Probability of malignancy for lesions detected on breast MRI: a predictive model incorporating BI-RADS imaging features and patient characteristics

Reducing False Negatives in Biopsy of Suspicious MRI Findings

Breast MRI is a highly sensitive imaging modality that often detects findings that are occult on mammography and US. Given the overlap in appearance of benign and malignant lesions, an accurate method of tissue sampling for MRI-detected findings is essential. Although MRI-directed US and correlation with mammography can be helpful for some lesions, a correlate is not always found. MRI-guided biopsy is a safe and effective method of tissue sampling for findings seen only on MRI. The unique limitations of this technique, however, contribute to false negatives, which can result in delays in diagnosis and adverse patient outcomes; this is of particular importance as most MRI examinations are performed in the high-risk or preoperative setting. Here, we review strategies to minimize false negatives in biopsy of suspicious MRI findings, including appropriate selection of biopsy modality, use of meticulous MRI-guided biopsy technique, management after target nonvisualization, assessment of adequate lesion sampling, and determination of radiology-pathology concordance. A proposed management algorithm for MRI-guided biopsy results will also be discussed.

A simplified scoring protocol to improve diagnostic accuracy with the breast imaging reporting and data system in breast magnetic resonance imaging

BACKGROUND

The breast imaging reporting and data system (BI-RADS) lexicon provides a standardized terminology for describing leision characteristics but does not provide defined rules for converting specific imaging features into diagnostic categories. The inter-reader agreement of the BI-RADS is moderate. In this study, we explored the use of a simplified protocol and scoring system for BI-RADS categorization which integrates the morphologic features (MF), kinetic time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values with equal weights, with a view to providing a convenient and practical method for breast magnetic resonance imaging (MRI) and improving the inter-reader agreement and diagnostic performance of BI-RADS.

METHODS

This cross-sectional, retrospective, single-center study included 879 patients with 898 histopathologically verified lesions who underwent an MRI scan on a 3.0 Tesla GE Discovery 750 MRI scanner between January 1, 2017, and June 30, 2020. The BI-RADS categorization of the studied lesions was assessed according to the sum of the assigned scores (the presence of malignant MF, lower ADC, and suspicious TIC each warranted a score of +1). Total scores of +2 and +3 were classified as category 5, scores of +1 were classified as category 4, and scores of +0 but with other lesions of interest were classified as category 3. The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity, and accuracy of this categorization were investigated to assess its efficacy and its consistency with pathology.

RESULTS

There were 472 malignant, 104 risk, and 322 benign lesions. Our simplified scoring protocol had high diagnostic accuracy, with an area under curve (AUC) value of 0.896. In terms of the borderline effect of pathological risk and category 4 lesions, our results showed that when risk lesions were classified together with malignant ones, the AUC value improved (0.876 vs. 0.844 and 0.909 vs. 0.900). When category 4 and 5 lesions were classified as malignant, the specificity, accuracy, and AUC value decreased (82.3% vs. 93.2%, 89.3% vs. 90.2%, and 0.876 vs. 0.909, respectively). Therefore, to improve the diagnostic accuracy of the protocol for BI-RADS categorization, only category 5 lesions should be considered to be malignant.

CONCLUSIONS

Our simplified scoring protocol that integrates MF, TIC, and ADC values with equal weights for BI-RADS categorization could improve both the diagnostic performance of the protocol for BI-RADS categorization in clinical practice and the understanding of the benign-risk-malignant breast diseases.

Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score

BACKGROUND

 Breast MRI is the most sensitive method for the detection of breast cancer and is an integral part of modern breast imaging. On the other hand, interpretation of breast MRI exams is considered challenging due to the complexity of the available information. Clinical decision rules that combine diagnostic criteria in an algorithm can help the radiologist to read breast MRI by supporting objective and largely experience-independent diagnosis.

METHOD

 Narrative review. In this article, the Kaiser Score (KS) as a clinical decision rule for breast MRI is introduced, its diagnostic criteria are defined, and strategies for clinical decision making using the KS are explained and discussed.

RESULTS

 The KS is based on machine learning and has been independently validated by international research. It is largely independent of the examination technique that is used. It allows objective differentiation between benign and malignant contrast-enhancing breast MRI findings using diagnostic BI-RADS criteria taken from T2w and dynamic contrast-enhanced T1w images. A flowchart guides the reader in up to three steps to determine a score corresponding to the probability of malignancy that can be used to assign a BI-RADS category. Individual decision making takes the clinical context into account and is illustrated by typical scenarios.

