The adjacent vessel sign on breast MRI: new data and a subgroup analysis for 1,084 histologically verified cases

Association of peritumoral region features assessed on breast MRI and prognosis of breast cancer: a systematic review and meta-analysis

BACKGROUND

Increasing attention has been given to the peritumoral region. However, conflicting findings have been reported regarding the relationship between peritumoral region features on MRI and the prognosis of breast cancer.

PURPOSE

To evaluate the relationship between peritumoral region features on MRI and prognosis of breast cancer.

MATERIALS AND METHODS

A retrospective meta-analysis of observational studies comparing either qualitative or quantitative assessments of peritumoral MRI features on breast cancer with poor prognosis and control subjects was performed for studies published till October 2022. Pooled odds ratios (ORs) or standardized mean differences and 95% confidence intervals (CIs) were estimated by using random-effects models. The heterogeneity across the studies was measured using the statistic I2. Sensitivity analyses were conducted to test this association according to different study characteristics.

RESULTS

Twenty-four studies comprising 1853 breast cancers of poor prognosis and 2590 control participants were included in the analysis. Peritumoral edema was associated with non-luminal breast cancers (OR=3.56; 95%CI: 2.17, 5.83; p=.000), high expression of the Ki-67 index (OR=3.70; 95%CI: 2.41, 5.70; p =.000), high histological grade (OR=5.85; 95%CI: 3.89, 8.80; p=.000), lymph node metastasis (OR=2.83; 95%CI: 1.71, 4.67; p=.000), negative expression of HR (OR=3.15; 95%CI: 2.03, 4.88; p=.000), and lymphovascular invasion (OR=1.72; 95%CI: 1.28, 2.30; p=.000). The adjacent vessel sign was associated with greater odds of breast cancer with poor prognosis (OR=2.02; 95%CI: 1.68, 2.44; p=.000). Additionally, breast cancers with poor prognosis had higher peritumor-tumor ADC ratio (SMD=0.67; 95%CI: 0.54, 0.79; p=.000) and peritumoral ADCmean (SMD=0.29; 95%CI: 0.15, 0.42; p=.000). A peritumoral region of 2-20 mm away from the margin of the tumor is recommended.

CONCLUSION

The presence of peritumoral edema and adjacent vessel signs, higher peritumor-tumor ADC ratio, and peritumoral ADCmean were significantly correlated with poor prognosis of breast cancer.

CLINICAL RELEVANCE STATEMENT

MRI features of the peritumoral region can be used as a non-invasive index for the prognostic evaluation of invasive breast cancer.

KEY POINTS

• Peritumoral edema was positively associated with non-luminal breast cancer, high expression of the Ki-67 index, high histological grade, lymph node metastasis, negative expression of HR, and lymphovascular invasion. • The adjacent vessel sign was associated with greater odds of breast cancers with poor prognosis. • Breast cancers with poor prognosis had higher peritumor-tumor ADC ratio and peritumoral ADCmean.

Assessing Axillary Lymph Node Burden and Prognosis in cT1-T2 Stage Breast Cancer Using Machine Learning Methods: A Retrospective Dual-Institutional MRI Study

BACKGROUND

Pathological axillary lymph node (pALN) burden is an important factor for treatment decision-making in clinical T1-T2 (cT1-T2) stage breast cancer. Preoperative assessment of the pALN burden and prognosis aids in the individualized selection of therapeutic approaches.

PURPOSE

To develop and validate a machine learning (ML) model based on clinicopathological and MRI characteristics for assessing pALN burden and survival in patients with cT1-T2 stage breast cancer.

STUDY TYPE

Retrospective.

POPULATION

A total of 506 females (range: 24-83 years) with cT1-T2 stage breast cancer from two institutions, forming the training (N = 340), internal validation (N = 85), and external validation cohorts (N = 81), respectively.

FIELD STRENGTH/SEQUENCE

This study used 1.5-T, axial fat-suppressed T2-weighted turbo spin-echo sequence and axial three-dimensional dynamic contrast-enhanced fat-suppressed T1-weighted gradient echo sequence.

ASSESSMENT

Four ML methods (eXtreme Gradient Boosting [XGBoost], Support Vector Machine, k-Nearest Neighbor, Classification and Regression Tree) were employed to develop models based on clinicopathological and MRI characteristics. The performance of these models was evaluated by their discriminative ability. The best-performing model was further analyzed to establish interpretability and used to calculate the pALN score. The relationships between the pALN score and disease-free survival (DFS) were examined.

STATISTICAL TESTS

Chi-squared test, Fisher's exact test, univariable logistic regression, area under the curve (AUC), Delong test, net reclassification improvement, integrated discrimination improvement, Hosmer-Lemeshow test, log-rank, Cox regression analyses, and intraclass correlation coefficient were performed. A P-value <0.05 was considered statistically significant.

RESULTS

The XGB II model, developed based on the XGBoost algorithm, outperformed the other models with AUCs of 0.805, 0.803, and 0.818 in the three cohorts. The Shapley additive explanation plot indicated that the top variable in the XGB II model was the Node Reporting and Data System score. In multivariable Cox regression analysis, the pALN score was significantly associated with DFS (hazard ratio: 4.013, 95% confidence interval: 1.059-15.207).

DATA CONCLUSION

The XGB II model may allow to evaluate pALN burden and could provide prognostic information in cT1-T2 stage breast cancer patients.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom

Purpose

Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Approach

A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to 300    μ m / 60    s ) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to 300    μ m / 1 to 10 s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity.

Results

The observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization.

Conclusion

An in silico validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.

Comparison of the diagnostic value of contrast-enhanced ultrasound combined with conventional ultrasound versus magnetic resonance imaging in malignant non-mass breast lesions

OBJECTIVE

To compare the diagnostic value of contrast-enhanced ultrasound (CEUS)+conventional ultrasound vs MRI for malignant non-mass breast lesions (NMLs).

