Breast MRI Using the VIBE Sequence: Clustered Ring Enhancement in the Differential Diagnosis of Lesions Showing Non-Masslike Enhancement

Could breast multiparametric MRI discriminate between pure ductal carcinoma in situ and microinvasive carcinoma?

BACKGROUND

Ductal carcinoma in situ (DCIS) is often reclassified as invasive cancer in the final pathology report of the surgical specimen. It is of significant clinical relevance to acknowledge the possibility of underestimating invasive disease when utilizing preoperative biopsies for a DCIS diagnosis. In cases where such histologic upgrades occur, it is imperative to consider them in the preoperative planning process, including the potential inclusion of sentinel lymph node biopsy due to the risk of axillary lymph node metastasis.

PURPOSE

To assess the capability of breast multiparametric magnetic resonance imaging (MP-MRI) in differentiating between pure DCIS and microinvasive carcinoma (MIC).

MATERIAL AND METHODS

Between January 2018 and November 2022, this retrospective study enrolled patients with biopsy-proven DCIS who had undergone preoperative breast MP-MRI. We assessed various MP-MRI features, including size, morphology, margins, internal enhancement pattern, extent of disease, presence of peritumoral edema, time-intensity curve value, diffusion restriction, and ADC value. Subsequently, a logistic regression analysis was conducted to explore the association of these features with the pathological outcome.

RESULTS

Of 129 patients with biopsy-proven DCIS, 36 had foci of micro-infiltration on surgical specimens and eight were diagnosed with invasive ductal carcinoma (IDC). The presence of micro-infiltration foci was significantly associated with several MP-MRI features, including tumor size (P <0.001), clustered ring enhancement (P <0.001), segmental distribution (P <0.001), diffusion restriction (P = 0.005), and ADC values <1.3 × 10-3 mm2/s (P = 0.004).

CONCLUSION

Breast MP-MRI has the potential to predict the presence of micro-infiltration foci in biopsy-proven DCIS and may serve as a valuable tool for guiding therapeutic planning.

The Role of MRI in Breast Cancer and Breast Conservation Therapy

Contrast-enhanced breast MRI has an established role in aiding in the detection, evaluation, and management of breast cancer. This article discusses MRI sequences, the clinical utility of MRI, and how MRI has been evaluated for use in breast radiotherapy treatment planning. We highlight the contribution of MRI in the decision-making regarding selecting appropriate candidates for breast conservation therapy and review the emerging role of MRI-guided breast radiotherapy.

Prognostic Molecular Biomarkers in Breast Cancer Lesions with Non-Mass Enhancement on MR

Clustered ring enhancement (CRE) is a new lexicon for non-mass enhancement (NME) of breast MR in the 5th BIRADS, indicating a high suspicion of malignancy. We wonder if the presence of CRE correlates with expression of prognostic molecular biomarkers of breast cancer. A total of 58 breast lesions, which MRI reported with NME, were collected between July 2013 and December 2018. The patterns of enhancement including CRE were reviewed and the pathological results with expression of molecular biomarkers were collected. The association between MRI NME, pathological, and IHC stain findings were investigated under univariate analysis. A total of 58 breast lesions were pathologically proven to have breast cancer, comprising 31 lesions with CRE and 27 lesions without CRE on breast MRI. The expression of the estrogen receptor (ER) (p = 0.017) and the progesterone receptor (PR) (p = 0.017) was significantly lower in lesions with CRE as compared with those without CRE. The expression of Ki-67 (≥25%) was significantly higher in lesions with CRE (p = 0.046). The lesions with CRE had a lower expression ratio of ER (50.71 ± 45.39% vs. 74.26 ± 33.59%, p = 0.028). Our study indicated that lesions with CRE may possess different features from those without CRE in molecular expression, bearing a more aggressive behavior.

Evolving Treatment Paradigms for Low-Risk Ductal Carcinoma In Situ: Imaging Needs

Ductal carcinoma in situ (DCIS) is a nonobligate precursor to invasive cancer that classically presents as asymptomatic calcifications on screening mammography. The increase in DCIS diagnoses with organized screening programs has raised concerns about overdiagnosis, while a patientcentric push for more personalized care has increased awareness about DCIS overtreatment. The standard of care for most new DCIS diagnoses is surgical excision, but nonsurgical management via active monitoring is gaining attention, and multiple clinical trials are ongoing. Imaging, along with demographic and pathologic information, is a critical component of active monitoring efforts. Commonly used imaging modalities including mammography, ultrasound, and MRI, as well as newer modalities such as contrast-enhanced mammography and dedicated breast PET, can provide prognostic information to risk stratify patients for DCIS active monitoring eligibility. Furthermore, radiologists will be responsible for closely surveilling patients on active monitoring and identifying if invasive progression occurs. Active monitoring is a paradigm shift for DCIS care, but the success or failure will rely heavily on the interpretations and guidance of radiologists.

Contrast-Enhanced Mammography-Guided Biopsy: Preliminary Results of a Single-Center Retrospective Experience

Background: CEM-guided breast biopsy is an advanced diagnostic procedure that takes advantage of the ability of CEM to enhance suspicious breast lesions. The aim pf this paper is to describe a single-center retrospective experience on CEM-guided breast biopsy in terms of procedural features and histological outcomes. Methods: 69 patients underwent the procedure. Patient age, breast density, presentation, dimensions, and lesion target enhancement were recorded. All the biopsy procedures were performed using a 7- or 10-gauge (G) vacuum-assisted biopsy needle. The procedural approach (horizontal or vertical) and the decubitus of the patient (lateral or in a sitting position) were noted. Results: A total of 69 patients underwent a CEM-guided biopsy. Suspicious lesions presented as mass enhancement in 35% of cases and non-mass enhancement in 65% of cases. The median size of the target lesions was 20 mm. The median procedural time for each biopsy was 10 ± 4 min. The patients were placed in a lateral decubitus position in 52% of cases and seated in 48% of cases. The most common approach was horizontal (57%). The mean AGD was 14.8 mGy. At histology, cancer detection rate was 28% (20/71). Conclusions: CEM-guided biopsy was feasible, with high procedure success rates and high tolerance by the patients.

Combination of Breast Ultrasound With Magnetic Resonance Imaging in the Diagnosis of Non-mass-like Breast Lesions Detected on Ultrasound: A New Integrated Strategy to Improve Diagnostic Performance

OBJECTIVE

The aim of the work described here was to evaluate the diagnostic performance of a new integrated strategy using breast ultrasound (US) combined with magnetic resonance imaging (MRI) to differentiate benign and malignant breast non-mass-like lesions (NMLs) detected on US.

METHODS

From October 2017 to January 2021, 183 NMLs detected on US that had undergone MRI examinations were included in this respective study. Pathological results were used as the reference standard. The integrated diagnostic strategy of breast US combined with MRI based on a combination of MRI Breast Imaging Reporting and Data System (BI-RADS) with discriminant sonographic indicators highly associated with malignancy was established and validated in a cohort of 61 women. The diagnostic performances of US, MRI and the combined method were calculated and compared.