KEY POINTS

· The KS as an evidence-based decision rule to objectively distinguish benign from malignant breast lesions is based on information contained in T2w und dynamic contrast-enhanced T1w sequences and is largely independent of specific examination protocols.. · The KS diagnostic criteria are in line with the MRI BI-RADS lexicon. We focused on defining a default category to be applied in the case of equivocal imaging criteria.. · The KS reflects increasing probabilities of malignancy and, together with the clinical context, assists individual decision making..

CITATION FORMAT

· Baltzer PA, Krug KB, Dietzel M. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1829-5985.

An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies

Objectives

Due to its high sensitivity, DCE MRI of the breast (bMRI) is increasingly used for both screening and assessment purposes. The high number of detected lesions poses a significant logistic challenge in clinical practice. The aim was to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions and thereby avoid unnecessary biopsies.

Methods

This retrospective study included consecutive patients with MRI-suspicious findings (BI-RADS 4/5). Two blinded readers analyzed DCE images using a commercially available software, automatically extracting BI-RADS curve types and pharmacokinetic enhancement features. After principal component analysis (PCA), a neural network–derived A.I. classifier to discriminate benign from malignant lesions was constructed and tested using a random split simple approach. The rate of avoidable biopsies was evaluated at exploratory cutoffs (C₁, 100%, and C₂, ≥ 95% sensitivity).

Results

Four hundred seventy (295 malignant) lesions in 329 female patients (mean age 55.1 years, range 18–85 years) were examined. Eighty-six DCE features were extracted based on automated volumetric lesion analysis. Five independent component features were extracted using PCA. The A.I. classifier achieved a significant (p < .001) accuracy to distinguish benign from malignant lesion within the test sample (AUC: 83.5%; 95% CI: 76.8–89.0%). Applying identified cutoffs on testing data not included in training dataset showed the potential to lower the number of unnecessary biopsies of benign lesions by 14.5% (C₁) and 36.2% (C₂).

Conclusion

The investigated automated 4D radiomics approach resulted in an accurate A.I. classifier able to distinguish between benign and malignant lesions. Its application could have avoided unnecessary biopsies.

Key Points

• Principal component analysis of the extracted volumetric and temporally resolved (4D) DCE markers favored pharmacokinetic modeling derived features.

• An A.I. classifier based on 86 extracted DCE features achieved a good to excellent diagnostic performance as measured by the area under the ROC curve with 80.6% (training dataset) and 83.5% (testing dataset).

• Testing the resulting A.I. classifier showed the potential to lower the number of unnecessary biopsies of benign breast lesions by up to 36.2%, p < .001 at the cost of up to 4.5% (n = 4) false negative low-risk cancers.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07787-z.

Scoring System to Predict Malignancy for MRI-Detected Lesions in Breast Cancer Patients: Diagnostic Performance and Effect on Second-Look Ultrasonography

Purpose

To design a scoring system to predict malignancy of additional MRI-detected lesions in breast cancer patients.

Materials and Methods

Eighty-six lesions (64 benign and 22 malignant) detected on preoperative MRI of 68 breast cancer patients were retrospectively included. The clinico-radiologic features were correlated with the histopathologic results using the Student's t-test, Fisher's exact test, and logistic regression analysis. The scoring system was designed based on the significant predictive features of malignancy, and its diagnostic performance was compared with that of the Breast Imaging-Reporting and Data System (BI-RADS) category.

Results

Lesion size ≥ 8 mm (p < 0.001), location in the same quadrant as the primary cancer (p = 0.005), delayed plateau kinetics (p = 0.010), T2 isointense (p = 0.034) and hypointense (p = 0.024) signals, and irregular mass shape (p = 0.028) were associated with malignancy. In comparison with the BI-RADS category, the scoring system based on these features with suspicious non-mass internal enhancement increased the diagnostic performance (area under the receiver operating characteristic curve: 0.918 vs. 0.727) and detected three false-negative cases. With this scoring system, 22 second-look ultrasound examinations (22/66, 33.3%) could have been avoided.