METHODS

A total of 109 NMLs detected by conventional ultrasound and examined by both CEUS and MRI were retrospectively analysed. The characteristics of NMLs in CEUS and MRI were noted, and agreement between the two modalities was analysed. Sensitivity, specificity, positive-predictive value (PPV), negative-predictive value (NPV), and area under the curve (AUC) of the two methods for diagnosing malignant NMLs were calculated in the overall sample and subgroups of different sizes(<10 mm, 10-20 mm, >20 mm).

RESULTS

A total of 66 NMLs detected by conventional ultrasound showed non-mass enhancement in MRI. Agreement between ultrasound and MRI was 60.6%. Probability of malignancy was higher when there was agreement between the two modalities. In the overall group, the sensitivity, specificity, PPV, and NPV of the two methods were 91.3%, 71.4%, 60%, 93.4% and 100%, 50.4%, 59.7%, 100%, respectively. The diagnostic performance of CEUS+conventional ultrasound was better than that of MRI (AUC: 0.825 vs 0.762, p = 0.043). The specificity of both methods decreased as lesion size increased, but sensitivity did not change. There was no significant difference between the AUCs of the two methods in the size subgroups (p > 0.05).

CONCLUSION

The diagnostic performance of CEUS+conventional ultrasound may be better than that of MRI for NMLs detected by conventional ultrasound. However, the specificity of both methods decrease significantly as lesion size increases.

ADVANCES IN KNOWLEDGE

This is the first study to compare the diagnostic performance of CEUS+conventional ultrasound vs that of MRI for malignant NMLs detected by conventional ultrasound. While CEUS+conventional ultrasound appears to be superior to MRI, subgroup analysis suggests that diagnostic performance is poorer for larger NMLs.

Diagnostic value of preoperative examination for evaluating margin status in breast cancer

BACKGROUND

A positive resection margin is a major risk factor for local breast cancer recurrence after breast-conserving surgery (BCS). Preoperative imaging examinations are frequently employed to assess the surgical margin.

AIM

To investigate the role and value of preoperative imaging examinations [magnetic resonance imaging (MRI), molybdenum target, and ultrasound] in evaluating margins for BCS.

METHODS

A retrospective study was conducted on 323 breast cancer patients who met the criteria for BCS and consented to the procedure from January 2014 to July 2021. The study gathered preoperative imaging data (MRI, ultrasound, and molybdenum target examination) and intraoperative and postoperative pathological information. Based on their BCS outcomes, patients were categorized into positive and negative margin groups. Subsequently, the patients were randomly split into a training set (226 patients, approximately 70%) and a validation set (97 patients, approximately 30%). The imaging and pathological information was analyzed and summarized using R software. Non-conditional logistic regression and LASSO regression were conducted in the validation set to identify factors that might influence the failure of BCS. A column chart was generated and applied to the validation set to examine the relationship between pathological margin range and prognosis. This study aims to identify the risk factors associated with failure in BCS.

RESULTS

The multivariate non-conditional logistic regression analysis demonstrated that various factors raise the risk of positive margins following BCS. These factors comprise non-mass enhancement (NME) on dynamic contrast-enhanced MRI, multiple focal vascular signs around the lesion on MRI, tumor size exceeding 2 cm, type III time-signal intensity curve, indistinct margins on molybdenum target examination, unclear margins on ultrasound examination, and estrogen receptor (ER) positivity in immunohistochemistry. LASSO regression was additionally employed in this study to identify four predictive factors for the model: ER, molybdenum target tumor type (MT Xmd Shape), maximum intensity projection imaging feature, and lesion type on MRI. The model constructed with these predictive factors exhibited strong consistency with the real-world scenario in both the training set and validation set. Particularly, the outcomes of the column chart model accurately predicted the likelihood of positive margins in BCS.

CONCLUSION

The proposed column chart model effectively predicts the success of BCS for breast cancer. The model utilizes preoperative ultrasound, molybdenum target, MRI, and core needle biopsy pathology evaluation results, all of which align with the real-world scenario. Hence, our model can offer dependable guidance for clinical decision-making concerning BCS.

Diagnostic value of preoperative examination for evaluating margin status in breast cancer

BACKGROUND

A positive resection margin is a major risk factor for local breast cancer recurrence after breast-conserving surgery (BCS). Preoperative imaging examinations are frequently employed to assess the surgical margin.

AIM

To investigate the role and value of preoperative imaging examinations [magnetic resonance imaging (MRI), molybdenum target, and ultrasound] in evaluating margins for BCS.

METHODS

A retrospective study was conducted on 323 breast cancer patients who met the criteria for BCS and consented to the procedure from January 2014 to July 2021. The study gathered preoperative imaging data (MRI, ultrasound, and molybdenum target examination) and intraoperative and postoperative pathological information. Based on their BCS outcomes, patients were categorized into positive and negative margin groups. Subsequently, the patients were randomly split into a training set (226 patients, approximately 70%) and a validation set (97 patients, approximately 30%). The imaging and pathological information was analyzed and summarized using R software. Non-conditional logistic regression and LASSO regression were conducted in the validation set to identify factors that might influence the failure of BCS. A column chart was generated and applied to the validation set to examine the relationship between pathological margin range and prognosis. This study aims to identify the risk factors associated with failure in BCS.

RESULTS

The multivariate non-conditional logistic regression analysis demonstrated that various factors raise the risk of positive margins following BCS. These factors comprise non-mass enhancement (NME) on dynamic contrast-enhanced MRI, multiple focal vascular signs around the lesion on MRI, tumor size exceeding 2 cm, type III time-signal intensity curve, indistinct margins on molybdenum target examination, unclear margins on ultrasound examination, and estrogen receptor (ER) positivity in immunohistochemistry. LASSO regression was additionally employed in this study to identify four predictive factors for the model: ER, molybdenum target tumor type (MT Xmd Shape), maximum intensity projection imaging feature, and lesion type on MRI. The model constructed with these predictive factors exhibited strong consistency with the real-world scenario in both the training set and validation set. Particularly, the outcomes of the column chart model accurately predicted the likelihood of positive margins in BCS.

