhancement and volumetric analysis of kinetic patterns were useful for predicting tumor aggressiveness.

Quantitative evaluation of contrast agent uptake in standard fat-suppressed dynamic contrast-enhanced MRI examinations of the breast

PURPOSE

To propose a method to quantify T1 and contrast agent uptake in breast dynamic contrast-enhanced (DCE) examinations undertaken with standard clinical fat-suppressed MRI sequences and to demonstrate the proposed approach by comparing the enhancement characteristics of lobular and ductal carcinomas.

METHODS

A standard fat-suppressed DCE of the breast was performed at 1.5 T (Siemens Aera), followed by the acquisition of a proton density (PD)-weighted sequence, also fat suppressed. Both sequences were characterized with test objects (T1 ranging from 30 ms to 2,400 ms) and calibration curves were obtained to enable T1 calculation. The reproducibility and accuracy of the calibration curves were also investigated. Healthy volunteers and patients were scanned with Ethics Committee approval. The effect of B0 field inhomogeneity was assessed in test objects and healthy volunteers. The T1 of breast tumors was calculated at different time points (pre-, peak-, and post-contrast agent administration) for 20 patients, pre-treatment (10 lobular and 10 ductal carcinomas) and the two cancer types were compared (Wilcoxon rank-sum test).

RESULTS

The calibration curves proved to be highly reproducible (coefficient of variation under 10%). T1 measurements were affected by B0 field inhomogeneity, but frequency shifts below 50 Hz introduced only 3% change to fat-suppressed T1 measurements of breast parenchyma in volunteers. The values of T1 measured pre-, peak-, and post-contrast agent administration demonstrated that the dynamic range of the DCE sequence was correct, that is, image intensity is approximately directly proportional to 1/T1 for that range. Significant differences were identified in the width of the distributions of the post-contrast T1 values between lobular and ductal carcinomas (P < 0.05); lobular carcinomas demonstrated a wider range of post-contrast T1 values, potentially related to their infiltrative growth pattern.

CONCLUSIONS

This work has demonstrated the feasibility of fat-suppressed T1 measurements as a tool for clinical studies. The proposed quantitative approach is practical, enabled the detection of differences between lobular and invasive ductal carcinomas, and further enables the optimization of DCE protocols by tailoring the dynamic range of the sequence to the values of T1 measured.

Effects of MRI scanner parameters on breast cancer radiomics

PURPOSE

To assess the impact of varying magnetic resonance imaging (MRI) scanner parameters on the extraction of algorithmic features in breast MRI radiomics studies.

METHODS

In this retrospective study, breast imaging data for 272 patients were analyzed with magnetic resonance (MR) images. From the MR images, we assembled and implemented 529 algorithmic features of breast tumors and fibrograndular tissue (FGT). We divided the features into 10 groups based on the type of data used for the feature extraction and the nature of the extracted information. Three scanner parameters were considered: scanner manufacturer, scanner magnetic field strength, and slice thickness. We assessed the impact of each of the scanner parameters on each of the feature by testing whether the feature values are systematically diverse for different values of these scanner parameters. A two-sample t-test has been used to establish whether the impact of a scanner parameter on values of a feature is significant and receiver operating characteristics have been used for to establish the extent of that effect.

RESULTS

On average, higher proportion (69% FGT versus 20% tumor) of FGT related features were affected by the three scanner parameters. Of all feature groups and scanner parameters, the feature group related to the variation in FGT enhancement was found to be the most sensitive to the scanner manufacturer (AUC = 0.81 ± 0.14).

CONCLUSIONS

Features involving calculations from FGT are particularly sensitive to the scanner parameters.

Spectral characterization of tissues in high spectral and spatial resolution MR images: Implications for a classification-based synthetic CT algorithm

PURPOSE

To characterize the spectral parameters of tissues with high spectral and spatial resolution magnetic resonance images to be used as a foundation for a classification-based synthetic CT algorithm.

METHODS

A phantom was constructed consisting of a section of fresh beef leg with bone embedded in 1% agarose gel. The high spectral and spatial (HiSS) resolution MR imaging sequence used had 1.0 mm in-plane resolution and 11.1 Hz spectral resolution. This sequence was used to image the phantom and one patient. Post-processing was performed off-line with IDL and included Fourier transformation of the time-domain data, labeling of fat and water peaks, and fitting the magnitude spectra with Lorentzian functions. Images of the peak height and peak integral of both the water and fat resonances were generated and analyzed. Several regions-of-interest (ROIs) were identified in phantom: bone marrow, cortical bone, adipose tissue, muscle, agar gel, and air; in the patient, no agar gel was present but an ROI of saline in the bladder was analyzed. All spectra were normalized by the noise within each voxel; thus, all parameters are reported in terms of signal-to-noise (SNR). The distributions of tissue spectral parameters were analyzed and scatterplots generated. Water peak height in cortical bone was compared to air using a nonparametric t-test. Composition of the various ROIs in terms of water, fat, or fat and water was also reported.

