Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment.

OBJECTIVE. The objective of this study was to assess the impact of artificial intelligence (AI)-based decision support (DS) on breast ultrasound (US) lesion assessment. MATERIALS AND METHODS. A multicenter retrospective review of 900 breast lesions (470/900 [52.2%] benign; 430/900 [47.8%] malignant) on US by 15 physicians (11 radiologists, two surgeons, two obstetrician/gynecologists). An AI system (Koios DS for Breast, Koios Medical) evaluated images and assigned them to one of four categories: benign, probably benign, suspicious, and probably malignant. Each reader reviewed cases twice: 750 cases with US only or with US plus DS; 4 weeks later, cases were reviewed in the opposite format. One hundred fifty additional cases were presented identically in each session. DS and reader sensitivity, specificity, and positive likelihood ratios (PLRs) were calculated as well as reader AUCs with and without DS. The Kendall τ-b correlation coefficient was used to assess intraand interreader variability. RESULTS. Mean reader AUC for cases reviewed with US only was 0.83 (95% CI, 0.78-0.89); for cases reviewed with US plus DS, mean AUC was 0.87 (95% CI, 0.84-0.90). PLR for the DS system was 1.98 (95% CI, 1.78-2.18) and was higher than the PLR for all readers but one. Fourteen readers had better AUC with US plus DS than with US only. Mean Kendall τ-b for US-only interreader variability was 0.54 (95% CI, 0.53-0.55); for US plus DS, it was 0.68 (95% CI, 0.67-0.69). Intrareader variability improved with DS; class switching (defined as crossing from BI-RADS category 3 to BI-RADS category 4A or above) occurred in 13.6% of cases with US only versus 10.8% of cases with US plus DS (p = 0.04). CONCLUSION. AI-based DS improves accuracy of sonographic breast lesion assessment while reducing inter- and intraobserver variability.

In the Setting of Negative Mammogram, Is Additional Breast Ultrasound Necessary for Evaluation of Breast Pain?

PURPOSE: To evaluate whether in the setting of negative diagnostic mammogram for breast pain additional ultrasound is necessary. METHODS: Retrospective IRB-approved review of our database identified 8085 women who underwent ultrasound evaluation for breast pain from 1/1/2013-12/31/2013. Of 8085 women, 559 women had mammogram evaluation preceding the ultrasound and these women comprise the basis of this study. The patient's age, type of mammogram examination (screening or diagnostic), Breast Imaging-Reporting and Data System (BI-RADS) breast density (BD), type of breast pain (focal, diffuse, cyclical, unilateral, bilateral), additional breast symptoms (palpable concern, nipple discharge, skin changes, others), mammogram or ultrasound findings and final BI-RADS assessment, follow-up imaging, and follow-up biopsy results were reviewed and recorded. RESULTS: The median age of patients was 46 years old (range: 27-97). Patients recalled from negative screening mammogram were 29.8% (167/559). Patients with preceding negative diagnostic mammogram were 70.2% (392/559). The BI-RADS BD distribution was BD1: 5.5%, BD2: 39.9%, BD3: 46.0%, BD4: 8.6%. Final BI-RADS assessments were BI-RADS 1/2 (79%), BI-RADS 3 (12.9%), BI-RADS 4 (8.1%), BI-RADS 5 (0%). Majority (66.9%, 374/559) of the patient had breast pain alone. Additional breast symptoms were also noted as follows: palpable concern (24%), nipple discharge (3.9%), skin changes or other (5.2%). On follow-up evaluation, 26 findings were recommended for tissue sampling yielding 2 malignancies (0.4%, 2/559) in 2 patients. In the setting of negative mammogram and clinical symptom of breast pain alone yielded no malignances (NPV, 100%, 374/374) and was not impacted by BD. In patients with additional symptoms accompanying pain, malignancies were present despite negative mammogram in 2 patients; nipple discharge (4.5%, 1/22), and palpable concern (0.7%, 1/134). CONCLUSION: In the setting of negative mammogram and breast pain alone, additional evaluation with ultrasound is likely low yield and may be unnecessary. However, with additional symptoms such as palpable concern or nipple discharge, ultrasound is likely an important adjunct modality for identifying mammographically occult tumors.

