Dynamic Breast Magnetic Resonance Imaging Research Articles

Diagnostic performance of minimum apparent diffusion coefficient value in differentiating the invasive breast cancer and ductal carcinoma in situ.

This study is to explore the role of the minimum apparent diffusion coefficient (ADC-min) value in the diagnosis of invasive breast cancer and ductal carcinoma in situ (DCIS). A total of 196 breast cancer patients with pathologically verified lesions were included. They received diffusion-weighted imaging and dynamic breast magnetic resonance imaging before the pathological confirmation. The ADC-min value and its relationship with invasive ductal carcinoma (IDC), IDC-DCIS, and DCIS were analyzed. Of the 196 breast cancer patients, there were 115 (58.67%) cases of IDC, 53 (27.04%) cases of IDC-DCIS, and 28 (14.29%) cases of DCIS. The mean ADC-min values for IDC, IDC-DCIS, and DCIS were (0.96 ± 0.16) × 10-3, (1.10 ± 0.13) × 10-3, and (1.24 ± 0.17) × 10-3 mm 2/s, respectively. The mean ADC-min value of IDC was significantly lower than that of IDC-DCIS and that of IDC-DCIS was significantly lower than that of DCIS (P < 0.01). The mean ADC-min value was also significantly different between invasive cancer and DCIS (P < 0.01). The mean ADC-min value can be used in the differential diagnosis of DCIS, with a cutoff point of 1.02 × 10-3 mm 2/s (sensitivity of 92.9% and specificity of 57.7%). The ADC-min values are significantly different among IDC, IDC-DCIS, and DCIS, with the lowest ADC-min values in IDC, followed by IDC-DCIS and DCIS. The ADC-min maybe used as a promising parameter to differentiate DCIS and invasive cancer.

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.

ObjectivesTo 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 methodsBetween 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.ResultsThere 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.ConclusionsPatients 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.

Correlation between minimum apparent diffusion coefficient values and the histological grade of breast invasive ductal carcinoma.

The present study aimed to investigate the correlation between the minimum apparent diffusion coefficient (ADCmin) value and the histological grade of breast invasive ductal carcinoma (IDC). In total, 129 pathologically verified lesions that were subjected to dynamic breast magnetic resonance imaging and diffusion weighted imaging prior to biopsy were included. The ADCmin value was calculated and its correlation with the tumor histological grade was investigated. Tumors of lower grades demonstrated significantly higher ADCmin values as compared with tumors of higher grades (F=33.49; P<0.01). The mean ADCmin values for IDC of grades I, II and III were (1.14±0.11)×10-3, (0.99±0.12)×10-3 and (0.86±0.13)×10-3 mm2/sec, respectively. Statistically significant differences were detected in the mean ADCmin value between tumors of grades II and III (P<0.01), as well as between tumors of grades I and II (P<0.01). In addition, the mean ADCmin values for the less aggressive (grades I and II) and more aggressive (grade III) groups were (1.01±0.13)×10-3 and (0.86±0.13)×10-3 mm2/sec, respectively (t=5.76, P<0.01). In conclusion, these data indicated that the ADCmin value was correlated with the IDC histological grade, and lower ADCmin values were associated with a higher histological grade and more aggressiveness. Thus, the ADCmin value may be considered as a promising prognostic parameter in identifying tumor aggressiveness.

Breast MRI at very short TE (minTE): image analysis of minTE sequences on non-fat-saturated, subtracted T1-weighted images