RESULTS

In phantom, the scatterplot of peak height (water versus fat) showed good separation of bone marrow and adipose tissue. Water versus fat integral scatterplot showed better separation of muscle and cortical bone than the peak height scatterplot. In the patient data, the distributions of water and fat peak heights were similar to that in phantom, with more overlap of bone marrow and cortical bone than observed in phantom. The relationship between bone marrow and cortical bone for peak integral was better separated than those of peak heights in the patient data. For both the phantom and patient, there was a significant amount of overlap in spectral parameters of cortical bone versus air.

CONCLUSION

These results show promising results for utilizing HiSS imaging in a classification-based synthetic CT algorithm. Cortical bone and air overlap was expected due to the short T2* of bone; reducing early echo times would improve the SNR in bone and image data from these early echoes could help differentiate these tissue types. Further studies need to be done with the goal of better separation of air and bone, and to extend the concept to volumetric imaging before it can be clinically applied.

Evaluation of Soft Tissue Sarcoma Response to Preoperative Chemoradiotherapy Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging

This study aims to assess the utility of quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters in comparison with imaging tumor size for early prediction and evaluation of soft tissue sarcoma response to preoperative chemoradiotherapy. In total, 20 patients with intermediate- to high-grade soft tissue sarcomas received either a phase I trial regimen of sorafenib + chemoradiotherapy (n = 8) or chemoradiotherapy only (n = 12), and underwent DCE-MRI at baseline, after 2 weeks of treatment with sorafenib or after the first chemotherapy cycle, and after therapy completion. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed using the Shutter-Speed model. After only 2 weeks of treatment with sorafenib or after 1 chemotherapy cycle, K^trans (rate constant for plasma/interstitium contrast agent transfer) and its percent change were good early predictors of optimal versus suboptimal pathological response with univariate logistic regression C statistics values of 0.90 and 0.80, respectively, whereas RECIST LD percent change was only a fair predictor (C = 0.72). Post-therapy K^trans, v_e (extravascular and extracellular volume fraction), and k_ep (intravasation rate constant), not RECIST LD, were excellent (C > 0.90) markers of therapy response. Several DCE-MRI parameters before, during, and after therapy showed significant (P < .05) correlations with percent necrosis of resected tumor specimens. In conclusion, absolute values and percent changes of quantitative DCE-MRI parameters provide better early prediction and evaluation of the pathological response of soft tissue sarcoma to preoperative chemoradiotherapy than the conventional measurement of imaging tumor size change.

Relating Doses of Contrast Agent Administered to TIC and Semi-Quantitative Parameters on DCE-MRI: Based on a Murine Breast Tumor Model

Objective

To explore the changes in the time-signal intensity curve(TIC) type and semi-quantitative parameters of dynamic contrast-enhanced(DCE)imaging in relation to variations in the contrast agent(CA) dosage in the Walker 256 murine breast tumor model, and to determine the appropriate parameters for the evaluation ofneoadjuvantchemotherapy(NAC)response.

Materials and Methods

Walker 256 breast tumor models were established in 21 rats, which were randomly divided into three groups of7rats each. Routine scanning and DCE-magnetic resonance imaging (MRI) of the rats were performed using a 7T MR scanner. The three groups of rats were administered different dosages of the CA0.2mmol/kg, 0.3mmol/kg, and 0.5mmol/kg, respectively; and the corresponding TICs the semi-quantitative parameters were calculated and compared among the three groups.

Results

The TICs were not influenced by the CA dosage and presented a washout pattern in all of the tumors evaluated and weren’t influenced by the CA dose. The values of the initial enhancement percentage(E_first), initial enhancement velocity(V_first), maximum signal(S_max), maximum enhancement percentage(E_max), washout percentage(E_wash), and signal enhancement ratio(SER) showed statistically significant differences among the three groups (F = 16.952, p = 0.001; F = 69.483, p<0.001; F = 54.838, p<0.001; F = 12.510, p = 0.003; F = 5.248, p = 0.031; F = 9.733, p = 0.006, respectively). However, the values of the time to peak(T_peak), maximum enhancement velocity(V_max), and washout velocity(V_wash)did not differ significantly among the three dosage groups (F = 0.065, p = 0.937; F = 1.505, p = 0.273; χ2 = 1.423, p = 0.319, respectively); the washout slope(Slope_wash), too, was uninfluenced by the dosage(F = 1.654, p = 0.244).