RESULTS

In the training set, the area under the receiver operating characteristic curve (AUC), sensitivity and specificity of US, MRI and the integrated diagnostic strategy using US combined with MRI for NMLs were 0.730, 93.7% and 52.3%; 0.849, 94.7% and 75.0%; and 0.901, 92.6% and 87.5%, respectively. Compared with US or MRI alone, the integrated diagnostic strategy significantly increased the AUC (p < 0.001, p = 0.007) and specificity (p < 0.001, p = 0.034) while maintaining high sensitivity (p = 0.774, p = 0.551). In the validation set, the integrated strategy of US combined with MRI (AUC = 0.899) also had good performance compared with US (AUC = 0.728) or MRI (AUC = 0.838).

CONCLUSION

The integrated diagnostic strategy of US combined with MRI exhibited good performance for breast NMLs compared with either modality used alone, which can improve the diagnostic specificity while maintaining high sensitivity.

Subcategorization of suspicious non-mass-like enhancement lesions(BI-RADS-MRI Category4)

BACKGROUND

This study aims to providing a reliable method that has good compliance and is easy to master to improve the accuracy of NMLE diagnosis.

METHODS

This study retrospectively analyzed 122 cases of breast non-mass-like enhancement (NMLE) lesions confirmed by postoperative histology. MRI features and clinical features of benign and malignant non-mass enhancement breast lesions were compared by using independent sample t test, χ2test and Fisher exact test. P < 0.05 was considered statistically significant. Statistically significant parameters were then included in logistic regression analysis to build a multiparameter differential diagnosis modelto subdivide the BI-RADS Category 4.

RESULTS

The distribution (odds ratio (OR) = 8.70), internal enhancement pattern (OR = 6.29), ADC value (OR = 5.56), and vascular sign (OR = 2.84) of the lesions were closely related to the benignity and malignancy of the lesions. These signs were used to build the MRI multiparameter model for differentiating benign and malignant non-mass enhancement breast lesions. ROC analysis revealed that its optimal diagnostic cut-off value was 5. The diagnostic specificity and sensitivity were 87.01% and 82.22%, respectively. Lesions with 1-6 points were considered BI-RADS category 4 lesions, and the positive predictive values of subtypes 4a, 4b, and 4c lesions were15.79%, 31.25%, and 77.78%, respectively.

CONCLUSIONS

Comprehensively analyzing the features of MRI of non-mass enhancement breast lesions and building the multiparameter differential diagnosis model could improve the differential diagnostic performance of benign and malignant lesions.

Use of a deep learning algorithm for non-mass enhancement on breast MRI: comparison with radiologists' interpretations at various levels

PURPOSE

To evaluate the diagnostic performance of deep learning using the Residual Networks 50 (ResNet50) neural network constructed from different segmentations for distinguishing malignant and benign non-mass enhancement (NME) on breast magnetic resonance imaging (MRI) and conduct a comparison with radiologists with various levels of experience.

MATERIALS AND METHODS

A total of 84 consecutive patients with 86 lesions (51 malignant, 35 benign) presenting NME on breast MRI were analyzed. Three radiologists with different levels of experience evaluated all examinations, based on the Breast Imaging-Reporting and Data System (BI-RADS) lexicon and categorization. For the deep learning method, one expert radiologist performed lesion annotation manually using the early phase of dynamic contrast-enhanced (DCE) MRI. Two segmentation methods were applied: a precise segmentation was carefully set to include only the enhancing area, and a rough segmentation covered the whole enhancing region, including the intervenient non-enhancing area. ResNet50 was implemented using the DCE MRI input. The diagnostic performance of the radiologists' readings and deep learning were then compared using receiver operating curve analysis.

RESULTS

The ResNet50 model from precise segmentation achieved diagnostic accuracy equivalent [area under the curve (AUC) = 0.91, 95% confidence interval (CI) 0.90, 0.93] to that of a highly experienced radiologist (AUC = 0.89, 95% CI 0.81, 0.96; p = 0.45). Even the model from rough segmentation showed diagnostic performance equivalent to a board-certified radiologist (AUC = 0.80, 95% CI 0.78, 0.82 vs. AUC = 0.79, 95% CI 0.70, 0.89, respectively). Both ResNet50 models from the precise and rough segmentation exceeded the diagnostic accuracy of a radiology resident (AUC = 0.64, 95% CI 0.52, 0.76).

CONCLUSION

These findings suggest that the deep learning model from ResNet50 has the potential to ensure accuracy in the diagnosis of NME on breast MRI.

Idiopathic Granulomatous Mastitis or Breast Cancer? A Comparative MRI Study in Patients Presenting with Non-Mass Enhancement

OBJECTIVE

To compare and determine discriminative magnetic resonance imaging (MRI) findings of idiopathic granulomatous mastitis (IGM) and breast cancer (BC) that present as non-mass enhancement.

MATERIALS AND METHODS

This retrospective study includes 68 IGM and 75 BC cases that presented with non-mass enhancement on breast MRI. All patients with a previous history of breast surgery, radiotherapy, or chemotherapy due to BC or a previous history of mastitis were excluded. On MRI images, presence of architectural distortion skin thickening, edema, hyperintense ducts containing protein, dilated fat-containing ducts and axillary adenopathies were noted. Cysts with enhancing walls, lesion size, lesion location, fistulas, distribution, internal enhancement pattern and kinetic features of non-mass enhancement were recorded. Apparent diffusion coefficient (ADC) values were calculated. Pearson chi-square test, Fisher's exact test, independent t test and Mann-Whitney U test were used as needed for statistical analysis and comparison. Multivariate logistic regression model was used to determine the independent predictors.

RESULTS

IGM patients were significantly younger than BC patients (p < 0.001). Cysts with thin (p < 0.05) or thick walls (p = 0.001), multiple cystic lesions, (p < 0.001), cystic lesions draining to the skin (p < 0.001), and skin fistulas (p < 0.05) were detected more often in IGM. Central (p < 0.05) and periareolar (p < 0.001) location and focal skin thickening (p < 0.05) were significantly more common in IGM. Architectural distortion (p = 0.001) and diffuse skin thickening (p < 0.05) were associated with BC. Multiple regional distribution was more common in IGM, whereas diffuse distribution and clumped enhancement were more common in BC (p < 0.05). In kinetic analysis, persistent enhancement was more common in IGM, whereas plateau and wash-out types were more common in BC (p < 0.001). Independent predictors for BC were age, diffuse skin thickening and kinetic curve types. There was no significant difference in the diffusion characteristics. Based on these findings, MRI had a sensitivity, specificity and accuracy of 88%, 67.65%, and 78.32%, respectively, in differentiating IGM from BC.

CONCLUSIONS

In conclusion, for non-mass enhancement, MRI can rule out malignancy with a considerably high sensitivity; however, specificity is still low, as many IGM patients have overlapping findings. Final diagnosis should be complemented with histopathology whenever necessary.