CONCLUSION

The proposed column chart model effectively predicts the success of BCS for breast cancer. The model utilizes preoperative ultrasound, molybdenum target, MRI, and core needle biopsy pathology evaluation results, all of which align with the real-world scenario. Hence, our model can offer dependable guidance for clinical decision-making concerning BCS.

Diagnostic value of Kaiser score combined with breast vascular assessment from breast MRI for the characterization of breast lesions

Objectives

The Kaiser scoring system for breast magnetic resonance imaging is a clinical decision-making tool for diagnosing breast lesions. However, the Kaiser score (KS) did not include the evaluation of breast vascularity. Therefore, this study aimed to use KS combined with breast vascular assessment, defined as KS^*, and investigate the effectiveness of KS^* in differentiating benign from malignant breast lesions.

Methods

This retrospective study included 223 patients with suspicious breast lesions and pathologically verified results. The histopathological diagnostic criteria were according to the fifth edition of the WHO classification of breast tumors. The KS^* was obtained after a joint evaluation combining the original KS and breast vasculature assessment. The receiver operating characteristic (ROC) curve was used for comparing differences in the diagnostic performance between KS^* and KS, and the area under the receiver operating characteristic (AUC) was compared.

Results

There were 119 (53.4%) benign and 104 (46.6%) malignant lesions in total. The overall sensitivity, specificity, and accuracy of increased ipsilateral breast vascularity were 69.2%, 76.5%, and 73.1%, respectively. The overall sensitivity, specificity, and accuracy of AVS were 82.7%, 76.5%, and 79.4%, respectively. For all lesions included the AUC of KS^* was greater than that of KS (0.877 vs. 0.858, P = 0.016). The largest difference in AUC was observed in the non-mass subgroup (0.793 vs. 0.725, P = 0.029).

Conclusion

Ipsilaterally increased breast vascularity and a positive AVS sign were significantly associated with malignancy. KS combined with breast vascular assessment can effectively improve the diagnostic ability of KS for breast lesions, especially for non-mass lesions.

Peritumoral imaging features of thymic epithelial tumors for the prediction of transcapsular invasion: beyond intratumoral analysis

PURPOSE

The purpose of this study was to differentiate cases without transcapsular invasion (Masaoka-Koga stage I) from cases with transcapsular invasion (Masaoka-Koga stage II or higher) in patients with thymic epithelial tumors (TETs) using tumoral and peritumoral computed tomography (CT) features.

METHODS

This retrospective study included 116 patients with pathological diagnoses of TETs. Two radiologists evaluated clinical variables and CT features, including size, shape, capsule integrity, presence of calcification, internal necrosis, heterogeneous enhancement, pleural effusion, pericardial effusion, and vascularity grade. Vascularity grade was defined as the extent of peritumoral vascular structures in the anterior mediastinum. The factors associated with transcapsular invasion were analyzed using multivariable logistic regression. In addition, the interobserver agreement for CT features was assessed using Cohen's or weighted kappa coefficients. The difference between the transcapsular invasion group and that without transcapsular invasion was evaluated statistically using the Student's t-test, Mann-Whitney U test, chi-square test, and Fisher's exact test.

RESULTS

Based on pathology reports, 37 TET cases without and 79 with transcapsular invasion were identified. Lobular or irregular shape [odds ratio (OR): 4.19; 95% confidence interval (CI): 1.53-12.09; P = 0.006], partial complete capsule integrity (OR: 5.03; 95% CI: 1.85-15.13; P = 0.002), and vascularity grade 2 (OR: 10.09; 95% CI: 2.59-45.48; P = 0.001) were significantly associated with transcapsular invasion. The interobserver agreement for shape classification, capsule integrity, and vascularity grade was 0.840, 0.526, and 0.752, respectively (P < 0.001 for all).

CONCLUSION

Shape, capsule integrity, and vascularity grade were independently associated with transcapsular invasion of TETs. Furthermore, three CT TET features demonstrated good reproducibility and help differentiate between TET cases with and without transcapsular invasion.

The potential of predictive and prognostic breast MRI (P2-bMRI)

Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.

Subclassification of BI-RADS 4 Magnetic Resonance Lesions: A Systematic Review and Meta-Analysis

OBJECTIVE

This research aims to investigate and evaluate the diagnostic efficacy of magnetic resonance imaging (MRI) in classifying Breast Imaging Reporting and Data System (BI-RADS) 4 lesions into subcategories: 4a, 4b, and 4c, so as to limit biopsies of suspected lesions in the breast.

METHODS

The PubMed, Web of Science, Embase, and Cochrane Library foreign language databases were searched for literature published between January 2000 and July 2018. After analyzing the selection, data extraction, and quality assessment, a meta-analysis was performed, including data pooling, heterogeneity testing, and meta-regression.

RESULTS

Fourteen articles, including 18 studies, met the inclusion criteria. The diagnostic efficacy of MRI for BI-RADS 4-weighted summary assay sensitivity and specificity were estimated at 0.95 [95% confidence interval (CI), 0.89-0.98] and 0.87 (95% CI, 0.81-0.91), respectively. The positive and negative likelihood ratios were 7.1 (95% CI, 4.7-10.7) and 0.06 (95% CI, 0.02-0.14), respectively. The diagnostic odds ratio was 126 (95% CI, 37-426), and the area under the receiver operating characteristic curve was 0.95 (95% CI, 0.93-0.97). The malignancy ratio of BI-RADS 4a, 4b, and 4c and malignancy range were 2.5% to 18.3%, 23.5% to 57.1%, and 58.0% to 95.2%, respectively.

CONCLUSION

Risk stratification of suspected lesions (BI-RADS categories 4a, 4b, and 4c) can be achieved by MRI. The MRI is an effective auxiliary tool to further subclassify BI-RADS 4 lesions and prevent unnecessary biopsy of BI-RADS 4a lesions.