Conclusion

The CA dosage didn’t affect the TIC type, T_peak, V_max, V_wash or Slope_wash. These dose-independent parameters as well as the TIC type might be more useful for monitoring the NAC response because they allow the comparisons of the DCE data obtained using different CA dosages.

Breast Background Parenchymal Enhancement on Screening Magnetic Resonance Imaging in Women Who Received Chest Radiotherapy for Childhood Hodgkin's Lymphoma

RATIONALE AND OBJECTIVES

Breast magnetic resonance imaging (MRI) is recommended for the screening of women with a history of chest radiotherapy and consequent increased breast cancer risk. The purpose of this study was to evaluate the impact of prior chest radiotherapy on breast tissue background parenchymal enhancement (BPE) at screening breast MRI.

MATERIALS AND METHODS

A departmental database was reviewed to identify asymptomatic women with either a history of chest radiotherapy for Hodgkin's lymphoma or age-matched controls who underwent screening breast MRI between 2009 and 2013. MRI studies were analyzed on an automated breast MRI viewing platform to calculate breast BPE and breast density.

RESULTS

A total of 61 cases (mean age 41.6 ± 6.75 years) and 61 controls (mean age 40.8 ± 6.99 years) were included. The age of patients at the time of chest radiotherapy was 22.6 ± 8.17 years. Screening MRI was performed 19.0 ± 7.43 years after chest radiotherapy. BPE was significantly higher in patients who received chest radiotherapy (50% vs. 37%, P <0.01). A weak to moderate positive correlation (r > 0.3; P < 0.03) was found between BPE and number of years post radiotherapy. There was a trend toward significant difference between the two groups in the correlation of BPE and age (P = 0.05). Breast density was not significantly different between the two groups.

CONCLUSIONS

BPE is significantly greater in women who receive chest radiotherapy for childhood Hodgkin's lymphoma, and unexpectedly, it positively correlates with the number of years passed after radiation therapy. Long-term biological effects of radiation therapy on breast parenchyma need further research.

Quality assurance in MRI breast screening: comparing signal-to-noise ratio in dynamic contrast-enhanced imaging protocols

MRI has been extensively used in breast cancer staging, management and high risk screening. Detection sensitivity is paramount in breast screening, but variations of signal-to-noise ratio (SNR) as a function of position are often overlooked. We propose and demonstrate practical methods to assess spatial SNR variations in dynamic contrast-enhanced (DCE) breast examinations and apply those methods to different protocols and systems. Four different protocols in three different MRI systems (1.5 and 3.0 T) with receiver coils of different design were employed on oil-filled test objects with and without uniformity filters. Twenty 3D datasets were acquired with each protocol; each dataset was acquired in under 60 s, thus complying with current breast DCE guidelines. In addition to the standard SNR calculated on a pixel-by-pixel basis, we propose other regional indices considering the mean and standard deviation of the signal over a small sub-region centred on each pixel. These regional indices include effects of the spatial variation of coil sensitivity and other structured artefacts. The proposed regional SNR indices demonstrate spatial variations in SNR as well as the presence of artefacts and sensitivity variations, which are otherwise difficult to quantify and might be overlooked in a clinical setting. Spatial variations in SNR depend on protocol choice and hardware characteristics. The use of uniformity filters was shown to lead to a rise of SNR values, altering the noise distribution. Correlation between noise in adjacent pixels was associated with data truncation along the phase encoding direction. Methods to characterise spatial SNR variations using regional information were demonstrated, with implications for quality assurance in breast screening and multi-centre trials.

Interpretation of Breast MRI Utilizing the BI-RADS Fifth Edition Lexicon: How Are We Doing and Where Are We Headed?

The Breast Imaging Reporting and Data System (BI-RADS) was first initiated in the late 1980s in order to standardize reporting, improve report organization, and to monitor outcomes for more clear, concise, and uniform communication of breast imaging findings. In the BI-RADS 5th edition, several changes and new additions have been made to the magnetic resonance imaging (MRI) lexicon, reflecting increased utilization and availability of breast MRI in clinical practice. Understanding the role and appropriate utilization of breast MRI and the BI-RADS lexicon could help with interpretation and effective communication of MRI findings as well as preparing for incorporation of more advanced imaging techniques. In this comprehensive review of the changes and new descriptors in the MRI section of the fifth edition of BI-RADS with pictorial examples, the reader would be able to achieve improved understanding of the MRI BI-RADS lexicon and its appropriate applications.

Optimization of Region of Interest Drawing for Quantitative Analysis: Differentiation Between Benign and Malignant Breast Lesions on Contrast-Enhanced Sonography

OBJECTIVES

This study was performed to evaluate the diagnostic utility of quantitative analysis of benign and malignant breast lesions using contrast-enhanced sonography.