Conclusion

The scoring system based on the lesion size, location relative to the primary cancer, delayed kinetic features, T2 signal intensity, mass shape, and non-mass internal enhancement can provide a more accurate approach to evaluate MRI-detected lesions in breast cancer patients.

Diffusion tensor imaging for characterizing tumor microstructure and improving diagnostic performance on breast MRI: a prospective observational study

Background

Diffusion-weighted imaging (DWI) can increase breast MRI diagnostic specificity due to the tendency of malignancies to restrict diffusion. Diffusion tensor imaging (DTI) provides further information over conventional DWI regarding diffusion directionality and anisotropy. Our study evaluates DTI features of suspicious breast lesions detected on MRI to determine the added diagnostic value of DTI for breast imaging.

Methods

With IRB approval, we prospectively enrolled patients over a 3-year period who had suspicious (BI-RADS category 4 or 5) MRI-detected breast lesions with histopathological results. Patients underwent multiparametric 3 T MRI with dynamic contrast-enhanced (DCE) and DTI sequences. Clinical factors (age, menopausal status, breast density, clinical indication, background parenchymal enhancement) and DCE-MRI lesion parameters (size, type, presence of washout, BI-RADS category) were recorded prospectively by interpreting radiologists. DTI parameters (apparent diffusion coefficient [ADC], fractional anisotropy [FA], axial diffusivity [λ₁], radial diffusivity [(λ₂ + λ₃)/2], and empirical difference [λ₁ − λ₃]) were measured retrospectively. Generalized estimating equations (GEE) and least absolute shrinkage and selection operator (LASSO) methods were used for univariate and multivariate logistic regression, respectively. Diagnostic performance was internally validated using the area under the curve (AUC) with bootstrap adjustment.

Results

The study included 238 suspicious breast lesions (95 malignant, 143 benign) in 194 women. In univariate analysis, lower ADC, axial diffusivity, and radial diffusivity were associated with malignancy (OR = 0.37–0.42 per 1-SD increase, p < 0.001 for each), as was higher FA (OR = 1.45, p = 0.007). In multivariate analysis, LASSO selected only ADC (OR = 0.41) as a predictor for a DTI-only model, while both ADC (OR = 0.41) and FA (OR = 0.88) were selected for a model combining clinical and imaging parameters. Post-hoc analysis revealed varying association of FA with malignancy depending on the lesion type. The combined model (AUC = 0.81) had a significantly better performance than Clinical/DCE-MRI-only (AUC = 0.76, p < 0.001) and DTI-only (AUC = 0.75, p = 0.002) models.

Conclusions

DTI significantly improves diagnostic performance in multivariate modeling. ADC is the most important diffusion parameter for distinguishing benign and malignant breast lesions, while anisotropy measures may help further characterize tumor microstructure and microenvironment.

Utility of Diffusion-weighted Imaging to Decrease Unnecessary Biopsies Prompted by Breast MRI: A Trial of the ECOG-ACRIN Cancer Research Group (A6702)

PURPOSE

Conventional breast MRI is highly sensitive for cancer detection but prompts some false positives. We performed a prospective, multicenter study to determine whether apparent diffusion coefficients (ADCs) from diffusion-weighted imaging (DWI) can decrease MRI false positives.Experimental Design: A total of 107 women with MRI-detected BI-RADS 3, 4, or 5 lesions were enrolled from March 2014 to April 2015. ADCs were measured both centrally and at participating sites. ROC analysis was employed to assess diagnostic performance of centrally measured ADCs and identify optimal ADC thresholds to reduce unnecessary biopsies. Lesion reference standard was based on either definitive biopsy result or at least 337 days of follow-up after the initial MRI procedure.

RESULTS

Of 107 women enrolled, 67 patients (median age 49, range 24-75 years) with 81 lesions with confirmed reference standard (28 malignant, 53 benign) and evaluable DWI were analyzed. Sixty-seven of 81 lesions were BI-RADS 4 (n = 63) or 5 (n = 4) and recommended for biopsy. Malignancies exhibited lower mean in centrally measured ADCs (mm²/s) than benign lesions [1.21 × 10^-3 vs.1.47 × 10^-3; P < 0.0001; area under ROC curve = 0.75; 95% confidence interval (CI) 0.65-0.84]. In centralized analysis, application of an ADC threshold (1.53 × 10^-3 mm²/s) lowered the biopsy rate by 20.9% (14/67; 95% CI, 11.2%-31.2%) without affecting sensitivity. Application of a more conservative threshold (1.68 × 10^-3 mm²/s) to site-measured ADCs reduced the biopsy rate by 26.2% (16/61) but missed three cancers.