Differential diagnosis of plasma cell mastitis and invasive ductal carcinoma using multiparametric MRI

Background

Evaluate the potential of multiparametric magnetic resonance imaging (MRI) for the differential diagnosis of plasma cell mastitis (PCM) and invasive ductal carcinoma (IDC).

Methods

A total of 465 female patients, including 197 PCM (42.4%) and 268 IDC (57.6%), were examined using breast MRI scanning using routine sequences, dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI) and MR spectroscopy (MRS). The MRI features of PCM and IDC lesions were analyzed and compared to the histological results.

Results

Compared to IDC, the PCM lesions were more frequent in the subareolar regions, hyperintense on T2WI (P<0.01) and showed an initial signal increase ≤90%, a persistent and plateau pattern of time-intensity curves, non-mass enhancement, multiple rim enhancements, central hyperintensity on DWI, a higher ADC value, and total choline (tCho) peak negative and tCho peak integral <29.95 AU (P<0.01). The following breast-associated findings were also observed frequently in PCM: Ipsilateral breast enlargement, nipple retraction, skin thickening, peripheral edema and axillary lymphadenopathy. However, no significant difference was observed between the two groups for the shape, border and adjacent vessel signs of the lesion.

Conclusions

Some of the MRI features of PCM and IDC lesions were different. An integrated analysis of these multiparametric MRI features can thus assist in the differential diagnosis of PCM and IDC lesions.

Dynamic contrast-enhanced and diffusion-weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status

BACKGROUND

Although sentinel lymph node biopsy (SLNB) is the current standard for identifying lymph metastasis in breast cancer patients, there are complications of SLNB.

PURPOSE

To evaluate preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) of invasive breast cancer for predicting sentinel lymph node metastasis.

STUDY TYPE

Retrospective.

POPULATION

In all, 309 patients who underwent clinically node-negative invasive breast cancer surgery FIELD STRENGTH/SEQUENCE: 3.0T, DCE-MRI, DWI.

ASSESSMENT

We collected clinicopathologic variables (age, histologic and nuclear grade, extensive intraductal carcinoma component, lymphovascular invasion, and immunohistochemical profiles) and preoperative MRI features (tumor size, background parenchymal enhancement, internal enhancement, adjacent vessel sign, whole-breast vascularity, initial enhancement pattern, kinetic curve types, quantitative kinetic parameters, tumoral apparent diffusion coefficient [ADC], peritumoral maximal ADC, and peritumoral-tumoral ADC ratio).

STATISTICAL TESTS

Multivariate logistic regressions were performed to determine independent variables associated with SLN metastasis, and the area under the receiver operating characteristic curve (AUC) was analyzed for those variables.

RESULTS

41 (13.3%) of the patients showed SLN metastasis. With MRI, tumor size (odds ratio [OR], 1.11; 95% confidence interval [CI], 1.06-1.17), heterogeneous (OR, 5.33; 95% CI, 1.71-16.58), and rim (OR, 15.54; 95% CI, 2.12-113.72) enhancement and peritumoral-tumoral ADC ratio (OR, 72.79; 95% CI, 7.15-740.82) were independently associated with SLN metastasis. Clinicopathologic variables independently associated with SLN metastasis included age (OR, 0.96; 95% CI, 0.92-0.99) and CD31 (OR, 2.90; 95% CI, 1.04-8.92). The area under the curve (AUC) of MRI features (0.80; 95% CI, 0.73-0.87) was significantly higher than for clinicopathologic variables (0.68; 95% CI, 0.60-0.77; P = 0.048) and was barely below statistical significance for combined MRI features with clinicopathologic variables (0.84; 95% CI 0.78-0.90, P = 0.057).

DATA CONCLUSION

Preoperative internal enhancement on DCE-MRI and peritumoral-tumoral ADC ratio on DWI might be useful for predicting SLN metastasis in patients with invasive breast cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:615-626.

Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors

PURPOSE

We propose a novel methodology to integrate morphological and functional information of tumor-associated vessels to assist in the diagnosis of suspicious breast lesions.

THEORY AND METHODS

Ultrafast, fast, and high spatial resolution DCE-MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE-MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, K^trans (volume transfer coefficient), and v_p (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability.

RESULTS

A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, K^trans , and v_p showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91).

CONCLUSION

This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.

Radiological imaging characteristics of intramammary hematological malignancies: results from a german multicenter study

To assess radiological procedures and imaging characteristics in patients with intramammary hematological malignancies (IHM). Radiological imaging studies of histopathological proven IHM cases from ten German University affiliated breast imaging centers from 1997-2012 were retrospectively evaluated. Imaging modalities included ultrasound (US), mammography and magnetic resonance imaging (MRI). Two radiologists blinded to the histopathological diagnoses independently assessed all imaging studies. Imaging studies of 101 patients with 204 intramammary lesions were included. Most patients were women (95%) with a median age of 64 years. IHM were classified as Non Hodgkin lymphoma (77.2%), plasmacytoma (11.9%), leukemia (9.9%), and Hodgkin lymphoma (1%). The mean lesion size was 15.8 ± 10.1 mm. Most IHM presented in mammography as lesions with comparable density to the surrounding tissue, and a round or irregular shape with indistinct margins. On US, most lesions were of irregular shape with complex echo pattern and indistinct margins. MRI shows lesions with irregular or spiculated margins and miscellaneous enhancement patterns. Using US or MRI, IHM were more frequently classified as BI-RADS 4 or 5 than using mammography (96.2% and 89.3% versus 75.3%). IHM can present with miscellaneous radiological patterns. Sensitivity for detection of IHM lesions was higher in US and MRI than in mammography.

MRI features of breast lymphoma: preliminary experience in seven cases

PURPOSE

We aimed to evaluate the imaging features of breast lymphoma using magnetic resonance imaging (MRI).

METHODS

This retrospective study consisted of seven patients with pathologically confirmed breast lymphoma. The breast lymphomas were primary in six patients and secondary in one patient. All patients underwent preoperative dynamic contrast-enhanced MRI and one underwent additional diffusion-weighted imaging (DWI) with a b value of 600 s/mm2. Morphologic characteristics, enhancement features, and apparent diffusion coefficient (ADC) values were reviewed.