METHODS

Contrast-enhanced sonography using the perflubutane-based contrast agent Sonazoid (Daiichi Sankyo, Tokyo, Japan) was performed in 94 pathologically proven palpable breast mass lesions, which could be depicted with B-mode sonography. Quantitative analyses using the time-intensity curve on contrast-enhanced sonography were performed in 5 region of interest (ROI) types (manually traced ROI and circular ROIs of 5, 10, 15, and 20 mm in diameter). The peak signal intensity, initial slope, time to peak, positive enhancement integral, and wash-out ratio were investigated in each ROI.

RESULTS

There were significant differences between benign and malignant lesions in the time to peak (P < .05), initial slope (P < .001), and positive enhancement integral (P < .05) for the manual ROI. Significant differences were found between benign and malignant lesions in the time to peak (P < .05) for the 5-mm ROI; the time to peak (P < .05) and initial slope (P< .05) for the 10-mm ROI; absolute values of the peak signal intensity (P< .05), time to peak (P< .01), and initial slope (P< .005) for the 15-mm ROI; and the time to peak (P < .05) and initial slope (P < .05) for the 20-mm ROI. There were no statistically significant differences in any wash-out ratio values for the 5 ROI types.

CONCLUSIONS

Kinetic analysis using contrast-enhanced sonography is useful for differentiation between benign and malignant breast lesions.

MRI kinetics with volumetric analysis in correlation with hormonal receptor subtypes and histologic grade of invasive breast cancers

OBJECTIVE. The aim of this study was to assess whether computer-assisted detection-processed MRI kinetics data can provide further information on the biologic aggressiveness of breast tumors. MATERIALS AND METHODS. We identified 194 newly diagnosed invasive breast cancers presenting as masses on contrast-enhanced MRI by a HIPAA-compliant pathology database search. Computer-assisted detection-derived data for the mean and median peak signal intensity percentage increase, most suspicious kinetic curve patterns, and volumetric analysis of the different kinetic patterns by mean percentage tumor volume were compared against the different hormonal receptor (estrogen-receptor [ER], progesterone-receptor [PR], ERRB2 (HER2/neu), and triple-receptor expressivity) and histologic grade subgroups, which were used as indicators of tumor aggressiveness. RESULTS. The means and medians of the peak signal intensity percentage increase were higher in ER-negative, PR-negative, and triple-negative (all p ≤ 0.001), and grade 3 tumors (p = 0.011). Volumetric analysis showed higher mean percentage volume of rapid initial enhancement in biologically more aggressive ER-negative, PR-negative, and triple-negative tumors compared with ER-positive (64% vs 53.6%, p = 0.013), PR-positive (65.4% vs 52.5%, p = 0.001), and nontriple-negative tumors (65.3% vs 54.6%, p = 0.028), respectively. A higher mean percentage volume of rapid washout component was seen in ERRB2-positive tumors compared with ERRB2-negative tumors (27.5% vs 17.9%, p = 0.020). CONCLUSION. Peak signal intensity percentage increase and volume analysis of the different kinetic patterns of breast tumors showed correlation with hormonal receptor and histologic grade indicators of cancer aggressiveness. Computer-assisted detection-derived MRI kinetics data have the potential to further characterize the aggressiveness of an invasive cancer.

Comparison of dynamic contrast-enhanced MRI parameters of breast lesions at 1.5 and 3.0 T: a pilot study

OBJECTIVE

To compare dynamic contrast-enhanced (DCE) MRI parameters from scans of breast lesions at 1.5 and 3.0 T.

METHODS

11 patients underwent paired MRI examinations in both Philips 1.5 and 3.0 T systems (Best, Netherlands) using a standard clinical fat-suppressed, T1 weighted DCE-MRI protocol, with 70-76 s temporal resolution. Signal intensity vs time curves were fit with an empirical mathematical model to obtain semi-quantitative measures of uptake and washout rates as well as time-to-peak enhancement (TTP). Maximum percent enhancement and signal enhancement ratio (SER) were also measured for each lesion. Percent differences between parameters measured at the two field strengths were compared.

RESULTS

TTP and SER parameters measured at 1.5 and 3.0 T were similar; with mean absolute differences of 19% and 22%, respectively. Maximum percent signal enhancement was significantly higher at 3 T than at 1.5 T (p = 0.006). Qualitative assessment showed that image quality was significantly higher at 3 T (p = 0.005).

CONCLUSION

Our results suggest that TTP and SER are more robust to field strength change than other measured kinetic parameters, and therefore measurements of these parameters can be more easily standardized than measurements of other parameters derived from DCE-MRI. Semi-quantitative measures of overall kinetic curve shape showed higher reproducibility than do discrete classification of kinetic curve early and delayed phases in a majority of the cases studied.

ADVANCES IN KNOWLEDGE

Qualitative measures of curve shape are not consistent across field strength even when acquisition parameters are standardized. Quantitative measures of overall kinetic curve shape, by contrast, have higher reproducibility.