CONCLUSIONS

DWI can reclassify a substantial fraction of suspicious breast MRI findings as benign and thereby decrease unnecessary biopsies. ADC thresholds identified in this trial should be validated in future phase III studies.

Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial

Comparative preliminary analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data collected in the International Breast MR Consortium 6883 multicenter trial was performed to distinguish benign and malignant breast tumors. Prebiopsy DCE-MRI data from 45 patients with suspicious breast lesions were obtained. Semiquantitative mean signal-enhancement ratio ([Formula: see text]) was calculated for all lesions, and quantitative pharmacokinetic, parameters [Formula: see text], [Formula: see text], and [Formula: see text], were calculated for the subset with available [Formula: see text] maps ([Formula: see text]). Diagnostic performance was estimated for DCE-MRI parameters and compared to standard clinical MRI assessment. Quantitative and semiquantitative metrics discriminated benign and malignant lesions, with receiver operating characteristic area under the curve (AUC) values of 0.71, 0.70, and 0.82 for [Formula: see text], [Formula: see text], and [Formula: see text], respectively ([Formula: see text]). At equal 94% sensitivity, the specificity and positive predictive value of [Formula: see text] (53% and 63%, respectively) and K^trans (42% and 58%) were higher than clinical MRI assessment (32% and 54%). A multivariable model combining [Formula: see text] and clinical MRI assessment had an AUC value of 0.87. Quantitative pharmacokinetic and semiquantitative analyses of DCE-MRI improves discrimination of benign and malignant breast tumors, with our findings suggesting higher diagnostic accuracy using [Formula: see text]. [Formula: see text] has potential to help reduce unnecessary biopsies resulting from routine breast imaging.

A simple scoring system for breast MRI interpretation: does it compensate for reader experience?

PURPOSE

To investigate the impact of a scoring system (Tree) on inter-reader agreement and diagnostic performance in breast MRI reading.

MATERIALS AND METHODS

This IRB-approved, single-centre study included 100 patients with 121 consecutive histopathologically verified lesions (52 malignant, 68 benign). Four breast radiologists with different levels of MRI experience and blinded to histopathology retrospectively evaluated all examinations. Readers independently applied two methods to classify breast lesions: BI-RADS and Tree. BI-RADS provides a reporting lexicon that is empirically translated into likelihoods of malignancy; Tree is a scoring system that results in a diagnostic category. Readings were compared by ROC analysis and kappa statistics.

RESULTS

Inter-reader agreement was substantial to almost perfect (kappa: 0.643-0.896) for Tree and moderate (kappa: 0.455-0.657) for BI-RADS. Diagnostic performance using Tree (AUC: 0.889-0.943) was similar to BI-RADS (AUC: 0.872-0.953). Less experienced radiologists achieved AUC: improvements up to 4.7 % using Tree (P-values: 0.042-0.698); an expert's performance did not change (P = 0.526). The least experienced reader improved in specificity using Tree (16 %, P = 0.001). No further sensitivity and specificity differences were found (P > 0.1).

CONCLUSION

The Tree scoring system improves inter-reader agreement and achieves a diagnostic performance similar to that of BI-RADS. Less experienced radiologists, in particular, benefit from Tree.

KEY POINTS

• The Tree scoring system shows high diagnostic accuracy in mass and non-mass lesions. • The Tree scoring system reduces inter-reader variability related to reader experience. • The Tree scoring system improves diagnostic accuracy in non-expert readers.

Improved T1, contrast concentration, and pharmacokinetic parameter quantification in the presence of fat with two-point Dixon for dynamic contrast-enhanced magnetic resonance imaging

PURPOSE

To evaluate the impact of fat and fat-suppression on the quantification of T1, gadolinium concentration, and pharmacokinetic parameters in DCE-MRI.