Are Mammographically Occult Additional Tumors Identified More Than 2 Cm Away From the Primary Breast Cancer on MRI Clinically Significant?

RATIONALE AND OBJECTIVES: To evaluate the clinical significance of mammographically occult additional tumors identified more than 2cm away from the primary breast cancer on preoperative magnetic resonance imaging (MRI). MATERIALS AND METHODS: An Institutional Review Board approved review of consecutive preoperative breast MRIs performed from 1/1/08 to 12/31/14, yielded 667 patients with breast cancer. These patients underwent further assessment to identify biopsy proven mammographically occult breast tumors located more than 2cm away from the edge of the primary tumor. Additional MRI characteristics of the primary and secondary tumors and pathology were reviewed. Statistical analysis was performed using SPSS (v. 24). RESULTS: Of 667 patients with breast cancer, 129 patients had 150 additional ipsilateral mammographically occult tumors that were more than 2cm away from the edge of the primary tumor. One hundred twelve of 129 (86.8%) patients had one additional tumor and 17/129 (13.2%) had two or more additional tumors. In 71/129 (55.0%), additional tumors were located in a different quadrant and in 58/129 (45.0%) additional tumors were in the same quadrant but ≥2cm away. Overall, primary tumor size was significantly larger (mean 1.87± 1.25 cm) than the additional tumors (mean 0.79 ± 0.61cm, p < 0.001). However, in 20/129 (15.5%) the additional tumor was larger and in 26/129 (20.2%) the additional tumor was ≥1cm. The primary tumor was significantly more likely to be invasive (81.4%, 105/129) compared to additional tumors (70%, 105/150, p = 0.03). In 9/129 (7.0%) patients, additional tumors yielded unsuspected invasive cancer orhigher tumor grade. The additional tumor was more likely to be nonmass lesion type (37.3% vs 24% p = 0.02) and focus lesion type (10% vs 0.08%, p < 0.001) compared to primary tumor. CONCLUSION: Mammographically occult additional tumors identified more than 2cm away from the primary breast tumor on MRI are unlikely to be surgically treated if undiagnosed and may be clinically significant.

Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement.

The aim of this study is to develop a fully automated convolutional neural network (CNN) method for quantification of breast MRI fibroglandular tissue (FGT) and background parenchymal enhancement (BPE). An institutional review board-approved retrospective study evaluated 1114 breast volumes in 137 patients using T1 precontrast, T1 postcontrast, and T1 subtraction images. First, using our previously published method of quantification, we manually segmented and calculated the amount of FGT and BPE to establish ground truth parameters. Then, a novel 3D CNN modified from the standard 2D U-Net architecture was developed and implemented for voxel-wise prediction whole breast and FGT margins. In the collapsing arm of the network, a series of 3D convolutional filters of size 3 × 3 × 3 are applied for standard CNN hierarchical feature extraction. To reduce feature map dimensionality, a 3 × 3 × 3 convolutional filter with stride 2 in all directions is applied; a total of 4 such operations are used. In the expanding arm of the network, a series of convolutional transpose filters of size 3 × 3 × 3 are used to up-sample each intermediate layer. To synthesize features at multiple resolutions, connections are introduced between the collapsing and expanding arms of the network. L2 regularization was implemented to prevent over-fitting. Cases were separated into training (80%) and test sets (20%). Fivefold cross-validation was performed. Software code was written in Python using the TensorFlow module on a Linux workstation with NVIDIA GTX Titan X GPU. In the test set, the fully automated CNN method for quantifying the amount of FGT yielded accuracy of 0.813 (cross-validation Dice score coefficient) and Pearson correlation of 0.975. For quantifying the amount of BPE, the CNN method yielded accuracy of 0.829 and Pearson correlation of 0.955. Our CNN network was able to quantify FGT and BPE within an average of 0.42 s per MRI case. A fully automated CNN method can be utilized to quantify MRI FGT and BPE. Larger dataset will likely improve our model.

Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset.