Purpose The aim was to evaluate a minimum echo time (minTE) protocol for breast magnetic resonance imaging (MRI) in patients with breast lesions compared to a standard TE (nTE) time protocol. Methods Breasts of 144 women were examined with a 1.5 Tesla MRI scanner. Additionally to the standard gradient-echo sequence with nTE (4.8 ms), a variant with minimum TE (1.2 ms) was used in an interleaved fashion which leads to a better temporal resolution and should reduce the scan time by approximately 50 %. Lesion sizes were measured and the signal-to-noise ratio (SNR) as well as the contrast-to-noise ratio (CNR) were calculated. Subjective confidence was evaluated using a 3-point scale before looking at the nTE sequences (1 = very sure that I can identify a lesion and classify it, 2 = quite sure that I can identify a lesion and classify it, 3 = definitely want to see nTE for final assessment) and the subjective image quality of all examinations was evaluated using a four-grade scale (1 = sharp, 2 = slight blur, 3 = moderate blur and 4 = severe blur/not evaluable) for lesion and skin sharpness. Lesion morphology and contrast enhancement were also evaluated. Results With minTE sequences, no lesion was rated with “definitely want to see nTE sequences for final assessment”. The difference of the longitudinal and transverse diameter did not differ significantly (p &gt; 0.05). With minTE, lesions and skin were rated to be significantly more blurry (p &lt; 0.01 for lesions and p &lt; 0.05 for skin). There was no difference between both sequences with respect to SNR, CNR, lesion morphology, contrast enhancement and detection of multifocal disease. Conclusion Dynamic breast MRI with a minTE protocol is feasible without a major loss of information (SNR, CNR, lesion morphology, contrast enhancement and lesion sizes) and the temporal resolution can be increased by a factor of 2 using minTE sequences. Key points

Contrast Enhancement in Breast Cancer and Background Mammary-Gland Tissue During the Super-Early Phase of Dynamic Breast Magnetic Resonance Imaging

We aimed to compare the contrast enhancement between tumor and mammary-gland tissue to distinguish lesions in the super-early phase, during which minimal contrast media uptake is observed in mammary-gland tissue. Dynamic magnetic resonance imaging, including the super-early phase with bolus tracking (BT) method (to determine the optimal imaging start time), was performed by using identical parameters to obtain transverse fat-suppressed T1-weighted images of both breasts. The percent enhancement (PE) and the contrast ratio (CR) indicators for tumor and mammary-gland tissue were assessed in each dynamic phase. The PE values of the tumor were 62.4% and 151.6%, and those of the mammary gland were 0.3% and 20.7% in the super-early and early phases, respectively. Therefore, virtually no background parenchymal enhancement was observed in the super-early phase. The variation in the PE values during the super-early phase was significantly smaller when the values were determined with the BT method (P < .05). The CR was highest in the early phase, and the CR in the super-early phase was lower than in the other phases. Early-phase PE and CR were significantly higher in invasive cancer cases than in noninvasive cancer cases (P < .01). A significant difference in the imaging start time was observed for the anatomic side factor by the BT method. Background parenchymal enhancement almost never appeared in the super-early phase, but the CR was lower in the super-early phase than in the early phase. The BT method allowed for an optimal imaging start time for the super-early phase and yielded images with less deviation of contrast enhancement.

Breast fat volume measurement using wide-bore 3 T MRI: comparison of traditional mammographic density evaluation with MRI density measurements using automatic segmentation

To compare magnetic resonance imaging (MRI)-derived breast density measurements using automatic segmentation algorithms with radiologist estimations using the Breast Imaging Reporting and Data Systems (BI-RADS) density classification. Forty women undergoing mammography and dynamic breast MRI as part of their clinical management were recruited. Fat-water separated MRI images derived from a two-point Dixon technique, phase-sensitive reconstruction, and atlas-based segmentation were obtained before and after intravenous contrast medium administration. Breast density was assessed using software from Advanced MR Analytics (AMRA), Linköping, Sweden, with results compared to the widely used four-quartile quantitative BI-RADS scale. The proportion of glandular tissue in the breast on MRI was derived from the AMRA sequence. The mean unenhanced breast density was 0.31±0.22 (mean±SD; left) and 0.29±0.21 (right). Mean breast density on post-contrast images was 0.32±0.19 (left) and 0.32±0.2 (right). There was "almost perfect" correlation between pre- and post-contrast breast density quantification: Spearman's correlation rho=0.98 (95% confidence intervals [CI]: 0.97-0.99; left) and rho=0.99 (95% CI: 0.98-0.99; right). The 95% limits of agreement were -0.11-0.08 (left) and -0.08-0.03 (right). Interobserver reliability for BI-RADS was "substantial": weighted Kappa k=0.8 (95% CI: 0.74-0.87). The Spearman correlation coefficient between BI-RADS and MRI breast density was rho=0.73 (95% CI: 0.60-0.82; left) and rho=0.75 (95% CI: 0.63-0.83; right) which was also "substantial". The AMRA sequence provides a fully automated, reproducible, objective assessment of fibroglandular breast tissue proportion that correlates well with mammographic assessment of breast density with the added advantage of avoidance of ionising radiation.