RESULTS

On MRI, three patients presented with a single mass, one with two masses, two with multiple masses, and one with a single mass and a contralateral focal enhancement. The MRI features of the eight biopsied masses in seven patients were analyzed. On MRI, the margins were irregular in six masses (75%) and spiculated in two (25%). Seven masses (87.5%) displayed homogeneous internal enhancement, while one (12.5%) showed rim enhancement. Seven masses (87.5%) showed a washout pattern and one (12.5%) showed a plateau pattern. The penetrating vessel sign was found in two masses (25%). One patient with two masses underwent DWI. Both masses showed hyperintense signal on DWI with ADC values of 0.867×10-3 mm2/s and 0.732×10-3 mm2/s, respectively.

CONCLUSION

Breast lymphoma commonly presents as a homogeneously enhancing mass with irregular margins and displays a washout curve pattern on dynamic MRI. A low ADC value may also indicate a possible diagnosis of breast lymphoma.

Dynamic contrast-enhanced breast magnetic resonance imaging for the prediction of early and late recurrences in breast cancer

The aim of the study was to evaluate dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) features for the prediction of early and late recurrences in patients with breast cancer.Of 1030 breast cancer patients who underwent surgery at our hospital from January 2007 to July 2011, 83 recurrent breast cancer patients were enrolled in this study. We compared MRI features (background parenchymal enhancement [BPE], internal enhancement, adjacent vessel sign, whole-breast vascularity, initial enhancement pattern, kinetic curve types, and quantitative kinetic parameters) and clinico-pathologic variables (age, stage, histologic grade, nuclear grade, existence of lymphovascular invasion and extensive intraductal carcinoma component, and immunohistochemical profiles) between patients with early (≤2.5 years after surgery) and late recurrence (>2.5 years after surgery). Cox proportional hazard regression analysis was performed to evaluate independent risk factors for early and late recurrence.On breast MRI, prominent ipsilateral whole-breast vascularity was independently associated with early recurrence (hazard ratio [HR], 2.86; 95% confidence intervals [CI], 1.39-5.88) and moderate or marked BPE (HR, 2.08; 95% CI, 1.04-4.18) and rim enhancement (HR, 2.14; 95% CI, 1.00-4.59) were independently associated with late recurrence. Clinico-pathologic variables independently associated with early recurrence included negative estrogen receptor (HR, 0.53; 95% CI, 0.29-0.96), whereas T2 stage (HR, 2.08; 95% CI, 1.04-4.16) and nuclear grade III (HR, 2.54; 95% CI, 1.29-4.98) were associated with late recurrence.In DCE-MRI, prominent ipsilateral whole-breast vascularity, moderate or marked BPE, and rim enhancement could be useful for predicting recurrence timing in patients with breast cancer.

Breast Contrast Enhanced MR Imaging: Semi-Automatic Detection of Vascular Map and Predominant Feeding Vessel

Purpose

To obtain breast vascular map and to assess correlation between predominant feeding vessel and tumor location with a semi-automatic method compared to conventional radiologic reading.

Methods

148 malignant and 75 benign breast lesions were included. All patients underwent bilateral MR imaging. Written informed consent was obtained from the patients before MRI. The local ethics committee granted approval for this study. Semi-automatic breast vascular map and predominant vessel detection was performed on MRI, for each patient. Semi-automatic detection (depending on grey levels threshold manually chosen by radiologist) was compared with results of two expert radiologists; inter-observer variability and reliability of semi-automatic approach were assessed.

Results

Anatomic analysis of breast lesions revealed that 20% of patients had masses in internal half, 50% in external half and the 30% in subareolar/central area. As regards the 44 tumors in internal half, based on radiologic consensus, 40 demonstrated a predominant feeding vessel (61% were supplied by internal thoracic vessels, 14% by lateral thoracic vessels, 16% by both thoracic vessels and 9% had no predominant feeding vessel—p<0.01), based on semi-automatic detection, 38 tumors demonstrated a predominant feeding vessel (66% were supplied by internal thoracic vessels, 11% by lateral thoracic vessels, 9% by both thoracic vessels and 14% had no predominant feeding vessel—p<0.01). As regards the 111 tumors in external half, based on radiologic consensus, 91 demonstrated a predominant feeding vessel (25% were supplied by internal thoracic vessels, 39% by lateral thoracic vessels, 18% by both thoracic vessels and 18% had no predominant feeding vessel—p<0.01), based on semi-automatic detection, 94 demonstrated a predominant feeding vessel (27% were supplied by internal thoracic vessels, 45% by lateral thoracic vessels, 4% by both thoracic vessels and 24% had no predominant feeding vessel—p<0.01). An excellent agreement between two radiologic assessments (k = 0.81) and between radiologic consensus and semi-automatic assessment (k = 0.80) was found to identify origin of predominant feeding vessel. An excellent reliability for semi-automatic assessment (Cronbach's alpha = 0.96) was reported.

Conclusions

Predominant feeding vessel location was correlated with breast lesion location: internal thoracic artery supplied the highest proportion of breasts with tumor in internal half and lateral thoracic artery supplied the highest proportion of breasts with lateral tumor.

Comparison of diameters of ipsilateral and contralateral internal mammary arteries by breast MRI in patients with unilateral breast cancer

PURPOSE

We compared maximal diameters of ipsilateral (IMA) and contralateral (IMA) internal mammary arteries in patients with unilateral breast cancer and analyze the implications of enlargements of ipsilateral or contralateral IMAs in relation to histopathologic factors.

MATERIALS AND METHODS

Of 568 women who underwent breast magnetic resonance imaging (MRI) examinations from January 2009 to May 2012, 196 had unilateral, histologically proven breast cancer. In 156 women, maximal IMA diameters in the second intercostal space were measured by two blinded radiologists in left and right sides using nonenhanced axial T2-weighted turbo spin-echo sequence images.