Investigating the influence of flip angle and k-space sampling on dynamic contrast-enhanced MRI breast examinations

RATIONALE AND OBJECTIVES

To retrospectively investigate the effect of flip angle (FA) and k-space sampling on the performance of dynamic contrast-enhanced (DCE-) magnetic resonance imaging (MRI) breast sequences.

MATERIALS AND METHODS

Five DCE-MRI breast sequences were evaluated (10°, 14°, and 18° FAs; radial or linear k-space sampling), with 7-10 patients in each group (n = 45). All sequences were compliant with current technical breast screening guidelines. Contrast agent (CA) uptake curves were constructed from the right mammary artery for each examination. Maximum relative enhancement, E(max), and time-to-peak enhancement, T(max), were measured and compared between protocols (analysis of variance and Mann-Whitney). For each sequence, calculated values of maximum relative enhancement, E(calc), were derived from the Bloch equations and compared to E(max). Fat suppression performance (residual bright fat and chemical shift artifact) was rated for each examination and compared between sequences (Fisher exact tests).

RESULTS

Significant differences were identified between DCE-MRI sequences. E(max) increased significantly at higher FAs and with linear k-space sampling (P < .0001; P = .001). Radial protocols exhibited greater T(max) than linear protocols at FAs of both 14° (P = .025) and 18° (P < .0001), suggesting artificially flattened uptake curves. Good correlation was observed between E(calc) and E(max) (r = 0.86). Fat suppression failure was more pronounced at an FA of 18° (P = .008).

CONCLUSIONS

This retrospective approach is validated as a tool to compare and optimize breast DCE-MRI sequences. Alterations in FA and k-space sampling result in significant differences in CA uptake curve shape which could potentially affect diagnostic interpretation. These results emphasize the need for careful parameter selection and greater standardization of breast DCE-MRI sequences.

Intraindividual, randomized comparison of the macrocyclic contrast agents gadobutrol and gadoterate meglumine in breast magnetic resonance imaging

OBJECTIVES

To compare intraindividually two macrocyclic contrast agents - gadobutrol and gadoterate meglumine (Gd-DOTA) - for dynamic and quantitative assessment of relative enhancement (RE) in benign and malignant breast lesions.

METHODS

This was an ethically approved, prospective, single-centre, randomized, crossover study in 52 women with suspected breast lesions referred for magnetic resonance imaging (MRI). Each patient underwent one examination with gadobutrol and one with Gd-DOTA (0.1 mmol/kg BW) on a 1.5 T system 1 - 7 days apart. Dynamic, T1-weighted, 3D gradient echo sequences were acquired under identical conditions. Quantitative evaluation with at least three regions of interest (ROI) per lesion was performed. Primary endpoint was RE during the initial postcontrast phase after the first and second dynamic acquisition, and peak RE. All lesions were histologically proven; differences between the examinations were evaluated.

RESULTS

Forty-five patients with a total of 11 benign and 34 malignant lesions were assessed. Mean RE was significantly higher for gadobutrol than Gd-DOTA (p < 0.0001). Gadobutrol showed significantly less washout (64.4 %) than Gd-DOTA (75.4 %) in malignant lesions (p = 0.048) CONCLUSIONS: Gadobutrol has higher RE values compared with Gd-DOTA, whereas Gd-DOTA shows more marked washout in malignant lesions. This might improve the detection of breast lesions and influence the specificity of breast MRI-imaging.

Breast DCE-MRI: influence of postcontrast timing on automated lesion kinetics assessments and discrimination of benign and malignant lesions

RATIONALE AND OBJECTIVES

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scanning protocols vary widely. The purpose of this study was to determine the effects of postcontrast timing on delayed-phase lesion kinetics assessment and ability to discriminate malignant from benign lesions.

MATERIALS AND METHODS

Following institutional review board approval, we retrospectively reviewed all lesions assessed on magnetic resonance examinations from April 2005 to June 2006. DCE-MRI was performed with 90-second temporal resolution. Delayed-phase kinetic parameters including percentages of persistent, plateau, and washout, and categorizations of predominant and worst curve type were compared between 4.5 and 7.5 minutes postcontrast. Ability to discriminate benign and malignant lesions based on delayed-phase kinetic parameters was compared between postcontrast timings by receiver operating characteristic (ROC) analysis.

RESULTS

Two hundred eighty consecutive breast lesions (206 malignant and 74 benign) were evaluated in 228 women. Comparing kinetics assessments at 7.5 versus 4.5 minutes: volume percentage of washout increased in malignancies by a mean of 9.4% (P<.0001) and increased slightly in benign lesions (mean 3.2%, P=.007); predominant curve type categorizations changed significantly only for malignancies (P<.0001); and worst curve categorizations did not change significantly for either benign or malignant lesions (P>.05). There were no significant differences between timings in area under ROC curves for delayed-phase kinetic parameters.