Relationship between morphological features and kinetic patterns of enhancement of the dynamic breast magnetic resonance imaging and tumor expression of metalloproteases and their inhibitors in invasive breast cancer

AimMatrix metalloproteases (MMPs) expression and their inhibitors (TIMPs) play an important role in tumor physiopathology, so we investigated the relationship between the magnetic resonance (MR) and MMPs/TIMPs expression by breast carcinomas. Materials and methodsMRI parameters of 64 breast carcinomas were investigated. An immunohistochemical study was also performed in these cases using tissue microarrays and specific antibodies against MMP-1, MMP-2, MMP-7, MMP-9, MMP-11, MMP-13, MMP-14, TIMP-1, TIMP-2 and TIMP-3. ResultsTumors with spiculated margins had a high global (score) values of MMP-1 or MMP-7, and high expression of TIMP-3 by tumor cells. Heterogeneous tumors had a higher score values of MMP-1, MMP-13, TIMP-2 or TIMP-3, and frequent expression of TIMP-3 by tumor cells. Tumors showing fast enhancement, had higher score values of MMP-1 or MMP-11. Associations between washout curve (type III) and MMP-1, MMP-11, MMP-13 and TIMP-1 expression by tumor cells, were found. ConclusionsMRI features may predict in some grade the expressions of MMPs/TIMPs in breast tumors, which might to contribute to a better biological characterization of breast cancer.

Differentiation between Phyllodes Tumors and Fibroadenomas Based on Mammographic Sonographic and MRI Features

Background: This study was performed to compare the mammographic, sonographic, and magnetic resonance imaging (MRI) characteristics of phyllodes tumors and fibroadenomas, which may resemble each other. Methods: Preoperative mammograms, B-mode and Doppler sonograms, and dynamic breast MRIs of 72 patients with pathologically proven fibroadenomas and 70 patients with pathologically proven phyllodes tumor were evaluated in this retrospective study. Statistical significance was evaluated using chi-square and Fisher's exact tests. Correlations in lesion size among radiological methods were examined by Pearson's correlation analysis. Results: The features that differed on mammogram were size, shape, and margin of the mass. Sonograms showed significant differences in size, shape, margin, echo pattern, and vascularization of the mass. Pearson's correlation analysis showed strong agreement among radiological methods in terms of assessment of size. Tumor size ≥ 3 cm, irregular shape, microlobulated margins, complex internal echo pattern, and hypervascularity were significant findings of phyllodes tumors. Internal cystic areas on MRI were frequently associated with phyllodes tumors. Conclusion: Mammographic, sonographic, and MRI findings of fibroadenomas and phyllodes tumors could help radiologists to ascertain imaging-histological concordance and guide clinicians in their decision making regarding adequate follow-up or the necessity of biopsy.

MR Imaging as an Additional Screening Modality for the Detection of Breast Cancer in Women Aged 50-75 Years with Extremely Dense Breasts: The DENSE Trial Study Design.

Women with extremely dense breasts have an increased risk of breast cancer and lower mammographic tumor detectability. Nevertheless, in most countries, these women are currently screened with mammography only. Magnetic resonance (MR) imaging has the potential to improve breast cancer detection at an early stage because of its higher sensitivity. However, MR imaging is more expensive and is expected to be accompanied by an increase in the number of false-positive results and, possibly, an increase in overdiagnosis. To study the additional value of MR imaging, a randomized controlled trial (RCT) design is needed in which one group undergoes mammography and the other group undergoes mammography and MR imaging. With this design, it is possible to determine the proportion of interval cancers within each study arm. For this to be an effective screening strategy, the additional cancers detected at MR imaging screening must be accompanied by a subsequent reduction in interval cancers. The Dense Tissue and Early Breast Neoplasm Screening, or DENSE, trial is a multicenter RCT performed in the Dutch biennial population-based screening program (subject age range, 50-75 years). The study was approved by the Dutch Minister of Health, Welfare and Sport. In this study, mammographic density is measured by using a fully automated volumetric method. Participants with extremely dense breasts (American College of Radiology breast density category 4) and a negative result at mammography (Breast Imaging Recording and Data System category 1 or 2) are randomly assigned to undergo additional MR imaging (n = 7237) or to be treated according to current practice (n = 28 948). Participants provide written informed consent before the MR imaging examination, which consists of dynamic breast MR imaging with gadolinium-based contrast medium and is intended to be performed for three consecutive screening rounds. The primary outcome is the difference in the proportions of interval cancers between the study arms. Secondary outcomes are the number of MR imaging screening-detected cancers, proportions of false-positive results, diagnostic yield of MR imaging, tumor characteristics, quality of life, and cost effectiveness.