MRI Breast: Current Imaging Trends, Clinical Applications, and Future Research Directions

Magnetic Resonance Imaging (MRI) is the most sensitive and advanced imaging technique in diagnosing breast cancer and is essential in improving cancer detection, lesion characterization, and determining therapy response. In addition to the dynamic contrast-enhanced (DCE) technique, functional techniques such as magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) further characterize and differentiate benign and malignant lesions thus, improving diagnostic accuracy. There is now an increasing clinical usage of MRI breast, including screening in high risk and supplementary screening tools in average-risk patients. MRI is becoming imperative in assisting breast surgeons in planning breast-conserving surgery for preoperative local staging and evaluation of neoadjuvant chemotherapy response. Other clinical applications for MRI breast include occult breast cancer detection, investigation of nipple discharge, and breast implant assessment. There is now an abundance of research publications on MRI Breast with several areas that still remain to be explored. This review gives a comprehensive overview of the clinical trends of MRI breast with emphasis on imaging features and interpretation using conventional and advanced techniques. In addition, future research areas in MRI breast include developing techniques to make MRI more accessible and costeffective for screening. The abbreviated MRI breast procedure and an area of focused research in the enhancement of radiologists' work with artificial intelligence have high impact for the future in MRI Breast.

Contrast-Enhanced Cone-Beam Breast CT: An Analysis of Diagnostic Value in Predicting Breast Lesion With Rim Enhancement Malignancy

Background

The objective of the current study was to investigate the diagnostic value of contrast-enhanced cone-beam breast computed tomography (CE-CBBCT) for breast lesion with rim enhancement (RE).

Methods

All 36 patients were examined by non-contrast (NC-CBBCT) and contrast-enhanced CBBCT (CE-CBBCT) after contrast media (CM) injection. Qualitative morphological enhancement parameters and quantitative enhancement parameters were compared between malignant and benign groups. Multivariable logistic regression analysis was performed to identify independent factors that could predict breast lesion with RE malignancy. Receiver operating curve (ROC) was used to evaluate prediction performance.

Results

A total of 36 patients with 40 lesions underwent breast CE-CBBCT were enrolled. There were significant differences in most qualitative morphological enhancement parameters between the two groups. A multivariate logistic regression model showed that △standardized HU (INR_{phase 2}−INR_preCM) [odds ratio (OR) = 1.148, 95% CI = 1.034–1.276, p = 0.01] and △standardized HU (RP_{phase 2} − RP_{phase 1}) (OR = 0.891, 95% CI = 0.814–0.976, p = 0.013) were independent indicators in predicting breast lesion with RE malignancy. △standardized HU (INR_{phase 2} − INR_preCM) combined with △standardized HU (RP_{phase 2} − RP_{phase 1}) showed significant larger area under the receiver operating curve (AUC) and higher sensitivity than each alone (p < 0.001, AUC = 0.932, sensitivity = 92.59%, specificity = 92.31%). The regression equation of the prediction model was as follows: Logit (p) = 0.351 + 0.138X × △standardized HU (INR_{phase 2} − INR_preCM) − 0.115 × △standardized HU (RP_{phase 2} − RP_{phase 1}).

Conclusion

With the observation of qualitative morphological enhancement parameters and the comparison of quantitative enhancement parameters of CBBCT, a reliable basis for the diagnostic accuracy in predicting breast lesion with RE could be provided. These conclusions should be verified in large, well-designed studies.

An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions

OBJECTIVES

To build an artificial intelligence (AI) system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images.

METHODS

This retrospective study collected 965 pure NME lesions (539 benign and 426 malignant) confirmed by histopathology or follow-up in 903 women. The 754 NME lesions acquired by one MR scanner were randomly split into the training set, validation set, and test set A (482/121/151 lesions). The 211 NME lesions acquired by another MR scanner were used as test set B. The AI system was developed using ResNet-50 with the axial and sagittal MIP images. One senior and one junior radiologist reviewed the MIP images of each case independently and rated its Breast Imaging Reporting and Data System category. The performance of the AI system and the radiologists was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

The AI system yielded AUCs of 0.859 and 0.816 in the test sets A and B, respectively. The AI system achieved comparable performance as the senior radiologist (p = 0.558, p = 0.041) and outperformed the junior radiologist (p < 0.001, p = 0.009) in both test sets A and B. After AI assistance, the AUC of the junior radiologist increased from 0.740 to 0.862 in test set A (p < 0.001) and from 0.732 to 0.843 in test set B (p < 0.001).

CONCLUSION

Our MIP-based AI system yielded good applicability in classifying NME lesions in breast MRI and can assist the junior radiologist achieve better performance.

KEY POINTS

• Our MIP-based AI system yielded good applicability in the dataset both from the same and a different MR scanner in predicting malignant NME lesions. • The AI system achieved comparable diagnostic performance with the senior radiologist and outperformed the junior radiologist. • This AI system can assist the junior radiologist achieve better performance in the classification of NME lesions in MRI.

The cone-beam breast computed tomography characteristics of breast non-mass enhancement lesions

BACKGROUND

Cone-beam computed tomography (CBBCT) of the breast is emerging as a way of improving breast cancer diagnostic yield.

PURPOSE

To find characteristics of non-mass enhancement (NME) lesions on breast CBBCT and to identify the characteristics that distinguish malignant and benign lesions.

MATERIAL AND METHODS

Breast CBBCT images of 84 NME lesions were analyzed. Internal enhancement distribution and patterns, calcification distribution and suspicious morphology, and ΔHU enhancement values were compared between post-contrast and pre-contrast malignant and benign lesions. Univariate analyses were applied to find the strongest indicators of malignancy, and logistic regression analysis was used to develop a fitting equation for the combined diagnostic model.

RESULTS

In the 84 NME lesions, the indicators of malignancy were as follows: segmental enhancement distribution (P = 0.011, 53.62% sensitivity, 86.67% specificity, 94.87% positive predictive value [PPV], and 28.89% negative predictive value [NPV]), clumped internal enhancement patterns (P = 0.017, 50.72% sensitivity, 86.67% specificity, 94.59% PPV, and 27.66% NPV), ΔHU ≥ 93.57 Hounsfield units (HU) (P = 0.004, 66.67% sensitivity, 73.33% specificity, 92.00% PPV, and 32.35% NPV), and NME lesions with calcification (P = 0.002, 36.23% sensitivity, 20.00% specificity, 82.14% PPV, and 67.57% NPV). The fitting equation for the combined diagnostic model was as follows: Logit (P) = -0.579 +1.318 × enhancement distribution + 1.000 × internal enhancement patterns + 1.539 × ΔHU value + 1.641 ×NME type.

CONCLUSION

Individual diagnostic criteria based on breast CBBCT characteristics (segmental enhancement distribution, clumped internal enhancement patterns, ΔHU values > 93.57 HU, and NME lesions with calcification) had high specificity and PPV; when combined, they had high sensitivity in predicting malignant NME lesions.

Magnetic resonance imaging in the evaluation of pathologic nipple discharge: indications and imaging findings

Pathologic nipple discharge (PND) is typically unilateral, spontaneous, involves a single duct, and is serous or bloody in appearance. In patients with PND, breast MRI can be helpful as an additional diagnostic tool when conventional imaging with mammogram and ultrasound are negative. MRI is able to detect the etiology of nipple discharge in 56-61% of cases when initial imaging with mammogram and ultrasound are negative. Advantages to using MRI in evaluation of PND include good visualization of the retroareolar breast and better evaluation of posterior lesions which may not be well evaluated on mammograms and galactograms. It is also less invasive compared to central duct excision. Papillomas and nipple adenomas are benign breast masses that can cause PND and are well visualized on MRI. Ductal ectasia, and infectious etiologies such as mastitis, abscess, and fistulas are additional benign causes of PND that are well evaluated with MRI. MRI is also excellent for evaluation of malignant causes of PND including Paget's disease, ductal carcinoma in-situ and invasive carcinoma. MRI's high negative predictive value of 87-98.2% is helpful in excluding malignant etiologies of PND.