CONCLUSIONS

The choice of delayed postcontrast timing more strongly affects the kinetics assessments for malignancies than benign breast lesions, but our results suggest that a shortened breast DCE-MRI protocol may not significantly impact diagnostic accuracy. Furthermore, worst curve type classifications are least affected by postcontrast timing and may provide reliable assessment of delayed-phase kinetics across protocols.

3.0T MR-CAD: Clinical Value in Diagnosis of Breast Tumor Compared with Conventional MRI

Purpose: to explore the clinical value of 3.0T magnetic resonance (MR) imaging compared with computer-aided MR diagnosis (MR-CAD) in differential diagnosis of benign and malignant breast tumors.

Materials and Methods: MRI method and MR-CAD method was used in the diagnosis of a total of 93 breast lesions of 78 patients, based on the morphological and time-intensity-curve (TIC) analysis. The accuracy of the two modalities in differentiating malignant and benign breast tumor was compared.

Results: MR-CAD method yielded a statistically better accuracy than MRI method. For 51 mass-like lesions, MRI and MR-CAD had no difference in diagnosing accuracy, but MR-CAD had better accuracy in 42 non-mass-like lesions.

Conclusion: MR-CAD had a notable advantage over MRI in differential diagnosis of benign and malignant breast tumors, especially non-mass-like tumor.

Invasive breast carcinoma: influence of prognosis and patient-related factors on kinetic MR imaging characteristics

PURPOSE

To retrospectively compare the kinetic magnetic resonance (MR) imaging characteristics of invasive breast carcinomas with both prognostic tumoral and patient-related parameters.

MATERIALS AND METHODS

This HIPAA-compliant retrospective study was approved by the institutional review board, and informed consent was waived. From January 2008 to January 2011, 273 consecutive women (mean age, 55 years; range, 23-83 years) with invasive breast cancers who had undergone MR imaging were selected. The kinetic curves were retrospectively classified according to the Breast Imaging Reporting and Data System lexicon. Initial enhancement and maximal enhancement percentages, time to peak enhancement, and the signal enhancement ratio were calculated for each lesion. Kinetic characteristics were compared according to tumoral parameters (size, pathologic type, grade, hormone receptor status, and c-erbB-2 status) and patient parameters (menopausal status, personal history of breast carcinoma) by means of univariate and then multivariate analysis by using false-discovery-rate statistics.

RESULTS

Lesions in menopausal patients exhibited less suspicious quantitative and qualitative characteristics than lesions in nonmenopausal patients. There was an independent association between the kinetic characteristics and menopausal status, with an odds ratio of 2.94 for the lack of rapid initial contrast material uptake and of 2.38 for the lack of washout in menopausal patients as compared with nonmenopausal patients. The odds ratio was 4.00 for not having rapid initial contrast material uptake in patients with a personal history of ipsilateral breast cancer.

CONCLUSION

Kinetic data in invasive breast cancer are associated with the patient's menopausal status, with a typical kinetic pattern of malignancy being less common in menopausal patients.

Breast dynamic contrast-enhanced examinations with fat suppression: are contrast-agent uptake curves affected by magnetic field inhomogeneity?

OBJECTIVES

To investigate the effect of magnetic field heterogeneity in breast dynamic contrast-enhanced examinations with fat saturation (DCE-FS).

METHODS

The magnetic field was mapped over the breasts in ten patients. DCE-FS was undertaken at 1.5 T with fast spoiled gradient echoes and spectrally selective fat saturation. Signal intensity was calculated for T1 values 25-1,200 ms both on and off resonance, and results were verified with a test object. Clinical examinations were evaluated for the predicted effects of field heterogeneity.

RESULTS

Magnetic field was found to vary by 3.6 ± 1.2 ppm over the central transaxial slice and 5.1 ± 1.5 over the whole breast volume (mean ± standard deviation). Computer simulations predict a reduction in the dynamic range if field heterogeneity leads to unintended water suppression, and distortion to CA uptake curves due to fat suppression failure (for fat containing pixels). A compromise between dynamic range and fat saturation performance is required. Both water suppression and fat suppression failure are apparent in clinical examinations.

CONCLUSION

Magnetic field heterogeneity is likely to reduce the sensitivity of DCE-FS by distorting the CA uptake curves because of fat suppression failure (for fat containing pixels) and by reducing the dynamic range because of unintended water suppression.

KEY POINTS

• Magnetic field heterogeneity is significant in breast magnetic resonance. • Contrast-agent uptake curves are distorted by a non-uniform magnetic field. • Radiologist must be aware of possibility of distortion to interpret uptake curves correctly. • Compromise between fat suppression and dynamic range is required.