We hypothesize that convolutional neural networks (CNN) can be used to predict neoadjuvant chemotherapy (NAC) response using a breast MRI tumor dataset prior to initiation of chemotherapy. An institutional review board-approved retrospective review of our database from January 2009 to June 2016 identified 141 locally advanced breast cancer patients who (1) underwent breast MRI prior to the initiation of NAC, (2) successfully completed adriamycin/taxane-based NAC, and (3) underwent surgical resection with available final surgical pathology data. Patients were classified into three groups based on their NAC response confirmed on final surgical pathology: complete (group 1), partial (group 2), and no response/progression (group 3). A total of 3107 volumetric slices of 141 tumors were evaluated. Breast tumor was identified on first T1 postcontrast dynamic images and underwent 3D segmentation. CNN consisted of ten convolutional layers, four max-pooling layers, and dropout of 50% after a fully connected layer. Dropout, augmentation, and L2 regularization were implemented to prevent overfitting of data. Non-linear functions were modeled by a rectified linear unit (ReLU). Batch normalization was used between the convolutional and ReLU layers to limit drift of layer activations during training. A three-class neoadjuvant prediction model was evaluated (group 1, group 2, or group 3). The CNN achieved an overall accuracy of 88% in three-class prediction of neoadjuvant treatment response. Three-class prediction discriminating one group from the other two was analyzed. Group 1 had a specificity of 95.1% ± 3.1%, sensitivity of 73.9% ± 4.5%, and accuracy of 87.7% ± 0.6%. Group 2 (partial response) had a specificity of 91.6% ± 1.3%, sensitivity of 82.4% ± 2.7%, and accuracy of 87.7% ± 0.6%. Group 3 (no response/progression) had a specificity of 93.4% ± 2.9%, sensitivity of 76.8% ± 5.7%, and accuracy of 87.8% ± 0.6%. It is feasible for current deep CNN architectures to be trained to predict NAC treatment response using a breast MRI dataset obtained prior to initiation of chemotherapy. Larger dataset will likely improve our prediction model.

Evaluation of association between degree of background parenchymal enhancement on MRI and breast cancer subtype.

PURPOSE: Evaluate possible association between BPE and breast cancer tumor type/prognostic markers. METHODS: IRB approved retrospective study from 1/2010-1/2014 identified 328 patients who had breast MRI and available clinical/pathology data. BPE was categorized according to BI-RADS. The association between BPE and breast cancer molecular subtype/prognostic factors was evaluated. RESULTS: No significant association was present between high BPE and the following: HER2+ tumors, basal tumors, tumors with axillary nodal disease, high nuclear grade tumors, high Ki-67 index tumors or larger tumors. CONCLUSION: Patients with high BPE may be at increased risk for breast cancer but not necessarily for those cancer subtypes with a poor prognosis.

Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset.

The aim of this study is to evaluate the role of convolutional neural network (CNN) in predicting axillary lymph node metastasis, using a breast MRI dataset. An institutional review board (IRB)-approved retrospective review of our database from 1/2013 to 6/2016 identified 275 axillary lymph nodes for this study. Biopsy-proven 133 metastatic axillary lymph nodes and 142 negative control lymph nodes were identified based on benign biopsies (100) and from healthy MRI screening patients (42) with at least 3 years of negative follow-up. For each breast MRI, axillary lymph node was identified on first T1 post contrast dynamic images and underwent 3D segmentation using an open source software platform 3D Slicer. A 32 × 32 patch was then extracted from the center slice of the segmented tumor data. A CNN was designed for lymph node prediction based on each of these cropped images. The CNN consisted of seven convolutional layers and max-pooling layers with 50% dropout applied in the linear layer. In addition, data augmentation and L2 regularization were performed to limit overfitting. Training was implemented using the Adam optimizer, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Code for this study was written in Python using the TensorFlow module (1.0.0). Experiments and CNN training were done on a Linux workstation with NVIDIA GTX 1070 Pascal GPU. Two class axillary lymph node metastasis prediction models were evaluated. For each lymph node, a final softmax score threshold of 0.5 was used for classification. Based on this, CNN achieved a mean five-fold cross-validation accuracy of 84.3%. It is feasible for current deep CNN architectures to be trained to predict likelihood of axillary lymph node metastasis. Larger dataset will likely improve our prediction model and can potentially be a non-invasive alternative to core needle biopsy and even sentinel lymph node evaluation.