Subcategory classifications of Breast Imaging and Data System (BI-RADS) category 4 lesions on MRI

PURPOSE

Category 4 in BI-RADS for magnetic resonance imaging (MRI) has a wide range of probabilities of malignancy, extending from > 2 to < 95%. We classified category 4 lesions into three subcategories and analyzed the positive predictive value (PPV) of malignancy in a tertiary hospital.

MATERIALS AND METHODS

This retrospective study included 346 breast MRIs with 434 category 2-5 lesions. All enhancing lesions were classified as category 2 (0% probability of malignancy), 3 (> 0%, ≤ 2%), 4 (> 2%, < 95%) and 5 (≥ 95%); category 4 lesions were further subcategorized into 4A (> 2%, ≤ 10%), 4B (> 10%, ≤ 50%) and 4C (> 50%, < 95%) at the time of diagnosis. Radiological and pathological reports were retrospectively analyzed, and the PPVs were calculated.

RESULTS

We included 149 malignant and 285 benign lesions. The PPVs of subcategories 4A, 4B and 4C were 1.8%, 11.8% and 67.5%, respectively. The PPVs were higher for lesions coexisting with category 5 or 6 lesions compared with those for isolated lesions.

CONCLUSION

Category 4 lesions can be classified into three subcategories depending on the likelihood of malignancy. Lesions coexisting with category 5 or 6 lesions are more likely to be malignant.

Idiopathic Granulomatous Mastitis and its Mimics on Magnetic Resonance Imaging: A Pictorial Review of Cases from India

Objectives:

Idiopathic granulomatous mastitis (IGM) is a rare inflammatory disease of the breast, which is benign but potentially morbid. Mammographic and sonographic findings have been well characterized, but magnetic resonance imaging (MRI) findings have been less thoroughly documented. The objective of this study was to demonstrate characteristic findings for IGM and its mimics via a retrospective review.

Material and Methods:

Breast MRI examinations performed at Sir Ganga Ram Hospital in New Delhi, India between 2014 and 2019 were retrospectively reviewed to identify cases in which a pattern suggestive of granulomatous mastitis was seen. Cases of known malignancy were excluded. Any available breast pathology results were then obtained, and cases with presumptive or definitive diagnoses were compiled for analysis.

Results:

Overall, cases identified with characteristic imaging findings and confirmed diagnosis included seven cases of IGM, four cases of invasive ductal carcinoma, two cases of tuberculous mastitis, one case of non- tuberculous infectious mastitis, one case of foreign body mastitis, and one case of eosinophilc mastitis. One case of IGM with masses rather than of non-mass enhancement was also identified.

Conclusion:

In our review, cases with clustered ring enhancement were found to have inflammatory, idiopathic, infectious and malignant etiologies. While, these etiologies can only be reliably differentiated on pathology, familiarity with the pattern and an awareness of the differential may lead to decreased morbidity due to delays in diagnosis.

The role of MRI and clinicopathologic features in predicting the invasive component of biopsy-confirmed ductal carcinoma in situ

Background

The upgrade rate of biopsy-confirmed ductal carcinoma in situ (DCIS) to invasive carcinoma is up to 50% on final pathology. We investigated MRI and clinicopathologic predictors of the invasive components of DCIS diagnosed by preoperative biopsy and then compared MRI features between patients with DCIS, microinvasive ductal carcinoma (mIDC), and invasive ductal carcinoma (IDC) diagnosed on final pathology.

Methods

Two hundred and one patients with 206 biopsy-confirmed DCIS lesions were enrolled. MRI and clinicopathologic features were used to predict either mIDC or IDC via a cumulative logistic regression analysis. For the lesions detected on MRI, morphologic and kinetic analyses were performed using the Chi-square, Fisher’s exact, and Kruskal-Wallis tests.

Results

Of all the lesions, 112 (54.4%) were diagnosed as DCIS, 50 (24.3%) were upgraded to mIDC, and 44 (21.4%) to IDC. The detection on MRI as mass (Odds ratio (OR) = 8.84, 95% confidence interval (CI) = 1.05–74.04, P = 0.045) or non-mass enhancement (NME; OR = 11.17, 95% CI = 1.35–92.36, P = 0.025), negative progesterone receptor (PR; OR = 2.40, 95% CI = 1.29–4.44, P = 0.006), and high Ki-67 level (OR = 2.42, 95% CI = 1.30–4.50, P = 0.005) were significant independent predictors of histologic upgrade. On MRI, 87 (42.2%) lesions appeared as mass and 107 (51.9%) as NME. Irregularly shaped, not-circumscribed, heterogeneous, or rim-enhancing masses with intratumoral high signal intensity or peritumoral edema, clumped or clustered ring-enhancing NMEs, and high peak enhancement were significantly associated with histologic upgrade (P < 0.001).

Conclusion

MRI detection, negative PR, and high Ki-67 levels are associated with a histologic upgrade in patients with biopsy-confirmed DCIS. Suspicious MRI features are more frequent in such patients.

Differentiation between Clinically Noninflammatory Granulomatous Lobular Mastitis and Noncalcified Ductal Carcinoma in situ Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging

<b><i>Introduction:</i></b> Challenges in differentiation between clinically noninflammatory granulomatous lobular mastitis (GLM) and noncalcified ductal carcinoma in situ (DCIS) remain. <b><i>Objective:</i></b> To identify the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) characteristics contributing to their differential diagnosis. <b><i>Methods:</i></b> A total of 33 clinically noninflammatory GLM and 36 noncalcified DCIS were retrospectively analyzed in the study. Internal enhancement of a nonmass enhancement (NME) lesion was divided into clustered enhanced ring (absence/presence), and clustered enhanced ring (presence) was further classified as small and large ring based on the optimal cutoff value. The 5th Breast Imaging and Data System MRI descriptors were used for assessing the other DCE-MRI characteristics. Multivariate analysis including variables with significant differences in univariate analyses was conducted to identify the independent predictors. The discriminative abilities of different predictors and their combination were compared by area under the receiver-operating characteristic curves (AUCs). <b><i>Results:</i></b> An NME lesion was seen more commonly in clinically noninflammatory GLM than in noncalcified DCIS (<i>p</i> = 0.003). DCE-MRI characteristics with significant differences in univariate analyses included NME size, clustered enhanced ring (absence/presence), ring size, initial increase and kinetic characteristics for the differentiation between these two entities presenting as NME lesion. Clustered enhanced ring (presence) was further classified as small (≤7 mm) or large ring (&#x3e;7 mm). Multivariate analysis revealed that internal enhancement and initial increase were identified as significant independent predictors, and the AUC of their combination achieved the highest value of 0.867 (95% CI, 0.748–0.943). <b><i>Conclusions:</i></b> An NME lesion with a large ring is more highly suggestive of clinically noninflammatory GLM.