RESULTS

In the 156 study patients, mean maximal diameter of ipsilateral IMAs (2.37 ± 0.60 mm) was significantly larger than that of contralateral IMAs (2.03 ± 0.58 mm) (p = 0.00). Ipsilateral IMA enlargement was present in 66.7 % of the patients (104 of 156). Furthermore, ipsilateral IMA enlargement was found to be significantly associated with human epidermal growth factor receptor-2 (HER-2) expression (p = 0.039).

CONCLUSIONS

Maximal IMA diameter was significantly greater in ipsilateral sides in breast cancer patients. Findings suggest ipsilateral IMA enlargement detected by MRI might be a useful additional predictor of HER-2 expression in unilateral breast cancer.

4D co-registration of X-ray and MR-mammograms: initial clinical results and potential incremental diagnostic value

PURPOSE

4D co-registration of X-ray- and MR-mammograms (XM and MM) is a new method of image fusion. The present study aims to evaluate its clinical feasibility, radiological accuracy, and potential clinical value.

METHODS

XM and MM of 25 patients were co-registered. Results were evaluated by a blinded reader.

RESULTS

Precision of the 4D co-registration was "very good" (mean-score [ms]=7), and lesions were "easier to delineate" (ms=5). In 88.8%, "relevant additional diagnostic information" was present, accounting for a more "confident diagnosis" in 76% (ms=5).

CONCLUSION

4D co-registration is feasible, accurate, and of potential clinical value.

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.

Effectiveness of additional diagnostic parameters in magnetic resonance mammography: a comparative study with the BI-RADS classification and scoring system

OBJECTIVE

This study aims to evaluate the strength of magnetic resonance (MR) lesion descriptors for malignancy and to determine the effectiveness of a scoring system that combines BI-RADS parameters with additional criteria.

MATERIALS AND METHODS

Three hundred thirty-nine histopathologically proven lesions that had undergone MR imaging were analyzed retrospectively. Based on the Fischer scoring system, an optimal cutoff value was calculated for combined parameters. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for all lesions with MR BI-RADS classification without using additional parameters. Finally, the results of the scoring system and MR BI-RADS classification were compared.

RESULTS

The optimal cutoff value according to the total score was calculated as 5. The sensitivity and the specificity of BI-RADS classification were calculated to be 94.20% and 56.12%, respectively. The scoring system using a cutoff value of 5 resulted in a little loss of sensitivity (89.86%) but resulted in a reasonable increase in the specificity (88.49%).

CONCLUSIONS

Additional parameters can improve the specificity of MR imaging. T2-weighted signal features, adjacent vessel sign, unilateral-focal edema, and hook sign were considered as effective parameters.

A rim-enhanced mass with central cystic changes on MR imaging: how to distinguish breast cancer from inflammatory breast diseases?

Objective

To evaluate the capacity of magnetic resonance imaging (MRI) to distinguish breast cancer from inflammatory breast diseases manifesting as a rim-enhanced mass with central cystic changes.

Materials and Methods

Forty cases of breast cancer and 52 of inflammatory breast diseases showing a rim-enhanced mass with central cystic changes were retrospectively reviewed. All cases underwent dynamic contrast-enhanced MRI and 31 of them underwent diffusion-weighted imaging (DWI). Morphological features, dynamic parameters and apparent diffusion coefficient (ADC) values were comparatively analyzed using univariate analysis and binary logistic regression analysis.

Results

Breast cancer had a significantly thicker wall than the inflammatory breast diseases (P<0.001) while internal enhancing septa were more common in inflammatory breast diseases (P = 0.003). On DWI, 86.7% of breast cancers demonstrate a peripheral hyperintensity whereas 93.8% of inflammatory breast diseases had a central hyperintensity (P<0.001). Compared to the inflammatory breast diseases, breast cancers had a lower ADC value for the wall (1.09×10⁻³ mm²/s vs 1.42×10⁻³ mm²/s, P<0.001) and a higher ADC value for the central part (1.94×10⁻³ mm²/s vs 1.05×10⁻³ mm²/s, P<0.001).

Conclusions

Both breast cancer and inflammatory breast diseases could present as a rim-enhanced mass with central cystic changes on MRI. Integrated analysis of the MR findings can allow for an accurate differential diagnosis.

A fully automatic multiscale 3-dimensional Hessian-based algorithm for vessel detection in breast DCE-MRI

OBJECTIVES

The objectives of this study were to develop a fully automatic method for detecting blood vessels in dynamic contrast-enhanced magnetic resonance imaging of the breast on the basis of a multiscale 3-dimensional Hessian-based algorithm and to evaluate the improvement in reducing the number of vessel voxels incorrectly classified as parenchymal lesions by a computer-aided diagnosis (CAD) system.

MATERIALS AND METHODS

The algorithm has been conceived to work on images obtained with different sequences, different acquisition parameters, such as the use of fat-saturation, and different contrast agents. The analysis was performed on 28 dynamic contrast-enhanced magnetic resonance imaging examinations, with 39 malignant (28 principal and 11 satellite) and 8 benign lesions, acquired at 2 centers using 2 different 1.5-T magnetic resonance scanners, radiofrequency coils, and contrast agents (14 studies from group A and 14 studies from group B). The method consists of 2 main steps: (a) the detection of linear structures on 3-dimensional images, with a multiscale analysis based on the second-order image derivatives and (b) the exclusion of non-vessel enhancements based on their morphological properties through the evaluation of the covariance matrix eigenvalues. To evaluate the algorithm performances, the identified vessels were converted into a 2-dimensional vasculature skeleton and then compared with manual tracking performed by an expert radiologist. When assessing the outcome of the algorithm performances in identifying vascular structures, the following terms must be considered: the correct-detection rate refers to pixels identified by both the algorithm and the radiologist, the missed-detection rate refers to pixels detected only by the radiologist, and the incorrect-detection rate refers to pixels detected only by the algorithm. The Wilcoxon rank sum test was used to assess differences between the performances of the 2 subgroups of images obtained from the different scanners.