A new user-friendly visual environment for breast MRI data analysis

In this paper a novel, user friendly visual environment for Breast MRI Data Analysis is presented (BreDAn). Given planar MRI images before and after IV contrast medium injection, BreDAn generates kinematic graphs, color maps of signal increase and decrease and finally detects high risk breast areas. The advantage of BreDAn, which has been validated and tested successfully, is the automation of the radiodiagnostic process in an accurate and reliable manner. It can potentially facilitate radiologists' workload.

A prospective study of the utility of magnetic resonance imaging in determining candidacy for partial breast irradiation

PURPOSE

Retrospective data have demonstrated that breast magnetic resonance imaging (MRI) may change a patient's eligibility for partial breast irradiation (PBI) by identifying multicentric, multifocal, or contralateral disease. The objective of the current study was to prospectively determine the frequency with which MRI identifies occult disease and to establish clinical factors associated with a higher likelihood of MRI prompting changes in PBI eligibility.

METHODS AND MATERIALS

At The University of Chicago, women with breast cancer uniformly undergo MRI in addition to mammography and ultrasonography. From June 2009 through May 2011, all patients were screened prospectively in a multidisciplinary conference for PBI eligibility based on standard imaging, and the impact of MRI on PBI eligibility according to National Surgical Adjuvant Breast and Bowel Project protocol B-39/Radiation Therapy Oncology Group protocol 0413 entry criteria was recorded. Univariable analysis was performed using clinical characteristics in both the prospective cohort and in a separate cohort of retrospectively identified patients. Pooled analysis was used to derive a scoring index predictive of the risk that MRI would identify additional disease.

RESULTS

A total of 521 patients were screened for PBI eligibility, and 124 (23.8%) patients were deemed eligible for PBI based on standard imaging. MRI findings changed PBI eligibility in 12.9% of patients. In the pooled univariable analysis, tumor size ≥ 2 cm on mammography or ultrasonography (P=.02), age <50 years (P=.01), invasive lobular histology (P=.01), and HER-2/neu amplification (P=.01) were associated with a higher likelihood of MRI changing PBI eligibility. A predictive score was generated by summing the number of significant risk factors. Patients with a score of 0, 1, 2, and 3 had changes to eligibility based on MRI findings in 2.8%, 13.2%, 38.1%, and 100%, respectively (P<.0001).

CONCLUSIONS

MRI identified additional disease in a significant number of patients eligible for PBI, based on standard imaging. Clinical characteristics may be useful in directing implementation of MRI in the staging of PBI candidates.

Contrast media in breast imaging

Although mammography is the standard imaging modality for detection of breast cancer, magnetic resonance (MR) imaging is a valuable adjunct and, in certain cases, is the imaging of choice. Contrast-enhanced breast MR imaging provides a noninvasive means of staging disease, assessing posttreatment response, and screening of high-risk patients with genetic predispositions. Additional indications for MR mammography include lesion characterization, contralateral breast evaluation in patients with proved malignancy, and identifying primary malignancy in patients with axillary nodal disease. There are several competing factors that influence the quality of the study. Finding the right balance is the key to providing high-quality images that can be accurately interpreted.

A feasible high spatiotemporal resolution breast DCE-MRI protocol for clinical settings

Three dimensional bilateral imaging is the standard for most clinical breast dynamic contrast-enhanced (DCE) MRI protocols. Because of high spatial resolution (sRes) requirement, the typical 1-2 min temporal resolution (tRes) afforded by a conventional full-k-space-sampling gradient echo (GRE) sequence precludes meaningful and accurate pharmacokinetic analysis of DCE time-course data. The commercially available, GRE-based, k-space undersampling and data sharing TWIST (time-resolved angiography with stochastic trajectories) sequence was used in this study to perform DCE-MRI exams on thirty one patients (with 36 suspicious breast lesions) before their biopsies. The TWIST DCE-MRI was immediately followed by a single-frame conventional GRE acquisition. Blinded from each other, three radiologist readers assessed agreements in multiple lesion morphology categories between the last set of TWIST DCE images and the conventional GRE images. Fleiss' κ test was used to evaluate inter-reader agreement. The TWIST DCE time-course data were subjected to quantitative pharmacokinetic analyses. With a four-channel phased-array breast coil, the TWIST sequence produced DCE images with 20 s or less tRes and ~ 1.0×1.0×1.4 mm(3) sRes. There were no significant differences in signal-to-noise (P=.45) and contrast-to-noise (P=.51) ratios between the TWIST and conventional GRE images. The agreements in morphology evaluations between the two image sets were excellent with the intra-reader agreement ranging from 79% for mass margin to 100% for mammographic density and the inter-reader κ value ranging from 0.54 (P<.0001) for lesion size to 1.00 (P<.0001) for background parenchymal enhancement. Quantitative analyses of the DCE time-course data provided higher breast cancer diagnostic accuracy (91% specificity at 100% sensitivity) than the current clinical practice of morphology and qualitative kinetics assessments. The TWIST sequence may be used in clinical settings to acquire high spatiotemporal resolution breast DCE-MRI images for both precise lesion morphology characterization and accurate pharmacokinetic analysis.