Ductal Carcinoma in Situ: Current Concepts in Biology, Imaging, and Treatment

Ductal carcinoma in situ (DCIS) of the breast is a group of heterogeneous epithelial proliferations confined to the milk ducts that nearly always present in asymptomatic women on breast cancer screening. A stage 0, preinvasive breast cancer, increased detection of DCIS was initially hailed as a means to prevent invasive breast cancer through surgical treatment with adjuvant radiation and/or endocrine therapies. However, controversy in the medical community has emerged in the past two decades that a fraction of DCIS represents overdiagnosis, leading to unnecessary treatments and resulting morbidity. The imaging hallmarks of DCIS include linearly or segmentally distributed calcifications on mammography or nonmass enhancement on breast MRI. Imaging features have been shown to reflect the biological heterogeneity of DCIS lesions, with recent studies indicating MRI may identify a greater fraction of higher-grade lesions than mammography does. There is strong interest in the surgical, imaging, and oncology communities to better align DCIS management with biology, which has resulted in trials of active surveillance and therapy that is less aggressive. However, risk stratification of DCIS remains imperfect, which has limited the development of precision therapy approaches matched to DCIS aggressiveness. Accordingly, there are opportunities for breast imaging radiologists to assist the oncology community by leveraging advanced imaging techniques to identify appropriate patients for the less aggressive DCIS treatments.

Interobserver variability and likelihood of malignancy for fifth edition BI-RADS MRI descriptors in non-mass breast lesions

Objective

Non-mass enhancement (NME) in breast MRI is the most common feature of ductal carcinoma in situ (DCIS). We sought to evaluate the interobserver variability and positive predictive value (PPV) for malignancy of NME descriptors using the fifth edition BI-RADS lexicon focusing on the newly introduced “clustered ring enhancement” pattern.

Materials and methods

Breast MRIs of 129 patients who had undergone MRI-guided vacuum-assisted biopsy (VAB) in our institution were reviewed. Studies assessed as NME were classified according to the fifth edition BI-RADS lexicon by two breast radiologists. Consensus was reached by involving a third radiologist. Interobserver variability and PPV for malignancy were assessed.

Results

Seventy-two of 129 studies were assessed as NME. The disagreement rate in the first assessment step (mass vs. NME) was low at 9.3% (ĸ = 0.81, 95% confidence interval [CI] 0.71–0.91). The disagreement rate for distribution patterns was 23.6% (ĸ = 0.67, 95% CI 0.54–0.80) and 22.2% (ĸ = 0.69, 95% CI 0.56–0.81) for internal enhancement patterns. Clustered ring enhancement (PPV 53.85, p = 0.038) and segmental distribution (PPV 62.5%, p = 0.028) had the highest malignancy rates among internal enhancement and distribution patterns with a significant result; the combination of clustered ring enhancement and segmental distribution raised the malignancy rate by approximately 4% (PPV 66.67%, p = 0.049).

Conclusion

There was a high agreement rate among readers when differentiating NME from mass lesions. The agreement rate was lower when assessing the distribution and internal enhancement pattern descriptors, but still substantial. The descriptors clustered ring enhancement and segmental distribution were significant predictors of malignancy.

Key Points

• Non-mass enhancement is a common morphological feature of non-invasive breast cancer (DCIS) in MRI. Differentiation between potentially malignant and benign changes may be very challenging.

• Since clustered ring enhancement and segmental distribution are both significant predictors of malignancy, the awareness of this important finding, combined with high-quality image interpretation skills, may improve the tumor detection rate.

• The combination of clustered ring enhancement and segmental distribution increases the positive predictive value for malignancy, which may be relevant for clinical practice.

The MRI characteristics of non-mass enhancement lesions of the breast: associations with malignancy

OBJECTIVE:

The American College of Radiology updated the terms used for expressing the imaging characteristics of non-mass enhancement (NME) lesions in the fifth edition of the breast imaging-reporting data system (BI-RADS) lexicon. Both the distribution and internal enhancement descriptors were revised for NME lesions. Our aim was to determine the MRI characteristics of NME lesions and to investigate their association with malignancy.

METHODS:

The MRI results of 129 NME lesions were retrospectively evaluated. The medical files, biopsy results and follow-up findings of lesions were recorded. Patients who had benign biopsy and those who had stable or regressed lesions during follow-up were classified as benign. All MRI results had been obtained with a 1.5 Tesla Signa HDx MR system (GE Healthcare).

RESULTS:

Segmental and diffuse distribution along with clustered-ring internal enhancement were significantly associated with malignancy, while linear distribution and homogeneous enhancement pattern were associated with benignancy. Additionally, the plateau type (Type II) curve was significantly more frequent in malignant lesions. There was no association between the presence of cystic structures and the benign/malignant nature of the lesion. However, multivariate logistic regression showed that only segmental distribution and diffusion restriction were associated with malignancy.

CONCLUSION:

In the current study, segmental distribution, clustered-ring enhancement, Type II dynamic curve and the presence of diffusion restriction were found to be associated with malignancy. There is a requirement for multicenter studies which include higher numbers of patients in order to better evaluate lesions with rarer characteristics for distribution and enhancement pattern.

ADVANCES IN KNOWLEDGE:

Our aim in this study was to investigate the MRI characteristics of NME lesions. We have reported the MRI findings of NME lesions and have found that segmental distribution and clustered-ring enhancement patterns are significantly more frequent in malignant lesions.

Patterns of malignant non-mass enhancement on 3-T breast MRI help predict invasiveness: using the BI-RADS lexicon fifth edition

Background Non-mass enhancements (NME) with invasive components account for 10-42% of total malignant NMEs. The factors associated with invasiveness on magnetic resonance imaging (MRI) could be useful for clinical assessment and treatment. Purpose To evaluate the clinical significances of the distributions and internal enhancement patterns (IEP) of malignant NMEs on 3-T breast MRI. Material and Methods A total of 448 consecutive women with newly diagnosed breast cancer that had undergone preoperative MRI and surgery between February 2013 and March 2016 were identified. After exclusions, 72 malignant NMEs without a mass in 72 women (mean age = 51.5 years) were included. Two readers independently assessed distributions and IEPs of NME, according to the Breast Imaging Reporting and Data System lexicon fifth edition. Collected data included the presence of invasion and histopathologic factors. Results A clustered ring IEP was significantly associated with invasive cancer (75.0%, P = 0.001, Reader1; 72.9%, P < 0.001, Reader 2), absence of necrosis (79.0%, P < 0.001; 72.1%, P < 0.001, respectively), and high Ki-67 expression (74.2%, P = 0.048; 74.2%, P = 0.003, respectively). A clumped IEP was related to ductal carcinoma in situ (33.3%, P = 0.025; 50.0%, P = 0.001, respectively), absence of lymph node metastasis (24.1%, P = 0.029; 31.5%, P = 0.030, respectively), and presence of necrosis (34.5%, P = 0.003; 44.8%, P = 0.001, respectively). Conclusion The presence of a clustered ring IEP in patients with breast cancer was found to be significantly associated with invasive breast cancer and high Ki-67 expression.