RESULTS

For the testing set, which is composed of 28 patients from 2 different clinical centers, the median correct-detection rate was 89.1%, the median missed-detection rate was 10.9%, and the median incorrect-detection rate was 27.1%. The difference between group A and group B was not significant (P > 0.25). The exclusion of vascular voxels from the lesion detection map of a CAD system leads to a reduction of 68.4% (30.0%) (mean [SD]) of the total number of false-positives because of vessels, without a significant difference between the 2 subgroups (P = 0.50).

CONCLUSIONS

The system showed promising results in detecting most vessels identified by an expert radiologist on both fat-saturated and non-fat-saturated images obtained from different scanners with variable temporal and spatial resolutions and types of contrast agent. Moreover, the algorithm may reduce the labeling of vascular voxels as parenchymal lesions by a CAD system for breast magnetic resonance imaging, improving the CAD specificity and, consequently, further stimulating the use of CAD systems in clinical workflow.

A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography

OBJECTIVES

In the face of multiple available diagnostic criteria in MR-mammography (MRM), a practical algorithm for lesion classification is needed. Such an algorithm should be as simple as possible and include only important independent lesion features to differentiate benign from malignant lesions. This investigation aimed to develop a simple classification tree for differential diagnosis in MRM.

METHODS

A total of 1,084 lesions in standardised MRM with subsequent histological verification (648 malignant, 436 benign) were investigated. Seventeen lesion criteria were assessed by 2 readers in consensus. Classification analysis was performed using the chi-squared automatic interaction detection (CHAID) method. Results include the probability for malignancy for every descriptor combination in the classification tree.

RESULTS

A classification tree incorporating 5 lesion descriptors with a depth of 3 ramifications (1, root sign; 2, delayed enhancement pattern; 3, border, internal enhancement and oedema) was calculated. Of all 1,084 lesions, 262 (40.4 %) and 106 (24.3 %) could be classified as malignant and benign with an accuracy above 95 %, respectively. Overall diagnostic accuracy was 88.4 %.

CONCLUSIONS

The classification algorithm reduced the number of categorical descriptors from 17 to 5 (29.4 %), resulting in a high classification accuracy. More than one third of all lesions could be classified with accuracy above 95 %.

KEY POINTS

• A practical algorithm has been developed to classify lesions found in MR-mammography. • A simple decision tree consisting of five criteria reaches high accuracy of 88.4 %. • Unique to this approach, each classification is associated with a diagnostic certainty. • Diagnostic certainty of greater than 95 % is achieved in 34 % of all cases.

Bilateral analysis of the cross-sectional area of the internal mammary arteries and veins in patients with and without breast cancer on breast magnetic resonance imaging

OBJECTIVE

To analyse bilateral differences in the cross-sectional area of the internal mammary artery (IMA) and vein (IMV) in breast cancer patients compared to healthy controls.

MATERIALS AND METHODS

On 135 breast MRIs the cross-sectional areas of the IMA and IMV were measured on the left and right side in the second and third intercostal space (ICS) by two independent readers. Differences were analysed using a linear mixed model.

RESULTS

In the healthy control group (n = 91) no significant differences between the cross-sectional areas of the IMA and IMV were observed. Both readers reported a mean adjusted difference of 0.12 mm2 (p = 0.298) and 0.21 mm2 (p = 0.058) for the IMA in the second ICS. In the malignancy group (n = 44) the cross-sectional area was significantly larger on the malignancy side compared to the contralateral side. The largest difference in the IMA was measured in the second ICS with a mean adjusted difference for reader 1 of 1.37 mm2 (p < 0.001) and for reader 2 of 0.81 mm2 (p = 0.003).

CONCLUSIONS

The vascular cross-sectional area of internal mammary vessels was significantly different on the side with breast cancer compared to the contralateral side. This difference was not observed in healthy controls.

MAIN MESSAGES

• MRI has become an important imaging modality in the diagnostic workup of breast cancer. • In healthy persons no significant difference in the size of the left and right IMA is observed. • A significant enlargement of the IMA on the malignant side occurs in most patients.

Prognostic role of MRI enhancement features in patients with breast cancer: value of adjacent vessel sign and increased ipsilateral whole-breast vascularity

OBJECTIVE

The purpose of this study was to compare adjacent vessel sign, increased ipsilateral whole-breast vascularity, and various MRI features as described in the American College of Radiology BI-RADS MRI lexicon with histopathologic predictors in patients with unilateral breast cancer.

MATERIALS AND METHODS

We retrospectively evaluated breast MRI examinations of 249 patients with histologically confirmed breast cancer. In addition to the BI-RADS MRI lexicon, the adjacent vessel sign and increased ipsilateral whole-breast vascularity of the cancer-bearing breast were evaluated by two independent observers. MRI features were then correlated with histopathologic prognostic factors.

RESULTS

The adjacent vessel sign was significantly (p=0.023 to p<0.001) associated with tumor size, lymph node metastasis, distant metastasis, nuclear grade, and expression of estrogen and progesterone receptors. Increased ipsilateral whole-breast vascularity was significantly associated with all histopathologic predictors (p=0.017 to p<0.001). In multivariate analysis, the significant and independent predictors were a spiculated margin and rim enhancement for negative estrogen and progesterone receptors, a kinetic curve type for higher histologic grade, and an increased ipsilateral whole-breast vascularity for larger tumor size, lymph node metastasis, distant metastasis, higher nuclear grade, and higher histologic grade.

CONCLUSION

In conjunction with the standard BI-RADS MRI lexicon, the adjacent vessel sign and increased ipsilateral whole-breast vascularity may serve as additional predictors of a poor prognosis.