Adjacent vessel sign and breast imaging reporting and data system are valuable for diagnosis of benign and malignant breast lesions

The purpose of this study is to investigate whether an adjacent vessel sign (AVS) observed on the maximum intensity projections (MIPs) from the subtracted images can help distinguish between malignant and benign breast lesions and to test whether the combination of breast imaging reporting and data system (BI-RADS) category and AVS can increase the specificity and diagnostic accuracy of breast magnetic resonance imaging (MRI). The study included 63 histologically verified lesions which underwent dynamic breast MRI before biopsy. All magnetic resonance (MR) images were evaluated by two radiologists in consensus, who were unaware of the histopathological outcome. The MR images of all cases were analyzed according to BI-RADS-MRI assessment category. Levels of suspicion were reported as categories of I–V. The presence of vessels either entering the enhancing lesion or in contact with the lesion edge on MIP images was considered as the presence of AVS. Final analysis of 63 masses revealed 41 malignant lesions (65.1%) and 22 benign lesions (34.9%). Thirty seven out of 41 malignant lesions and 3 out of 22 benign lesions were associated with adjacent vessel, with highly significant difference between benign and malignant lesions (P < 0.001), especially for lesions smaller than 2.0 cm. The corresponding specificity, sensitivity and accuracy of contrast-enhanced 3.0-T breast were 86.4%, 82.9% and 84.1%, respectively. Based on BI-RADS-MRI category, the specificity, sensitivity and accuracy of breast MRI were 54.5%, 100% and 84.1%, respectively. After combining BI-RADS category and AVS, the specificity, sensitivity and accuracy of breast MRI were 90.9%, 82.9% and 85.7%, respectively. AVS can help differentiate malignant from benign breast lesions, especially for the lesions smaller than 2.0 cm. The combination of BI-RADS category and AVS can increase the specificity and the diagnostic accuracy of breast MRI.

A Novel Approach to Contrast-Enhanced Breast Magnetic Resonance Imaging for Screening

The use of breast magnetic resonance imaging (MRI) as screening tool has been stalled by high examination costs. Scan protocols have lengthened to optimize specificity. Modern view-sharing sequences now enable ultrafast dynamic whole-breast MRI, allowing much shorter and more cost-effective procedures. This study evaluates whether dynamic information from ultrafast breast MRI can be used to replace standard dynamic information to preserve accuracy. We interleaved 20 ultrafast time-resolved angiography with stochastic trajectory (TWIST) acquisitions (0.9 × 1 × 2.5 mm, temporal resolution, 4.3 seconds) during contrast inflow in a regular high-resolution dynamic MRI protocol. A total of 160 consecutive patients with 199 enhancing abnormalities (95 benign and 104 malignant) were included. The maximum slope of the relative enhancement versus time curve (MS) obtained from the TWIST and curve type obtained from the regular dynamic sequence as defined in the breast imaging reporting and data system (BIRADS) lexicon were recorded. Diagnostic performance was compared using receiver operating characteristic analysis. All lesions were visible on both the TWIST and standard series. Maximum slope allows discrimination between benign and malignant disease with high accuracy (area under the curve, 0.829). Types of MS were defined in analogy to BIRADS curve types: MS type 3 implies a high risk of malignancy (MS >13.3%/s; specificity, 85%), MS type 2 yields intermediate risk (MS <13.3%/s and >6.4%/s), and MS type 1 implies a low risk (MS <6.4%/s; sensitivity, 90%). This simplification provides a much higher accuracy than the much lengthier BIRADS curve type analysis does (area under the curve, 0.812 vs 0.692; P = 0.0061). Ultrafast dynamic breast MRI allows detection of breast lesions and classification with high accuracy using MS. This allows substantial shortening of scan protocols and hence reduces imaging costs, which is beneficial especially for screening.

Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology

While dimension reduction has been previously explored in computer aided diagnosis (CADx) as an alternative to feature selection, previous implementations of its integration into CADx do not ensure strict separation between training and test data required for the machine learning task. This compromises the integrity of the independent test set, which serves as the basis for evaluating classifier performance. We propose, implement and evaluate an improved CADx methodology where strict separation is maintained. This is achieved by subjecting the training data alone to dimension reduction; the test data is subsequently processed with out-of-sample extension methods. Our approach is demonstrated in the research context of classifying small diagnostically challenging lesions annotated on dynamic breast magnetic resonance imaging (MRI) studies. The lesions were dynamically characterized through topological feature vectors derived from Minkowski functionals. These feature vectors were then subject to dimension reduction with different linear and non-linear algorithms applied in conjunction with out-of-sample extension techniques. This was followed by classification through supervised learning with support vector regression. Area under the receiver-operating characteristic curve (AUC) was evaluated as the metric of classifier performance. Of the feature vectors investigated, the best performance was observed with Minkowski functional 'perimeter' while comparable performance was observed with 'area'. Of the dimension reduction algorithms tested with 'perimeter', the best performance was observed with Sammon's mapping (0.84±0.10) while comparable performance was achieved with exploratory observation machine (0.82±0.09) and principal component analysis (0.80±0.10). The results reported in this study with the proposed CADx methodology present a significant improvement over previous results reported with such small lesions on dynamic breast MRI. In particular, non-linear algorithms for dimension reduction exhibited better classification performance than linear approaches, when integrated into our CADx methodology. We also note that while dimension reduction techniques may not necessarily provide an improvement in classification performance over feature selection, they do allow for a higher degree of feature compaction.

Classification of Small Lesions in Breast MRI: Evaluating The Role of Dynamically Extracted Texture Features Through Feature Selection.

Dynamic texture quantification, i.e., extracting texture features from the lesion enhancement pattern in all available post-contrast images, has not been evaluated in terms of its ability to classify small lesions. This study investigates the classification performance achieved with texture features extracted from all five post-contrast images of lesions (mean lesion diameter of 1.1 cm) annotated in dynamic breast magnetic resonance imaging exams. Sixty lesions are characterized dynamically using Haralick texture features. The texture features are then used in a classification task with support vector regression and a fuzzy k-nearest neighbor classifier; free parameters of these classifiers are optimized using random sub-sampling cross-validation. Classifier performance is determined through receiver-operator characteristic (ROC) analysis, specifically through computation of the area under the ROC curve (AUC). Mutual information is used to evaluate the contribution of texture features extracted from different post-contrast stages to classifier performance. Significant improvements (p < 0.05) are observed for six of the thirteen texture features when the lesion enhancement pattern is quantified using the proposed approach of dynamic texture quantification. The highest AUC value observed (0.82) is achieved with texture features responsible for capturing aspects of lesion heterogeneity. Mutual information analysis reveals that texture features extracted from the third and fourth post-contrast images contributed most to the observed improvement in classifier performance. These results show that the performance of automated character classification with small lesions can be significantly improved through dynamic texture quantification of the lesion enhancement pattern.

Effect of the Menstrual Cycle on Background Parenchymal Enhancement in Breast MR Imaging

We assessed the influence of the menstrual cycle on background parenchymal enhancement (BPE) of the breast in the early and delayed phases of dynamic magnetic resonance (MR) imaging and the optimal timing of MR imaging of the breast in Japanese women. We reviewed dynamic MR images of 165 consecutive women with regular menstrual cycles and divided the women into 4 groups by week of the menstrual cycle: 32 in Week One (Days 1 through 4 of the menstrual cycle); 46 in Week 2 (Days 5 through 12); 49 in Week 3 (Days 13 through 20); and 38 in Week 4 (Days 21 through 30). We qualitatively evaluated BPE of the whole breast in the early and delayed phases of MR imaging; categorized enhancement as minimal, mild, moderate, or marked; and calculated the rate at which signal intensity increased (=SI post-SI pre/SI pre) in regions of interest in from the early and delayed phase to the before contrast administration phase to assess BPE quantitatively. In both the early and delayed dynamic MR phases, BPE was significantly more extensive and stronger in Week 4 than Week 2 (P<0.01). Throughout the menstrual cycle, BPE was significantly stronger in the delayed phase than in the early phase in both qualitative (Week One, P=0.0002; Weeks 2 through 4, P<0.0001) and quantitative (Weeks One through 4, P<0.0001) assessments. The optimal time to perform dynamic breast MR imaging in premenopausal Japanese women was during Days 5 through 12 of the menstrual cycle.