Evaluation of Breast Cancer Morphology Using Diffusion-Weighted and Dynamic Contrast-Enhanced MRI: Intermethod and Interobserver Agreement

BACKGROUND

The capability of diffusion-weighted imaging (DWI) for morphological analysis of breast lesions is underexplored.

PURPOSE

To evaluate the utility of DWI for assessment of morphological features of breast cancer by comparing DWI and dynamic contrast-enhanced (DCE) MRI findings to determine intermethod and interobserver agreement.

STUDY TYPE

Retrospective.

POPULATION

Seventy-eight women with pathohistologically proven breast cancer.

FIELD STRENGTH/SEQUENCE

1.5T. DWI and DCE images.

ASSESSMENT

Diffusion-weighted and DCE images were placed in two separate case sets. Three radiologists, blinded to all other information, independently evaluated each case set on two separate occasions. Lesions were interpreted according to the fifth edition of the ACR BI-RADS lexicon.

STATISTICAL ANALYSIS

Kappa (κ) statistics were calculated in order to assess intermethod and interobserver agreement.

RESULTS

For values that attained statistical significance (P < 0.05), intermethod agreement ranged from fair (κ = 0.22) for nonmass internal patterns to significant (κ = 0.8) for lesion type. On DWI, interobserver agreement varied from fair (κ = 0.34) for mass shape to significant (κ = 0.75) for lesion type. On DCE MRI, interobserver agreement varied from fair (κ = 0.27) for irregular vs. spiculated mass margin to perfect (κ = 1) for circumscribed vs. noncircumscribed mass margin.

DATA CONCLUSION

On the whole, there was moderate intermethod agreement. The values of interobserver agreement were mostly similar between DWI and DCE MRI. This suggests that DWI is applicable for morphological assessment of breast cancer, notwithstanding substantially inferior spatial resolution compared to DCE MRI.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:1381-1390.

Grading system to categorize breast MRI using BI-RADS 5th edition: a statistical study of non-mass enhancement descriptors in terms of probability of malignancy

PURPOSE

To analyze the association of breast non-mass enhancement descriptors in the BI-RADS 5th edition with malignancy, and to establish a grading system and categorization of descriptors.

MATERIALS AND METHODS

This study was approved by our institutional review board. A total of 213 patients were enrolled. Breast MRI was performed with a 1.5-T MRI scanner using a 16-channel breast radiofrequency coil. Two radiologists determined internal enhancement and distribution of non-mass enhancement by consensus. Corresponding pathologic diagnoses were obtained by either biopsy or surgery. The probability of malignancy by descriptor was analyzed using Fisher's exact test and multivariate logistic regression analysis. The probability of malignancy by category was analyzed using Fisher's exact and multi-group comparison tests.

RESULTS

One hundred seventy-eight lesions were malignant. Multivariate model analysis showed that internal enhancement (homogeneous vs others, p < 0.001, heterogeneous and clumped vs clustered ring, p = 0.003) and distribution (focal and linear vs segmental, p < 0.001) were the significant explanatory variables. The descriptors were classified into three grades of suspicion, and the categorization (3, 4A, 4B, 4C, and 5) by sum-up grades showed an incremental increase in the probability of malignancy (p < 0.0001).

CONCLUSION

The three-grade criteria and categorization by sum-up grades of descriptors appear valid for non-mass enhancement.

Clinics in diagnostic imaging (180). Ductal carcinoma in situ (DCIS)

A 26-year-old woman presented with a slow-growing right breast lump. Excision biopsy of the lump showed invasive ductal carcinoma with adjacent ductal carcinoma in situ (DCIS). Preoperative imaging was performed to assess the extent of disease. Magnetic resonance (MR) imaging of the breasts showed an area of clustered ring enhancement deep to the biopsy site, which was representative of residual DCIS. DCIS is a common noninvasive malignancy that manifests as a primary breast tumour or in association with other lesions. The radiological features of DCIS are discussed herein, with special attention to the clustered ring enhancement pattern on MR imaging.

Magnetic resonance imaging characteristics of granulomatous mastitis

Granulomatous mastitis (GM) is a benign chronic inflammatory condition of the breast. This study was performed to determine the utility of magnetic resonance imaging (MRI) in differentiating GM from malignancy. MRI findings in 12 women with clinical or histopathologically-proven GM were retrospectively reviewed. Non-mass enhancement on MRI was present in all 12 patients with clustered ring enhancement being the most common pattern (n=7, 58%). Architectural distortion (n=10, 83%), skin thickening (n=10, 83%) and focal skin enhancement (n=10, 83%) were also very common. MRI features of GM are often identical to features considered suspicious for malignancy on MRI.

Is there any Correlation between Magnetic Resonance Imaging Features of Breast Lesions of BIRADS Category 4 with Histopathologic Results?

BACKGROUND

To evaluate the correlation of magnetic resonance imaging (MRI) features of breast lesions of Breast Imaging Reporting and Database System (BI-RADS) category 4 with histopathologic results.

MATERIALS AND METHODS

In a prospective study between December 2013 and April 2015, patients with suspicious mammographic and/or ultrasound findings referred for Breast MRI were evaluated. Patients with lesions of BI-RADS category 4 were enrolled with a written informed consent. In each patient, mass lesion (ML) or nonmass lesion (NML) was determined, and different characteristics of the lesions were recorded. A follow-up program was taken with mean 3-12 months. Patients who underwent core needle biopsy or open biopsy were summoned.

RESULTS

Seventy-eight females aged 24-67 years (mean 43.1 ± 8.8) met the inclusion criteria and had adequate samples for histopathologic study. Twenty-nine (37.2%) patients had ML and 49 (62.8%) patients had NML. Tissue sampling in 63 (80.7%) patients was through core needle biopsy and in 15 (19.2%) patients through surgery. A wide spectrum of benign and malignant pathologic diagnoses was seen. In statistical analysis, none of the MRI features has a significant correlation with any specific histopathologic diagnosis (P = 0.185). However, the relation between the MRI category (ML or NML) and pathology results was significant at level of 0.1 (P = 0.06).

CONCLUSION

This study showed that a wide spectrum of histopathologic results is seen in BI-RADS category 4. However, in this sample volume, none of the MRI features in this BI-RADS category has a significant correlation with any specific histopathologic diagnosis.

How I report breast magnetic resonance imaging studies for breast cancer staging and screening

Magnetic resonance imaging (MRI) of the breast is the most sensitive imaging technique for the diagnosis and local staging of primary breast cancer and yet, despite the fact that it has been in use for 20 years, there is little evidence that its widespread uncritical adoption has had a positive impact on patient-related outcomes.

This has been attributed previously to the low specificity that might be expected with such a sensitive modality, but with modern techniques and protocols, the specificity and positive predictive value for malignancy can exceed that of breast ultrasound and mammography. A more likely explanation is that historically, clinicians have acted on MRI findings and altered surgical plans without prior histological confirmation. Furthermore, modern adjuvant therapy for breast cancer has improved so much that it has become a very tall order to show a an improvement in outcomes such as local recurrence rates.