The unusual suspects: A review of unusual benign and malignant male breast imaging cases.

Male breast disease is uncommon. Men presenting with breast symptoms may represent unique diagnostic challenges for the radiologist, particularly if imaging findings are not classic for gynecomastia or carcinoma. In this paper we review 10 unusual male breast cases, 5 benign and 5 malignant, including the radiologic findings, differential diagnosis, pathology and management.

Reflector-guided breast tumor localization versus wire localization for lumpectomies: A comparison of surgical outcomes.

PURPOSE: To compare surgical outcomes of SAVI SCOUT reflector localization (SSL) versus wire localization (WL) for breast tumors. METHODS: Retrospective review of 42 SSL cases and 42 WL cases. WL patients were consecutively matched for clinical-pathologic features. Final surgical outcome measures were tumor specimen volume, margin status, and re-excision rates. RESULTS: No significant differences were present in median specimen volumes (SSL-15.2cm3 vs. WL-16.3cm3), positive margin rate (SSL-9.5% vs. WL-7.1%), close margin rate (SSL-7.1% vs. WL-11.9%) or re-excision rate (SSL-7.1% vs. WL-9.5%). CONCLUSION: SSL is an acceptable alternative to WL with no significant differences in surgical outcomes.

Utilization of multiple SAVI SCOUT surgical guidance system reflectors in the same breast: A single-institution feasibility study.

SAVI SCOUT Surgical Guidance System has been shown to be a reliable and safe alternative to wire localization in breast surgery. This study evaluated the feasibility of using multiple reflectors in the same breast. We performed an IRB-approved, HIPAA-compliant, single-institution retrospective review of 183 patients who underwent breast lesion localization and excision using SAVI SCOUT Surgical Guidance System (Cianna Medical) between June 2015 and January 2017. We performed a subset analysis in 42 patients in whom more than one reflector was placed. Specimen radiography, pathology, distance between reflectors, target removal, margin positivity, and complications were evaluated. Among 183 patients, 42 patients had more than one reflector placed in the same breast to localize 68 lesions. Benign (n = 6, 8.8%), high-risk (n = 23, 33.8%), and malignant (n = 39, 57.4%) lesions were included. Thirty-six patients (85.7%) had a total of 2 reflectors placed and 6 patients had a total of 3 reflectors placed (14.3%). The indications for multiple reflector placement in the same breast included multiple separate lesions (n = 23) and bracketing of large lesions (n = 19). The mean distance between the reflectors was 42 mm (22-93 mm). All lesions were successfully targeted and retrieved. Of 39 malignant lesions, 10.3% (n = 4) had positive margins and 10.3% (n = 4) had close (<1 mm) margins at surgery. All patients with positive margins underwent re-excision. No complications occurred preoperatively, intra-operatively, or postoperatively. The use of multiple SAVI SCOUT reflectors for localizing multiple lesions in the same breast or bracketing large lesions is feasible and safe.

Does breast MRI background parenchymal enhancement indicate metabolic activity? Qualitative and 3D quantitative computer imaging analysis.

