DWI shows potential for improving the PPV of breast MRI for lesions of varied types and sizes. However, considerable overlap in ADC of benign and malignant lesions necessitates validation of these findings in larger studies.

Diffusion-weighted MRI shows promise in differentiation of benign and malignant masses and lesions with nonmasslike enhancement found at breast MRI and is not affected by lesion size. However, ADC measurements may be more useful for discriminating masses than for discriminating lesions with nonmasslike enhancement.

Purpose: To investigate the diagnostic performance of diffusion-weighted imaging (DWI) for mammographically and clinically occult breast lesions. Materials and Methods:The study included 91 women with 118 breast lesions (91 benign, 12 ductal carcinoma in situ [DCIS], 15 invasive carcinoma) initially detected on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and assigned BI-RADS category 3, 4, or 5. DWI was acquired with b ¼ 0 and 600 s/mm 2 . Lesion visibility was assessed on DWI. Apparent diffusion coefficient (ADC) values were compared between malignancies, benign lesions, and normal (no abnormal enhancement on DCE-MRI) breast tissue, and the diagnostic performance of DWI was assessed based on ADC thresholding.Results: Twenty-four of 27 (89%) malignant and 74/91 (81%) benign lesions were hyperintense on the b ¼ 600 s/ mm 2 diffusion-weighted images. Both DCIS (1.33 6 0.19 Â 10 À3 mm 2 /s) and invasive carcinomas (1.30 6 0.27 Â 10 À3 mm 2 /s) were lower in ADC than benign lesions (1.71 6 0.43 Â 10 À3 mm 2 /s; P < 0.001), and each lesion type was lower in ADC than normal tissue (1.90 6 0.38 Â 10 À3 mm 2 /s, P 0.001). Receiver operating curve (ROC) analysis showed an area under the curve (AUC) of 0.77, and sensitivity ¼ 96%, specificity ¼ 55%, positive predictive value (PPV) ¼ 39%, and negative predictive value (NPV) ¼ 98% for an ADC threshold of 1.60 Â 10 À3 mm 2 /s. MAGNETIC RESONANCE IMAGING (MRI) is playing a growing role in breast cancer detection, particularly for screening patients at high risk for cancer (1) and evaluating the extent of disease in patients with a recent diagnosis of cancer (2). Breast MRI currently relies on the differential enhancement between normal and malignant tissue on T1-weighted dynamic contrast-enhanced MRI (DCE-MRI) sequences. However, the requirement for intravenous administration of a gadolinium-based contrast agent increases the associated time, costs, and potential toxicity of the breast DCE-MRI examination. These factors limit the accessibility of this screening tool for many women. ConclusionDiffusion-weighted imaging (DWI) is a noncontrast MRI technique that has potential as an alternative breast imaging method without the risks and costs associated with DCE-MRI. DWI measures the mobility of water molecules in vivo and reflects characteristics of the microscopic cellular environment, including cell density, cell organization, and membrane integrity (3). DWI is an established diagnostic tool in neuroimaging, but application to other areas of the body has been challenging due to technical limitations. More recent advances in MR technology have facilitated the application of DWI for detecting and characterizing a variety of carcinomas outside of the brain (4-8). Preliminary DWI studies of the breast reported high sensitivity for detecting cancer, based on low diffusivity in carcinomas due to higher cell density (9-11). Furthermore, quantitative DWI analyses have shown that the apparent diffusion coefficient (ADC) is significantly lower in many breast carcinom...

Combinations of BI-RADS lesion descriptors can predict the probability of malignancy for breast MRI masses but not for NMLE. If our model is validated, masses with a low probability of malignancy may be eligible for short-interval follow-up rather than biopsy. Further research focused on predictive features of NMLE is needed.

Purpose:To evaluate the positive predictive values (PPVs) of Breast Imaging and Reporting Data Systems (BI-RADS) assessment categories for breast magnetic resonance (MR) imaging and to identify the BI-RADS MR imaging lesion features most predictive of cancer. Materials and Methods:This institutional review board-approved HIPAA-compliant prospective multicenter study was performed with written informed consent. Breast MR imaging studies of the contralateral breast in women with a recent diagnosis of breast cancer were prospectively evaluated. Contralateral breast MR imaging BI-RADS assessment categories, morphologic descriptors for foci, masses, non-masslike enhancement (NMLE), and kinetic features were assessed for predictive values for malignancy. PPV of each imaging characteristic of interest was estimated, and logistic regression analysis was used to examine the predictive ability of combinations of characteristics. Results:Of 969 participants, 71.3% had a BI-RADS category 1 or 2 assessment; 10.9%, a BI-RADS category 3 assessment; 10.0%, a BI-RADS category 4 or 5 assessment; and 7.7%, a BI-RADS category 0 assessment on the basis of initial MR images. Thirty-one cancers were detected with MR imaging. Overall PPV for BI-RADS category 4 and 5 lesions was 0.278, with 17 cancers in patients with a BI-RADS category 4 lesion (PPV, 0.205) and 10 cancers in patients with a BI-RADS category 5 lesion (PPV, 0.714). Of the cancers, one was a focus, 17 were masses, and 13 were NMLEs. For masses, irregular shape, irregular margins, spiculated margins, and marked internal enhancement were most predictive of malignancy. For NMLEs, ductal, clumped, and reticular or dendritic enhancement were the features most frequently seen with malignancy. Kinetic enhancement features were less predictive of malignancy than were morphologic features. Conclusion:Standardized terminology of the BI-RADS lexicon enables quantification of the likelihood of malignancy for MR imaging-detected lesions through careful evaluation of lesion features. In particular, BI-RADS assessment categories and morphologic descriptors for masses and NMLE were useful in estimating the probability of cancer.q RSNA, 2012

Purpose: To investigate whether diffusion tensor imaging (DTI) measures of anisotropy in breast tumors are different from normal breast tissue and can improve the discrimination between benign and malignant lesions. Materials and Methods:The study included 81 women with 105 breast lesions (76 malignant, 29 benign). DTI was performed during breast MRI examinations, and fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were measured for breast lesions and normal tissue in each subject. FA and ADC were compared between cancers, benign lesions, and normal tissue by univariate and multivariate analyses.Results: The FA of carcinomas (mean 6 SD: 0.24 6 0.07) was significantly lower than normal breast tissue in the same subjects (0.29 6 0.07; P < 0.0001). Multiple logistic regression showed that FA and ADC were each independent discriminators of malignancy (P < 0.0001), and that FA improved discrimination between cancer and normal tissue over ADC alone. However, there was no difference in FA between malignant and benign lesions (P ¼ 0.98).Conclusion: Diffusion anisotropy is significantly lower in breast cancers than normal tissue, which may reflect alterations in tissue organization. Our preliminary results suggest that FA adds incremental value over ADC alone for discriminating malignant from normal tissue but does not help with distinguishing benign from malignant lesions.

Increased background parenchymal enhancement on breast MRI is associated with younger patient age and higher abnormal interpretation rate. However, it is not related to significant differences in positive biopsy rate, cancer yield, sensitivity, or specificity of MRI.

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