In order to obtain clinically useful information, it is necessary to understand the strengths and weaknesses of the technique and the physiological processes reflected in breast MRI. An appropriate indication for the scan, proper patient preparation and good scan technique, with rigorous quality assurance, are all essential prerequisites for a diagnostically relevant study.

The use of recognised descriptors from a standardised lexicon is helpful, since assessment can then dictate subsequent recommendations for management, as in the American College of Radiology BI-RADS (Breast Imaging Reporting and Data System) lexicon (Morris et al., ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System, 2013). It also enables audit of the service. However, perhaps the most critical factor in the generation of a meaningful report is for the reporting radiologist to have a thorough understanding of the clinical question and of the findings that will influence management. This has never been more important than at present, when we are in the throes of a remarkable paradigm shift in the treatment of both early stage and locally advanced breast cancer.

Idiopathic granulomatous mastitis: magnetic resonance imaging findings with diffusion MRI

BACKGROUND

Idiopathic granulomatous mastitis (IGM) is a rare benign breast disease with unknown etiology which can mimic breast carcinoma, both clinically and radiologically. Magnetic resonance imaging (MRI) findings of IGM have been previously described; however there is no study evaluating diffusion-weighted MRI findings of IGM.

PURPOSE

To analyze conventional, dynamic contrast-enhanced, and diffusion-weighted MRI signal characteristics of IGM by comparing it with the contralateral normal breast parenchyma.

MATERIAL AND METHODS

A total of 39 patients were included in the study. On dynamic contrast-enhanced MRI, the distribution and enhancement patterns of the lesions were evaluated. We also detected the frequencies of involving quadrants, retroareolar involvement, accompanying abscess, and skin edema. T2-weighted (T2W) and STIR signal intensities and both mean and minimum apparent diffusion coefficient (ADC) values were compared with the contralateral normal parenchyma.

RESULTS

IGM showed significantly lower mean and minimum ADC values when compared with the normal parenchyma. Signal intensities on T2W and STIR sequences of the lesion were significantly higher than the normal parenchyma. On dynamic contrast-enhanced MRI, 7.7% of the patients had mass-like contrast enhancement, 92.3% of the patients had non-mass-like contrast enhancement. Abscess was positive in 33.3% of the patients.

CONCLUSION

As a result, IGM showed commonly non-mass-like lesions with restricted diffusion. Although it is a benign pathology, it may show clustered ring-like enhancement like malignant lesions.

Descriptors of Malignant Non-mass Enhancement of Breast MRI: Their Correlation to the Presence of Invasion

RATIONALE AND OBJECTIVES

This study aims to investigate the clinical significance of malignant non-mass enhancement (NME) descriptors in breast magnetic resonance images by assessing their correlation to the presence of invasion or lymph node metastasis.

MATERIALS AND METHODS

Three radiologists independently reviewed magnetic resonance images with malignant NMEs between January 2008 and December 2009. Distribution was assessed first, and then each of four internal enhancement patterns-clumped, clustered ring, branching, and hypointense area-was evaluated dichotomously (yes or no). Because clustered rings and hypointense areas were thought to be major structural elements of heterogeneous NMEs, they were also evaluated by integrating them into one collective descriptor we called the "heterogeneous structures." Chi-square test, Fisher exact test, or Student t test was used to analyze differences of variables by each reviewer. Positive predictive values (PPVs) of descriptors in predicting presence of invasion or lymph node metastasis were calculated. P < 0.05 was considered significant.

RESULTS

We included 131 malignant NMEs (76 in situ and 55 invasive) in 129 patients (two bilateral). All three observers' results showed clustered rings (PPVs 54.5%, 54.5%, 50.0%) (P = 0.0005, 0.038, 0.029) and hypointense areas (PPVs 63.6%, 61.5%, 73.9%) (P = 0.004, 0.024, 0.0006) to be significantly associated with invasion. When clustered rings and hypointense areas were integrated into heterogeneous structures, they were significantly associated with invasion (PPVs 54.3%, 53.3%, 51.8%) (P = 0.0003, 0.016, 0.003).

CONCLUSIONS

The NME descriptors clustered rings, hypoechoic areas, and heterogeneous structures, assessed collectively, were associated with invasive breast cancer.

Morphology evaluation of nonmass enhancement on breast MRI: Effect of a three-step interpretation model for readers' performances and biopsy recommendations

OBJECTIVE

To evaluate and compare the use of a newly introduced interpretation model for breast nonmass enhancement (NME, defined as an area of enhancement without a three-dimensional, space-occupying lesion) with the use of the standard interpretation method based on BI-RADS.

MATERIALS AND METHODS

Two expert and two less-experienced breast imaging radiologists performed reading sessions of 86 malignant and 64 benign NME lesions twice. First, radiologists characterized NME using BI-RADS descriptors and assessed the likelihood of malignancy and need for a biopsy. Second, the likelihood of malignancy and need for a biopsy were assessed with the use of the model, in which three-step characterization of morphological features were performed: (1) selection of distribution modifiers, (2) homogeneous vs. heterogeneous internal enhancement (IE) pattern, and (3) evaluation of presence of "clumped", "clustered ring enhancement (CRE)", and "branching" IE signs. Multireader-multicase receiver operating characteristic analysis was used to evaluate observers' performances. Univariate and multivariate logistic regression analyses were performed for morphology descriptors.

RESULTS

With use of the model, average Az of less-experienced radiologists (0.77-0.83; p=0.013) and average sensitivity of all radiologists (96.2-98.2%; p=0.007) improved significantly. NPV also improved but nonsignificantly (81.1-91.9%; p=0.055). Multivariate analyses of the second reading showed branching, clumped, and CRE signs to be significant predictors of malignancy in the results of 3, 2, and 2 readers, respectively.

CONCLUSION

The three-step interpretation model for NME has the potential to improve less-experienced radiologists' performances, making them comparable to expert breast imagers.

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

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

Association of Different MRI BIRADS Descriptors With Malignancy in Non Mass-Like Breast Lesions

Background:

Several studies on the diagnostic efficacy of MRI has not real consensus for the accuracy of MRI characteristics in non mass like breast lesions, and the number of malignant lesions in different studies is insufficient.

Objectives:

In this study we aimed to analyze the diagnostic role of MRI BIRADS features for diagnosis of malignancy in non mass like breast lesions.

Patients and Methods:

All patients with positive findings (BIRADS 3, 4, 5), which had either biopsy proved pathology or follow-up MRI data at least for 12 months were included in the study. Finally, 213 breasts MRI that showed non mass like enhancing lesions among our patients were assessed in study. One experienced breast radiologist who was unaware of any clinical information or the histopathologic diagnosis evaluated all images retrospectively. The morphologic parameters evaluated consisted of distribution modifiers and pattern of internal enhancement. The kinetic enhancement parameters were assessed as showing washout, plateau, or persistent patterns. In the enhancement kinetic analysis, thew most worrisome curve type in each lesion was considered for interpretation, if it was more than 2% enhancement. We have evaluated the visual findings by comparison of the signal intensity on the first and third dynamic series. Data for the study were extracted from the breast MRI database and analyzed using SPSS version 16 statistical software.