Characteristics of axillary lymph nodes apparent on dynamic contrast-enhanced breast MRI in healthy women

The study was initiated to characterize and better understand the natural characteristics of axillary lymph nodes (LNs) apparent on dynamic breast magnetic resonance imaging (MRI). The most important finding in 71 subjects that included healthy women was that 41% showed strong enhanced axillary LNs. The dynamic curves of these LNs revealed an initial mean signal increase of 197% (±58%), all of them with a following plateau (34%) or washout (66%). Our study points out that the previous understanding of contrast enhancement in breast lesions should be taken with care when assessing axillary LNs. This has to be considered especially in preoperative breast MRI.

Myoid hamartoma of the breast that proved difficult to diagnose: a case report

Myoid hamartomas of the breast are extremely rare breast lesions, with a poorly understood pathogenesis. We describe the case of a 38-year-old premenopausal woman who presenting with a mass in the left breast. Mammography revealed an oval mass that was partly indistinct, and ultrasonography showed a hypoechoic mass with a slightly irregular margin. Bilateral breast dynamic magnetic resonance imaging was performed for a more detailed evaluation. The images showed rapid initial enhancement and a microlobulated margin. Because the suspicion of malignancy was strong at that time, core needle biopsy was performed. Histologically, the tumor was identified as fibroadenoma. A case of myoid hamartoma of the breast that proved difficult to diagnose is reported, and discussed with reference to the literature.

Washout of Mass-Like Benign Breast Lesions at Dynamic Magnetic Resonance Imaging

This study aimed to estimate the frequency and timing of washout in a series of pathologically proven benign mass-like breast lesions at dynamic magnetic resonance imaging. Institutional review board approval was obtained for this retrospective study. We evaluated enhancement kinetics of 33 pathologically confirmed benign breast lesions: fibroadenomas (n = 22), adenosis (n = 6), typical ductal hyperplasia (n = 2), fibroadenoma with ductal hyperplasia (n = 1), fibrosclerosis (n = 1), and inflammatory lesion (n = 1). Coronal 3-dimensional T1-weighted gradient-echo sequences were acquired before/after intravenous injection of 0.1 mmol/ kg gadoterate meglumine (time resolution, 111 seconds), 1 before and 5 after contrast injection. The time point at which the kinetic curve demonstrated a washout was recorded. Cumulative distribution of lesions showing washout was built. Paired comparisons of specificity for washout kinetics were performed using the McNemar test. Of 33 lesions, washout was never observed in 20 (61%), whereas 13 (39%) showed washout during the study. Of these 13 lesions, only 1 (inflammatory mass) exhibited washout within the first 3 minutes (specificity, 97%), 9 within 6 minutes (specificity, 73%), and 13 within 8 minutes (specificity, 61%). Specificity of washout kinetics within 3 minutes (97%) was significantly larger than that from the sixth minute (73%) and thereafter (P < 0.016). A prolonged observation for dynamic breast magnetic resonance imaging may result in false-positive washout, especially after 6 minutes. Late washout should not be considered a reliable marker of malignancy.