The Great Mimicker: Zona Zoster at the Mastectomy Site Causing Contralateral Intramammary Lymph Node Enlargement

Zona zoster is rarely observed in patients with malignancy; when present, it follows a dermatomal fashion. Involvement of widely separated regions is very rare. Hereby, zona zoster causing enlarged intramammary lymph nodes (IMLN) in the opposite breast is reported for the first time in literature. The masses were hypoechoic on US with no hilum and hypervascular on color Doppler US. MRI showed hypointense masses with type 3 time-intensity curve and adjacent vessel sign. The complete regression of the nodes after the antiviral therapy confirmed the diagnosis. In breast cancer patients, IMLN enlargements may mimic breast cancer metastasis, and zona zoster infection of the mastectomy site may present with contralateral IMLN enlargement due to altered lymphatic drainage. When breast US is not sufficient for the differential diagnosis, breast MRI may warrant proper diagnosis, and prevent unnecessary biopsies. Antiviral treatment with followup would be sufficient for management.

The necrosis sign in magnetic resonance-mammography: diagnostic accuracy in 1,084 histologically verified breast lesions

Necrosis sign (NS) is a new descriptor for differential diagnosis of breast lesions in magnetic resonance (MR)-mammography (MRM). This study was designed: (a) to analyze diagnostic accuracy of NS in 1,084 histologically verified breast lesions, (b) to assess performance of NS in subgroups. This study was approved by the local ethical committee. All histologically verified lesions having undergone MR-mammography at our institution over 12 years were evaluated by experienced radiologists (> 500 MRM) according to standard protocols and study design (T1w; 0.1 mmol/kg bw gadolinium diethylenetriamine penta-acetic acid; T2-turbo spin echo (TSE)). Patients with history of breast biopsy (surgically, minimal-invasive), radiation- or chemotherapy ≤ 1 year before MRM were excluded. NS was assessed on T2w-TSE sequences and was rated positive if a hyperintense center in a hypointense lesion could be visualized (chi-squared test). One thousand and eighty-four lesions were available for statistical analysis (648: malignant, 436: benign). NS was significantly associated with malignancy (p < 0.001), providing specificity and positive predictive value (PPV) of 96.1% and 78.8%. Malignant lesions > 20 mm presented significantly more often NS (p < 0.001) than neoplasias ≤ 20 mm. There was no difference regarding prevalence of NS in small versus advanced benign lesions (n.s.), leading to better performance of NS in lesions > 20 mm (PPV: 87.8%). Correlation between NS and Grading of invasive carcinomas was significant. In this study of 1,084 lesions necrosis sign was a specific and highly predictive feature for differential diagnosis in MRM (Specificity: 96.1%; PPV: 78.8%). This particularly counts for advanced lesions (PPV 87.8%). As this new descriptor correlates with Grading, it could be used as an initial estimate of patient's prognosis.

Accuracy of magnetic resonance in suspicious breast lesions: a systematic quantitative review and meta-analysis

Dynamic contrast-enhanced breast magnetic resonance (MR) is a promising emerging technique for evaluating breast lesions. A quantitative systematic review was performed to estimate the accuracy of breast MR in the diagnosis of high-risk breast lesions and breast cancer. A comprehensive search of the Cochrane Library, MEDLINE, CANCERLIT, LILACS, and EMBASE databases was performed from January 1985 to August 2010. The medical subjects heading (MeSH) and text words for the terms "breast neoplasm", "breast lesions", "breast cancer" and "magnetic resonance" were combined with the MeSH term diagnosis ("sensitivity and specificity"). Studies that compared breast MR with paraffin-embedded sections parameters for the diagnosis of breast lesions (benign, high-risk borderline, and breast cancer) were included. Sixty-nine studies were analyzed, which included 9,298 women with 9,884 breast lesions. Interrater overall agreement between breast MR and paraffin section diagnosis was 79% (κ = 0.55), indicating moderate agreement. Pooled sensitivity and specificity were 90% [95% CI 88-92%] and 75% [95% CI 70-79%], respectively. The pooled likelihood positive ratio was 3.64 (95% CI 3.0-4.2) and the negative ratio was 0.12 (95% CI 0.09-0.15). For breast cancer or high-risk lesions versus benign lesions, the AUC was 0.91 for breast MR and the point Q* was 0.84. In summary, breast MR is a useful pre-operative test for predicting the diagnosis of breast lesions.

Magnetic resonance mammography of invasive lobular versus ductal carcinoma: systematic comparison of 811 patients reveals high diagnostic accuracy irrespective of typing

OBJECTIVE

Invasive lobular (ILC) and ductal carcinomas (IDC) are the most frequent subtypes of breast cancer. Diagnosis of ILC is often challenging. This study was conducted to (1) evaluate dynamic and morphologic profiles and to (2) compare the diagnostic accuracy of IDC and ILC in magnetic resonance mammography (MRM).

METHODS

Our database consisted of all consecutive MRMs over a 12-year period (standardized protocol: T1-weighted fast low-angle shot; 0.1-mmol gadolinium-diethylenetriaminepentaacetate per kilogram of body weight; T2-weighted turbo spin-echo, 1.5 T; histological verification after MRM), which were evaluated by experienced (>500 MRMs) radiologists in consensus, applying 17 predefined descriptors. All the patients gave written consent; this study was approved by the local institutional review board. Extracting all the ILCs (n = 108), IDCs (n = 347), and benign lesions (n = 436) from the database, the data set of the study was created.In ILC and IDC diagnostic accuracy of single descriptors was calculated and compared separately (chi test). Using all the descriptors, a combined binary logistic regression analysis was applied to calculate the overall diagnostic accuracy for ILC and IDC. The corresponding areas under the curve were compared.

RESULTS

ILC and IDC, showed wash-in and an irregular shape without difference (P = 1.0 and P = 0.4). Wash-out was more typical of IDC (72.6%; ILC, 57.4%; P = 0.007). Perifocal edema was diagnosed more frequently in IDC (45.5%; P = 0.05). For overall accuracy, the areas under the curve were 0.929 for ILC and 0.939 for IDC (P = 0.5).

CONCLUSIONS

The dynamic and morphologic profiles of ILC and IDC were overlapping, and minor differences between both subgroups could be identified. Accordingly, the overall diagnostic accuracy of MRM was high and without difference between both subtypes of breast cancer.