Revisiting Nonmass Enhancement in Breast MRI: Analysis of Outcomes and Follow-Up Using the Updated BI-RADS Atlas

Chikarmane, Sona A.; Michaels, Aya; Giess, Catherine S.

doi:10.2214/ajr.17.18086

Cited by 30 publications

(29 citation statements)

References 16 publications

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“…Apart from the interobserver variability, the results showed that clustered ring enhancement and segmental distribution pattern are significantly more frequently associated with malignancy in comparison with other NME descriptors. This finding is concordant with several previous studies [17, 21, 28, 29]. There are prior studies both with higher and lower absolute results, but there is only one prior study [17] performed after the publication of the revised fifth edition of BI-RADS lexicon (Table 4).…”

Section: Discussionsupporting

confidence: 91%

“…This finding is concordant with several previous studies [17, 21, 28, 29]. There are prior studies both with higher and lower absolute results, but there is only one prior study [17] performed after the publication of the revised fifth edition of BI-RADS lexicon (Table 4). This study, by Chikarmane et al, departs from our study in a few aspects.…”

Section: Discussionsupporting

confidence: 91%

“…of malignant lesions/total no. of lesions with segmental distribution (PPV%)Tozaki et al [21]6123/61 (37%)22/23 (96%)19/61 (31%)19/19 (100%)Uematsu et al [29]12466/124 (53%)51/66 (77%)53/124 (43%)45/53 (85%)Chikarmane et al [17]*14430/144 (21%)11/30 (37%)25/144 (17%)10/25 (40%)Our study*6713/67 (19.4%)7/13 (53.85%)8/67 (11.9%)5/8 (62.5%)*Performed after the introduction of the revised BI-RADS lexicon 5th edition (2014)…”

Section: Discussionmentioning

confidence: 99%

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

Lunkiewicz

Forte

Freiwald³

et al. 2019

Eur Radiol

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ObjectiveNon-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 methodsBreast 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.ResultsSeventy-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).ConclusionThere 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.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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

Lunkiewicz

Forte

Freiwald³

et al. 2019

Eur Radiol

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“…While most cancers are easily recognizable by their morphologic features alone, smaller lesions are more difficult to assess (62). In general, the features of NME are less specific than those of masses (60,63,64). Foci have a likelihood of malignancy of 2.9%-6% (65).…”

Section: Dwi Sequencesmentioning

confidence: 99%

Breast MRI: State of the Art

Mann¹,

Cho²,

Moy³

2019

Radiology

562

325

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reast MRI is an indispensable modality, along with mammography and US. Its main indications are staging of known cancer, screening for breast cancer in women at increased risk, and evaluation of response to neoadjuvant chemotherapy (1-3). As opposed to mammography and US, MRI is a functional technique. Heywang et al (4) and Kaiser and Zeitler (5) independently introduced this technique in the 1980s. Contrast material-enhanced MRI evaluates the permeability of blood vessels by using an intravenous contrast agent (gadolinium chelate) that shortens the local T1 time, leading to a higher signal on T1-weighted images (6). The underlying principle is that neoangiogenesis leads to formation of leaky vessels that allow for faster extravasation of contrast agents (7), thus leading to rapid local enhancement. Despite improvements in the technique of breast MRI, this principle is still the basis of all clinical MRI protocols. However, most MRI protocols nowadays are multiparametric (8,9). This review describes the current state of the art in breast MRI, with a focus on the major indications and the potential indication-based adaptations to the imaging protocol to maximize its value. Requirements for Breast MRI Breast MRI studies should be interpreted by radiologists with expertise in breast imaging, including mammographic and US studies, as these examinations are often complementary. Although empirical data on the learning curve for breast MRI are lacking, some studies showed improved performance of radiologists over time (10), and reporting breast MRI studies requires sufficient exposure to the technique. It is best practice to use a field strength of at least 1.5 T to acquire images at a sufficiently high spatial resolution (1-3). Utilization of a dedicated breast coil is mandatory to obtain images of diagnostic quality. Women lie in the prone position with the breasts hanging free in the recesses of the coil. This design allows the breast tissue to spread, which facilitates detection of abnormalities and prevents motion artifacts induced by respiration (11,12). A breast coil should have at least four channels, but modern designs

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“…The morphological features and dynamic contrast enhanced (DCE) parameters in benign and malignant NME have been studied extensively. Many investigators con rmed the results obtained by Tozaki et al for the NME internal enhancement and distribution patterns, and they suggested that most benign NMEs appeared with a linear distribution and homogeneous internal enhancement, whereas lesions exhibiting a heterogeneous and clustered ring internal enhancement with segmental distribution were highly suggestive of malignant NME [20][21][22]. For TIC, previous studies have demonstrated no statistically signi cant differences between benign and malignant lesions in any type of enhancement pattern [23].…”

Section: Discussionmentioning

confidence: 70%

Additive Value of Texture Analysis Based on Breast MRI for Distinguishing Between Benign and Malignant Non-mass Enhancement in Premenopausal Women

Tan

Mai

Huang

et al. 2020

Preprint

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Background: Non-mass enhancement (NME) is a diagnostic dilemma. Texture analysis (TA) could serve as an objective method to quantify tumor characteristics and growth patterns. However, there are few reports about TA use in NME diagnosis. To our knowledge, NME diagnosis based on the combination of the features noted on routine MRI and TA has not been reported.. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. Methods: Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann-Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis.Results: The combination model showed a superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising.Conclusion: The combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.

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Revisiting Nonmass Enhancement in Breast MRI: Analysis of Outcomes and Follow-Up Using the Updated BI-RADS Atlas

Cited by 30 publications

References 16 publications

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

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

Breast MRI: State of the Art

Additive Value of Texture Analysis Based on Breast MRI for Distinguishing Between Benign and Malignant Non-mass Enhancement in Premenopausal Women

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