Results:

Totally 188 patients had 213 non mass like lesions. Mean age of the patients was 44.9 ± 8.3 years (24-63). Totally 46 of lesions were malignant (21.6%). The most common BIRADS score was 4 (116; 54.5%). The most prevalent feature of distribution, internal enhancement and curve type were focal (59.2%), clumped (27.2%) and washout (34.3%). Distribution of different subgroups of MR BIRADS features was different among benign and malignant lesions (All Pvalues < 0.05). Regarding association with malignancy, odds ratio of lesions with segmental or ductal linear distribution was 3.4 (95% CI = 1.7-6.8), Clumped, Reticular and Dendritic internal enhancement was 2.5 (95% CI = 1.3-5) and wash out curve type was 5.4 (95% CI = 2.7-10.9). Sensitivity of higher MR BIRADS (4,5) for diagnosis of malignancy was 100%. Specificity of segmental or ductal linear distribution in diagnosis of malignancy was 81%. Specificity of BIRADS 5 for diagnosis of malignancy was 98%. In a multivariate logistic regression analysis for diagnosis of malignancy in which distribution, internal enhancement and curve type were considered as independent variables, distribution and curve type remained significant in the model while the internal enhancement showed a borderline P-value.

Conclusions:

Although in our study washout pattern was the most powerful indicator for malignant pathology in non mass like enhancing lesions, more studies with larger sample size needs in this regard.

Ductal carcinoma in situ of the breasts: review of MR imaging features

The incidence of ductal carcinoma in situ (DCIS) has increased over the past few decades and now accounts for over 20% of newly diagnosed cases of breast cancer. Although the detection of DCIS has increased with the advent of widespread mammography screening, it is essential to have a more accurate assessment of the extent of DCIS for successful breast conservation therapy. Recent studies evaluating the detection of DCIS with magnetic resonance (MR) imaging have used high spatial resolution techniques and have increasingly been performed to screen a high-risk population as well as to evaluate the extent of disease. This work has shown that MR imaging is the most sensitive modality currently available for identifying DCIS and is more accurate than mammography in evaluating the extent of DCIS. MR imaging is particularly sensitive for identifying high-grade and intermediate-grade DCIS. DCIS may have variable morphologic features on MR images, with non-mass enhancement morphology being the most common manifestation. Less commonly, DCIS may also manifest as a mass on MR images, in which case it is most likely to be irregular. The kinetics of DCIS are also variable, with fast uptake and a plateau curve reported as the most common kinetic pattern. Additional MR imaging tools such as diffusion-weighted imaging and quantitative kinetic analysis combined with the benefit of high field strength, such as 3 T, may increase the sensitivity and specificity of breast MR imaging in the detection of DCIS.

MR features to suggest microinvasive ductal carcinoma of the breast: can it be differentiated from pure DCIS?

BACKGROUND

Morphologic and kinetic characteristics of breast lesions are regarded as a major criterion for their differential diagnosis in dynamic magnetic resonance imaging (MRI). However, there have not been well-reported MRI findings of microinvasive ductal carcinoma.

PURPOSE

To evaluate MRI characteristics of microinvasive ductal carcinoma of the breast and to compare MRI findings in patients with microinvasive ductal carcinoma and pure ductal carcinoma in situ (DCIS).

MATERIAL AND METHODS

Eighty-one patients with pathologically confirmed microinvasive ductal carcinomas (n = 37) or pure DCIS (n = 44) were included in this study. The MRI findings were analyzed without knowledge of the pathologic and conventional imaging findings. For all the lesions detected on MRI, morphologic and kinetic analyses were performed according to the Breast Imaging Reporting and Data System. For the non-mass lesions, the presence of clustered ring enhancement was also analyzed. Statistical analyses were performed using Student's t test, χ(2) test, and Fisher's exact test.

RESULTS

In total 35 cases of microinvasive ductal carcinoma and 39 cases of DCIS were detected on MRI. The most common and dominant MRI findings of microinvasive ductal carcinoma and DCIS were non-mass lesions with heterogeneous enhancement. However, the spiculated margin of the mass-type lesion (P = 0.022), the segmental distribution (P = 0.023), and clustered ring enhancement (P = 0.006) of the non-mass-type lesion, and the enhancement kinetics showing strong initial enhancement (P = 0.004) with subsequent wash-out (P = 0.001) were significantly more frequent in microinvasive ductal carcinoma than in DCIS.

CONCLUSION

Non-mass lesions with segmental distribution, heterogeneous enhancement, and strong initial enhancement with a wash-out curve were the dominant MRI findings of microinvasive ductal carcinoma. Compared with DCIS, microinvasive ductal carcinoma showed more suspicious imaging characteristics. For the non-mass lesions, clustered ring enhancement was also a characteristic finding of microinvasion on MRI.

Updates and revisions to the BI-RADS magnetic resonance imaging lexicon

This article summarizes the updates and revisions to the second edition of the BI-RADS MRI lexicon. A new feature in the lexicon is background parenchymal enhancement and its descriptors. Another major focus is on revised terminology for masses and non-mass enhancement. A section on breast implants and associated lexicon terms has also been added. Because diagnostic breast imaging increasingly includes multimodality evaluation, the new edition of the lexicon also contains revised recommendations for combined reporting with mammography and ultrasound if these modalities are included as comparison, and clarification on the use of final assessment categories in MR imaging.

High-spatial-resolution 3-T breast MRI of nonmasslike enhancement lesions: an analysis of their features as significant predictors of malignancy

OBJECTIVE

The purpose of this study was to analyze the features of nonmasslike enhancement detected on 3-T MRI and to determine which of these features are significant predictors of malignancy.

MATERIALS AND METHODS

Retrospective review was performed of 124 consecutive patients with nonmasslike enhancement detected on 3-T MRI after biopsy or surgery. We described nonmasslike enhancement using the descriptors in the BI-RADS MRI lexicon. In addition to the BI-RADS descriptors, whether clustered ring enhancement was present and whether surrounding high signal intensity (SI) was present on T2-weighted imaging were assessed.

RESULTS

Cancer was identified in 85 lesions (69%). Of these lesions, ductal carcinoma in situ (DCIS) was found in 41 (48%) and invasive cancer in 44 (52%). The features found to be significant predictors of malignancy were segmental (p = 0.001), focal (p = 0.006), dendritic (p = 0.017), and clustered ring enhancement (p = 0.026) and surrounding high SI on T2-weighted imaging (p < 0.0001). The features found to be significant predictors of invasive cancer were dendritic enhancement (p < 0.0001) and surrounding high SI on T2-weighted imaging (p < 0.0001). There were no significant predictive features for DCIS. Homogeneous enhancement was found to be a significant predictor of benignancy (p = 0.001). Kinetic patterns were not significant predictors of malignancy. Nonmasslike enhancement of 1 cm or larger was more often malignant than lesions smaller than 1 cm (p < 0.0001). In multivariate analysis, a lesion size of 1 cm or larger was found to be the only significant predictor of malignancy for nonmasslike enhancement.

CONCLUSION

Segmental, focal, dendritic, and clustered ring enhancement; surrounding high SI on T2-weighted imaging; and a lesion size of 1 cm or larger can act as predictors of malignancy for nonmasslike enhancement detected on 3-T MRI, but kinetic characteristics cannot.