Effect of protocol parameters on contrast agent washout curve separability in breast dynamic contrast enhanced MRI: a simulation study

Variability in diagnostic performance of breast dynamic contrast-enhanced MRI has highlighted the need for improved standardization. While guidance exists on some aspects of the technique, currently, there is no standardized method for selecting repetition time and flip angle, which are important determinants of image contrast. This study develops a theoretical framework for quantitative optimization of temporal aspects of dynamic contrast-enhanced MRI based on area under the receiver operating curve. Optimizations in simulation demonstrate the potential for increases in area under the receiver operating characteristic curve by up to 0.20 and specificity at a sensitivity of 90% by up to 19%, depending on the protocol. These results suggest that careful selection of repetition time and flip angle can improve diagnostic performance and identify these quantities as potentially important parameters for future standardization.

The diverse pathology and kinetics of mass, nonmass, and focus enhancement on MR imaging of the breast

PURPOSE

To compare the pathology and kinetic characteristics of breast lesions with focus-, mass-, and nonmass-like enhancement.

MATERIALS AND METHODS

A total of 852 MRI detected breast lesions in 697 patients were selected for an IRB approved review. Patients underwent dynamic contrast enhanced MRI using one pre- and three to six postcontrast T(1)-weighted images. The "type" of enhancement was classified as mass, nonmass, or focus, and kinetic curves quantified by the initial enhancement percentage (E(1)), time to peak enhancement (T(peak)), and signal enhancement ratio (SER). These kinetic parameters were compared between malignant and benign lesions within each morphologic type.

RESULTS

A total of 552 lesions were classified as mass (396 malignant, 156 benign), 261 as nonmass (212 malignant, 49 benign), and 39 as focus (9 malignant, 30 benign). The most common pathology of malignant/benign lesions by morphology: for mass, invasive ductal carcinoma/fibroadenoma; for nonmass, ductal carcinoma in situ (DCIS)/fibrocystic change(FCC); for focus, DCIS/FCC. Benign mass lesions exhibited significantly lower E(1), longer T(peak), and lower SER compared with malignant mass lesions (P < 0.0001). Benign nonmass lesions exhibited only a lower SER compared with malignant nonmass lesions (P < 0.01).

CONCLUSION

By considering the diverse pathology and kinetic characteristics of different lesion morphologies, diagnostic accuracy may be improved.

Development and characterization of a dynamic lesion phantom for the quantitative evaluation of dynamic contrast-enhanced MRI

PURPOSE

To develop a dynamic lesion phantom that is capable of producing physiological kinetic curves representative of those seen in human dynamic contrast-enhanced MRI (DCE-MRI) data. The objective of this phantom is to provide a platform for the quantitative comparison of DCE-MRI protocols to aid in the standardization and optimization of breast DCE-MRI.

METHODS

The dynamic lesion consists of a hollow, plastic mold with inlet and outlet tubes to allow flow of a contrast agent solution through the lesion over time. Border shape of the lesion can be controlled using the lesion mold production method. The configuration of the inlet and outlet tubes was determined using fluid transfer simulations. The total fluid flow rate was determined using x-ray images of the lesion for four different flow rates (0.25, 0.5, 1.0, and 1.5 ml/s) to evaluate the resultant kinetic curve shape and homogeneity of the contrast agent distribution in the dynamic lesion. High spatial and temporal resolution x-ray measurements were used to estimate the true kinetic curve behavior in the dynamic lesion for benign and malignant example curves. DCE-MRI example data were acquired of the dynamic phantom using a clinical protocol.

RESULTS

The optimal inlet and outlet tube configuration for the lesion molds was two inlet molds separated by 30° and a single outlet tube directly between the two inlet tubes. X-ray measurements indicated that 1.0 ml/s was an appropriate total fluid flow rate and provided truth for comparison with MRI data of kinetic curves representative of benign and malignant lesions. DCE-MRI data demonstrated the ability of the phantom to produce realistic kinetic curves.

CONCLUSIONS

The authors have constructed a dynamic lesion phantom, demonstrated its ability to produce physiological kinetic curves, and provided estimations of its true kinetic curve behavior. This lesion phantom provides a tool for the quantitative evaluation of DCE-MRI protocols, which may lead to improved discrimination of breast cancer lesions.