Dynamic Breast Magnetic Resonance Imaging: Pretreatment Prediction of Tumor Response to Neoadjuvant Chemotherapy

BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may have the potential of predicting response to neoadjuvant chemotherapy for patients with breast cancer. However, most of these studies focused on evaluating hot-spot characteristics. To thoroughly reflect tumor status, the cold spot and heterogeneity characteristics should also be evaluated. Patients and MethodsDCE-MRIs from 60 patients newly diagnosed with primary invasive breast cancer were reviewed. Kinetic parameters (including cold spot, hot spot, and heterogeneity parameters) derived from DCE-MRI data were used to describe cold spot, hot spot, and heterogeneity features. Patients with a pathologic complete response (pCR) or a ductal carcinoma in situ with microinvasion after chemotherapy were categorized into the pCR group. Pretreatment kinetic parameters in the pCR and non-pCR groups were compared by using univariate tests. Binary logistic regression analysis was used to identify the independent predictors for pCR. The best cutoff value of the independent predictor at pretreatment, with which to differentiate between patients who had a pCR and a non-pCR, was calculated by using receiver operating characteristic curve analysis. ResultsAfter chemotherapy, 10 (16.7%) patients were categorized into the pCR group and 50 (83.3%) into non-pCR group. Multivariate analysis showed that pretreatment washout slope at a cold spot (washoutC) was the only significant and independent predictor of pCR (β = 26.128; P = .005). The best pretreatment washoutC cutoff value with which to differentiate between patients who had pCR and those with non-pCR was 0.0277, which yielded a sensitivity of 80.0% (95% CI, 44.4%-97.5%) and a specificity of 74.0% (95% CI, 59.7%-85.4%). ConclusionWashoutC may be used as a predictor for pCR in patients with breast cancer who undergo neoadjuvant chemotherapy.

Background enhancement of mammary glandular tissue on breast dynamic MRI: imaging features and effect on assessment of breast cancer extent

Just as mammographic breast density influences mammographic sensitivity, the degree of background enhancement in breast magnetic resonance imaging (MRI) may influence the sensitivity of breast MRI. The purpose of this study is to assess the influence of background enhancement on the accuracy of breast cancer extent assessment using MRI and to assess the correlation between the accuracy of breast cancer extent assessment and the kinetic analysis of background enhancement in dynamic contrast-enhanced MRI. Seventy bilateral breast MRI examinations were evaluated to assess the extent of a known primary tumor. Background enhancement was classified into four categories by visual assessment: minimal, mild, moderate, and marked, in the early dynamic phase and in the late dynamic phase. The correlation of the results with histological findings was examined. Background enhancement grade showed a significant tendency to increase during dynamic contrast-enhanced MRI. When classifying background enhancement at early dynamic phase, the accuracy of tumor extent assessment by MRI with moderate/marked background enhancement was 60%, which was lower than the 78% accuracy with minimal/mild background enhancement, but not significantly so (p=0.153). When classifying background enhancement at late dynamic phase, the accuracy with moderate/marked background enhancement was 61%, which was significantly lower than the 83% accuracy with minimal/mild background enhancement (p=0.034). There was no tumor-size-related bias between the groups (p=0.089). The degree of background enhancement on breast MRI affects the accuracy of breast cancer extent assessment, especially at late dynamic phase.

A comparison of imaging methods in the evaluation of breast pathologies

Evaluating the performance of magnetic resonance imaging (MRI) and comparison with mammography, ultrasound (US) and histopathology results in cases of women with suspicious breast lesions. Forty nine cases on which histopathology was performed included in the study. All cases were applied mammography and US, and then MRI. Biopsy or post-operational results have been generated. Among the 49 cases, 27 (55%) benign and 22 (45%) were malign lesions. Using mammography, of the 49 cases, 24 (49%) were deemed to be malign. True positive cases were 20 (41%). In ultrasound, 25 (51%) were malign, 24 (49%) were benign. True positive cases were 21 (43%). Lesions were detected in all of the 49 cases using MRI. All of the 22 (45%) malign cases were diagnosed as malign lesion. Sensitivities of mammography, US and MRI in detecting lesions were 83%, 95% and 100%, and specificities were 85%, 85% and 92% respectively. In MRI, all cases were applied dynamic contrast sequences, for the cases with lesions detected, time signal intensity (SI) curves were drawn. 23 cases were detected as Type 1 (47%), 2 cases were Type 2 (4%), 24 cases were Type 3 (49%) SI curve. According to SI curves sensitivity in detecting malignities was 95% and specificity was 81%. MRI has been found superior to mammography and US in detecting masses especially with its characteristics of higher spatial resolution, less binding of dynamic properties evaluation on user. There may be a decrease of unnecessary interventional operations in benignmalign detection of breast lesions using dynamic breast MRI.