PURPOSE: To investigate whether the degree of breast magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) is associated with the amount of breast metabolic activity measured by breast parenchymal uptake (BPU) of 18F-FDG on positron emission tomography / computed tomography (PET/CT). MATERIALS AND METHODS: An Institutional Review Board (IRB)-approved retrospective study was performed. Of 327 patients who underwent preoperative breast MRI from 1/1/12 to 12/31/15, 73 patients had 18F-FDG PET/CT evaluation performed within 1 week of breast MRI and no suspicious findings in the contralateral breast. MRI was performed on a 1.5T or 3.0T system. The imaging sequence included a triplane localizing sequence followed by sagittal fat-suppressed T2 -weighted sequence, and a bilateral sagittal T1 -weighted fat-suppressed fast spoiled gradient-echo sequence, which was performed before and three times after a rapid bolus injection (gadobenate dimeglumine, Multihance; Bracco Imaging; 0.1 mmol/kg) delivered through an IV catheter. The unaffected contralateral breast in these 73 patients underwent BPE and BPU assessments. For PET/CT BPU calculation, a 3D region of interest (ROI) was drawn around the glandular breast tissue and the maximum standardized uptake value (SUVmax ) was determined. Qualitative MRI BPE assessments were performed on a 4-point scale, in accordance with BI-RADS categories. Additional 3D quantitative MRI BPE analysis was performed using a previously published in-house technique. Spearman's correlation test and linear regression analysis was performed (SPSS, v. 24). RESULT: The median time interval between breast MRI and 18F-FDG PET/CT evaluation was 3 days (range, 0-6 days). BPU SUVmax mean value was 1.6 (SD, 0.53). Minimum and maximum BPU SUVmax values were 0.71 and 4.0. The BPU SUVmax values significantly correlated with both the qualitative and quantitative measurements of BPE, respectively (r(71) = 0.59, P < 0.001 and r(71) = 0.54, P < 0.001). Qualitatively assessed high BPE group (BI-RADS 3/4) had significantly higher BPU SUVmax of 1.9 (SD = 0.44) compared to low BPE group (BI-RADS 1/2) with an average BPU SUVmax of 1.17 (SD = 0.32) (P < 0.001). On linear regression analysis, BPU SUVmax significantly predicted qualitative and quantitative measurements of BPE (ß = 1.29, t(71) = 3.88, P < 0.001 and ß = 19.52, t(71) = 3.88, P < 0.001). CONCLUSION: There is a significant association between breast BPU and BPE, measured both qualitatively and quantitatively. Increased breast cancer risk in patients with high MRI BPE could be due to elevated basal metabolic activity of the normal breast tissue, which may provide a susceptible environment for tumor growth. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:753-759.

Beyond Wires and Seeds: Reflector-guided Breast Lesion Localization and Excision.

Purpose To evaluate outcomes of Savi Scout (Cianna Medical, Aliso Viejo, Calif) reflector-guided localization and excision of breast lesions by analyzing reflector placement, localization, and removal, along with target excision and rates of repeat excision (referred to as re-excision). Materials and Methods A single-institution retrospective review of 100 women who underwent breast lesion localization and excision by using the Savi Scout surgical guidance system from June 2015 to May 2016 was performed. By using image guidance 0-8 days before surgery, 123 nonradioactive, infrared-activated, electromagnetic wave reflectors were percutaneously inserted adjacent to or within 111 breast targets. Twenty patients had two or three reflectors placed for bracketing or for localizing multiple lesions, and when ipsilateral, they were placed as close as 2.6 cm apart. Target and reflector were localized intraoperatively by one of two breast surgeons who used a handpiece that emitted infrared light and electromagnetic waves. Radiographs of the specimen and pathologic analysis helped verify target and reflector removal. Target to reflector distance was measured on the mammogram and radiograph of the specimen, and reflector depth was measured on the mammogram. Pathologic analysis was reviewed. Re-excision rates and complications were recorded. By using statistics software, descriptive statistics were generated with 95% confidence intervals (CIs) calculated. Results By using sonographic (40 of 123; 32.5%; 95% CI: 24.9%, 41.2%) or mammographic (83 of 123; 67.5%; 95% CI: 58.8% 75.1%) guidance, 123 (100%; 95% CI: 96.4%, 100%) reflectors were placed. Mean mammographic target to reflector distance was 0.3 cm. All 123 (100%; 95% CI: 96.4%, 100%) targets and reflectors were excised. Pathologic analysis yielded 54 of 110 malignancies (49.1%; 95% CI: 39.9%, 58.3%; average, 1.0 cm; range, 0.1-5 cm), 32 high-risk lesions (29.1%; 95% CI: 21.4%, 38.2%), and 24 benign lesions (21.8%; 95% CI: 115.1%, 30.4%). Four of 54 malignant cases (7.4%; 95% CI: 2.4%, 18.1%) demonstrated margins positive for cancer that required re-excision. Five of 110 radiographs of the specimen (4.5%; 95% CI: 1.7%, 10.4%) demonstrated increased distance between the target and reflector distance of greater than 1.0 cm (range, 1.1-2.6 cm) compared with postprocedure mammogram the day of placement, three of five were associated with hematomas, two of five migrated without identifiable cause. No related postoperative complications were identified. Conclusion Savi Scout is an accurate, reliable method to localize and excise breast lesions with acceptable margin positivity and re-excision rates. Bracketing is possible with reflectors as close as 2.6 cm. Savi Scout overcomes many limitations of other localization methods, which warrants further study. © RSNA, 2017.