METHODS

T1 values were measured in fat-free phantoms using variable flip angle with no fat suppression, quick or interleaved fat saturation (QFS), or two-point Dixon and were compared with reference values measured with inversion recovery-prepared turbo spin echo. Relaxivity of gadolinium-benzyloxypropionictetraacetate (Gd-BOPTA) was measured in emulsions of Gd-BOPTA solution and fat using Dixon in-phase and water-only images. Liver T1 and pharmacokinetic parameters of 15 patients were calculated from Dixon in-phase and water-only images and were correlated with liver fat signal fraction.

RESULTS

T1 values measured using Dixon water-only and non-fat-suppressed images matched the reference values; while T1 values measured using QFS showed large deviations. Relaxivities and Gd measured in the Dixon water-only images were less affected by the fat than those measured in the in-phase images. The correlation between liver fat fraction and the differences in measured pharmacokinetic parameters using Dixon in-phase and water-only images were significant (P < 0.05) for T1, K(trans), and incremental area under the curve, but not Ve (P = 0.1).

CONCLUSION

Dixon water-only images provided more reliable estimation of T1, Gd, and pharmacokinetic parameters when fat was present.

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

RATIONALE AND OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

MRI-guided breast needle core biopsies: pathologic features of newly diagnosed malignancies

Magnetic resonance imaging (MRI) of the breast is used for select groups of patients. MRI-guided breast core needle biopsies performed over a 3-year period were retrospectively reviewed to determine the incidence and types of cancers found and to correlate the cancers with the MRI findings and the indication for the study. Patients were stratified based on indication for MRI examination including, evaluation of disease extent in patients with current ipsilateral carcinoma, surveillance for recurrence of prior ipsilateral carcinoma, as a problem-solving method and for screening high-risk patients. The high-risk screening group included those with family history (with or without germline mutations), prior chest wall radiation, and contralateral breast carcinoma (current or prior). Four-hundred and forty-five biopsies were performed on 386 patients. The majority of biopsies (79%) were benign. Biopsies demonstrating ductal carcinoma in situ (DCIS) and invasive carcinoma were more likely to present as nonmass-like and mass-forming enhancements respectively, but with only 52% specificity. The highest rate of malignancy (44%) was seen in the least frequently biopsied patient group (n = 25), those with prior ipsilateral carcinoma. Conversely, the most frequently biopsied group (n = 283), the high-risk screening group, demonstrated the lowest malignancy rate (16%). Within this group, most malignant cases were invasive carcinomas (n = 27), 67% of which were small (≤1 cm), well or moderately differentiated with a good prognostic receptor profile (estrogen receptor positive, human epidermal growth factor receptor 2 negative), and lacked nodal macrometastases. The remaining malignant cases in the high-risk screening group were DCIS with or without microinvasion (n = 18), 78% of which demonstrated high nuclear grade. Overall, enhancement pattern did not correlate with the likelihood of or type of malignancy. The most common types of carcinomas identified by screening were small estrogen receptor positive invasive tumors and high grade DCIS.

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.

Utility of targeted sonography in management of probably benign breast lesions identified on magnetic resonance imaging

OBJECTIVES

The purpose of this study was to determine the utility of targeted sonography in the management of probably benign breast lesions detected on magnetic resonance imaging (MRI).

METHODS

A total of 4370 consecutive contrast-enhanced breast MRI examinations from March 1, 2004, to March 1, 2009, were retrospectively reviewed. The study was Health Insurance Portability and Accountability Act compliant and Institutional Review Board approved. When targeted sonography was recommended for a Breast Imaging Reporting and Data System (BI-RADS) category 3 examination, results of the sonography and any subsequent breast pathologic examinations were recorded. The frequency of identifying the MRI-detected lesions and the rate at which the BI-RADS category was changed by sonography were calculated for mass and non-mass-like lesions.

RESULTS

Of the 4370 examinations, 349 (8%) had BI-RADS 3 findings in 346 patients. One hundred eighteen lesions underwent targeted sonography for evaluation of 85 masses and 33 areas of non-mass-like enhancement. Of these 118 lesions, 54 (46%) were seen on sonography. No cancers were detected on sonography in the areas of non-mass-like enhancement. Two of the 85 masses (2.4%) evaluated with targeted sonography had a malignant diagnosis before initiation of follow-up.

CONCLUSIONS

Selective use of targeted sonography, particularly in masses, may help identify some malignancies before initiating short-interval follow-up for MRI-detected BI-RADS 3 lesions.