Evaluation of the SAVI SCOUT Surgical Guidance System for Localization and Excision of Nonpalpable Breast Lesions: A Feasibility Study.

OBJECTIVE: The purpose of this study is to evaluate the feasibility of the SAVI SCOUT surgical guidance system, which uses a nonradioactive infrared-activated electromagnetic wave reflector, to localize and excise nonpalpable breast lesions. We evaluated the system's use in 15 nonpalpable breast lesions in 13 patients. CONCLUSION: Image-guided placement was successful for 15 of 15 (100%) reflectors. The final pathologic analysis found that lesion excision was successful, including five malignancies with negative margins. No patients required reexcision or experienced complications. SAVI SCOUT is a feasible method for breast lesion localization and excision.

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study.

BACKGROUND: The amount of fibroglandular tissue (FGT) has been linked to breast cancer risk based on mammographic density studies. Currently, the qualitative assessment of FGT on mammogram (MG) and magnetic resonance imaging (MRI) is prone to intra and inter-observer variability. The purpose of this study is to develop an objective quantitative FGT measurement tool for breast MRI that could provide significant clinical value. METHODS: An IRB approved study was performed. Sixty breast MRI cases with qualitative assessment of mammographic breast density and MRI FGT were randomly selected for quantitative analysis from routine breast MRIs performed at our institution from 1/2013 to 12/2014. Blinded to the qualitative data, whole breast and FGT contours were delineated on T1-weighted pre contrast sagittal images using an in-house, proprietary segmentation algorithm which combines the region-based active contours and a level set approach. FGT (%) was calculated by: [segmented volume of FGT (mm(3))/(segmented volume of whole breast (mm(3))] ×100. Statistical correlation analysis was performed between quantified FGT (%) on MRI and qualitative assessments of mammographic breast density and MRI FGT. RESULTS: There was a significant positive correlation between quantitative MRI FGT assessment and qualitative MRI FGT (r=0.809, n=60, P<0.001) and mammographic density assessment (r=0.805, n=60, P<0.001). There was a significant correlation between qualitative MRI FGT assessment and mammographic density assessment (r=0.725, n=60, P<0.001). The four qualitative assessment categories of FGT correlated with the calculated mean quantitative FGT (%) of 4.61% (95% CI, 0-12.3%), 8.74% (7.3-10.2%), 18.1% (15.1-21.1%), 37.4% (29.5-45.3%). CONCLUSIONS: Quantitative measures of FGT (%) were computed with data derived from breast MRI and correlated significantly with conventional qualitative assessments. This quantitative technique may prove to be a valuable tool in clinical use by providing computer generated standardized measurements with limited intra or inter-observer variability.

Three-Dimensional Quantitative Validation of Breast Magnetic Resonance Imaging Background Parenchymal Enhancement Assessments.

The magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) and its clinical significance as a biomarker of breast cancer risk has been proposed based on qualitative studies. Previous BPE quantification studies lack appropriate correlation with BPE qualitative assessments. The purpose of this study is to validate our three-dimensional BPE quantification method with standardized BPE qualitative cases. An Institutional Review Board-approved study reviewed 500 consecutive magnetic resonance imaging cases (from January 2013-December 2014) using a strict inclusion criteria and 120 cases that best represented each of the BPE qualitative categories (minimal or mild or moderate or marked) were selected. Blinded to the qualitative data, fibroglandular tissue contours of precontrast and postcontrast images were delineated using an in-house, proprietary segmentation algorithm. Metrics of BPE were calculated including %BPE ([ratio of BPE volume to fibroglandular tissue volume] × 100) at multiple threshold levels to determine the optimal cutoff point for BPE quantification that best correlated with the reference BPE qualitative cases. The highest positive correlation was present at ×1.5 precontrast average signal intensity threshold level (r = 0.84, P < 0.001). At this level, the BPE qualitative assessment of minimal, mild, moderate, and marked correlated with the mean quantitative %BPE of 14.1% (95% CI: 10.9-17.2), 26.1% (95% CI: 22.8-29.3), 45.9% (95% CI: 40.2-51.7), and 74.0% (95% CI: 68.6-79.5), respectively. A one-way analysis of variance with post-hoc analysis showed that at ×1.5 precontrast average signal intensity level, the quantitative %BPE measurements best differentiated the four reference BPE qualitative groups (F [3,117] = 106.8, P < 0.001). Our three-dimensional BPE quantification methodology was validated using the reference BPE qualitative cases and could become an invaluable clinical tool to more accurately assess breast cancer risk and to test chemoprevention strategies.

Artifacts in Breast Magnetic Resonance Imaging.

As breast magnetic resonance imaging has evolved to become a routine part of clinical practice, so too has the need for radiologists to be aware of its potential pitfalls and limitations. Unique challenges arise in the identification and remedy of artifacts in breast magnetic resonance imaging, and it is important that radiologists and technicians work together to optimize protocols and monitor examinations such that these may be minimized or avoided entirely. This article presents patient-related and technical artifacts that may give rise to reduced image quality and ways to recognize and reduce them.

The Contact Zone: A Common Site of Tumor Recurrence in a Patient Who Underwent Skin-Sparing Mastectomy and Myocutaneous Flap Reconstruction.

Routine magnetic resonance imaging (MRI) screening is not typically warranted in asymptomatic patients with a history of breast cancer and myocutaneous flap reconstruction due to the rare incidence of local tumor recurrence. We present a case of recurrent invasive ductal carcinoma along the contact zone between the transverse rectus abdominis myocutaneous (TRAM) flap and the native breast tissue that was incidentally detected on a routine high-risk screening-MRI of the breast in an asymptomatic patient with a history of breast cancer.

Breast cancer molecular subtype as a predictor of the utility of preoperative MRI.

OBJECTIVE: The purpose of this study was to discern whether breast cancer molecular subtype, a known prognostic indicator, can be used to select patients with the highest likelihood of having clinically significant additional findings on breast MRI. MATERIALS AND METHODS: A database review from January 2010 through December 2013 identified 299 patients who underwent preoperative breast MRI with tumors classifiable into molecular subtypes. Subtypes were classified on the basis of immunohistochemical staining surrogates as luminal A (hormone receptor [ER or PR] positive, ERBB2 [formerly HER2 or HER2/neu] negative, luminal B (hormone receptor positive, ERBB2 positive), ERBB2 (hormone receptor negative, ERBB2 positive), or basal (hormone receptor and ERBB2 negative). Univariate and multivariate logistic regression analyses were used to determine the association between subtype and additional breast MRI findings, including multicentric or multifocal disease, contralateral disease, chest wall involvement, skin and nipple involvement, and internal mammary and axillary lymphadenopathy. RESULTS: The subtype distribution was luminal A, 70.6% (211/299); luminal B, 14.1% (42/299); ERBB2, 5.4% (16/299); and basal, 10.0% (30/299). ERBB2 and luminal B sub-types were more often associated with multicentric disease (25.0% and 26.2%), multifocal disease (37.5% and 35.7%), and axillary disease (50.0% and 45.2%) than were luminal A cancers (multicentric disease, 10.9%; multifocal disease 20.4%; axillary disease, 22.7%) (p < 0.001). In multivariate analysis, after control for patient age, tumor size, and nuclear grade, patients with ERBB2-overexpressing tumors were 2.4 times as likely as patients with luminal A tumors to have multicentric disease (p = 0.016), 2.0 times as likely to have multifocal disease (p = 0.024), 1.7 times as likely to have skin and nipple involvement (p = 0.013), and 1.9 times as likely to have axillary disease (p = 0.011). CONCLUSION: Preoperative MRI may most benefit patients with tumors with ERBB2 overexpression because of the increased likelihood of the presence of additional disease.