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Purpose: While mammography has excellent sensitivity for the detection of breast lesions, its specificity is limited. Adjunct screening with ultrasound may partially alleviate this issue but also increases false positives, resulting in unnecessary biopsies. Our study investigated the use of Google AutoML Vision (Mountain View, California), a commercially available machine learning service, to both identify and characterize indeterminate breast lesions on ultrasound. Approach: B-mode images from 253 independent cases of indeterminate breast lesions scheduled for core biopsy were used for model creation and validation. The performances of two sub-models from AutoML Vision, the image classification model and object detection model, were evaluated, while also investigating training strategies to enhance model performances. Pathology from the patient's biopsy was used as a reference standard. Results: The image classification models trained under different conditions demonstrated areas under the precision-recall curve (AUC) ranging from 0.85 to 0.96 during internal validation. Once deployed, the model with highest internal performance demonstrated a sensitivity of 100% [95% confidence interval (CI) of 73.5% to 100%], specificity of 83.3% ( CI = 51.6 % to 97.9%), positive predictive value (PPV) of 85.7% ( CI = 62.9 % to 95.5%), and negative predictive value (NPV) of 100% (CI non-evaluable) in an independent dataset. The object detection model demonstrated lower performance internally during development ( AUC = 0.67 ) and during prediction in the independent dataset [ sensitivity = 75 % ( CI = 42.8 to 94.5), specificity = 80 % ( CI = 51.9 to 95.7), PPV = 75 % ( CI = 50.8 to 90.0), and NPV = 80 % ( CI = 59.3 % to 91.7%)], but was able to demonstrate the location of the lesion within the image. Conclusions: Two models appear to be useful tools for identifying and classifying suspicious areas on B-mode images of indeterminate breast lesions., (© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Rationale and Objectives: Breast cancer is the leading type of cancer among women. Visualization and characterization of breast lesions based on vascularity kinetics was evaluated using three-dimensional (3D) contrast-enhanced ultrasound imaging in a clinical study., Materials and Methods: Breast lesions (n = 219) were imaged using power Doppler imaging (PDI), 3D contrast-enhanced harmonic imaging (HI), and 3D contrast-enhanced subharmonic imaging (SHI) with a modified Logiq 9 ultrasound scanner using a 4D10L transducer. Quantitative metrics of vascularity derived from 3D parametric volumes (based on contrast perfusion; PER and area under the curve; AUC) were generated by off-line processing of contrast wash-in and wash-out. Diagnostic accuracy of these quantitative vascular parameters was assessed with biopsy results as the reference standard., Results: Vascularity was observed with PDI in 93 lesions (69 benign and 24 malignant), 3D HI in 8 lesions (5 benign and 3 malignant), and 3D SHI in 83 lesions (58 benign and 25 malignant). Diagnostic accuracy for vascular heterogeneity, PER, and AUC ranged from 0.52 to 0.75, while the best logistical regression model (vascular heterogeneity ratio, central PER, and central AUC) reached 0.90., Conclusion: 3D SHI successfully detects contrast agent flow in breast lesions and characterization of these lesions based on quantitative measures of vascular heterogeneity and 3D parametric volumes is promising., (Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

In this work, we propose a deep learning method for breast mass segmentation in ultrasound (US). Variations in breast mass size and image characteristics make the automatic segmentation difficult. To address this issue, we developed a selective kernel (SK) U-Net convolutional neural network. The aim of the SKs was to adjust network's receptive fields via an attention mechanism, and fuse feature maps extracted with dilated and conventional convolutions. The proposed method was developed and evaluated using US images collected from 882 breast masses. Moreover, we used three datasets of US images collected at different medical centers for testing (893 US images). On our test set of 150 US images, the SK-U-Net achieved mean Dice score of 0.826, and outperformed regular U-Net, Dice score of 0.778. When evaluated on three separate datasets, the proposed method yielded mean Dice scores ranging from 0.646 to 0.780. Additional fine-tuning of our better-performing model with data collected at different centers improved mean Dice scores by ~6%. SK-U-Net utilized both dilated and regular convolutions to process US images. We found strong correlation, Spearman's rank coefficient of 0.7, between the utilization of dilated convolutions and breast mass size in the case of network's expansion path. Our study shows the usefulness of deep learning methods for breast mass segmentation. SK-U-Net implementation and pre-trained weights can be found at github.com/mbyr/bus_seg., Competing Interests: Conflict of interest The authors do not have any conflicts of interest.

Background: Quantitative diffusion-weighted imaging (DWI) MRI is a promising technique for cancer characterization and treatment monitoring. Knowledge of the reproducibility of DWI metrics in breast tumors is necessary to apply DWI as a clinical biomarker., Purpose: To evaluate the repeatability and reproducibility of breast tumor apparent diffusion coefficient (ADC) in a multi-institution clinical trial setting, using standardized DWI protocols and quality assurance (QA) procedures., Study Type: Prospective., Subjects: In all, 89 women from nine institutions undergoing neoadjuvant chemotherapy for invasive breast cancer., Field Strength/sequence: DWI was acquired before and after patient repositioning using a four b-value, single-shot echo-planar sequence at 1.5T or 3.0T., Assessment: A QA procedure by trained operators assessed artifacts, fat suppression, and signal-to-noise ratio, and determine study analyzability. Mean tumor ADC was measured via manual segmentation of the multislice tumor region referencing DWI and contrast-enhanced images. Twenty cases were evaluated multiple times to assess intra- and interoperator variability. Segmentation similarity was assessed via the Sørenson-Dice similarity coefficient., Statistical Tests: Repeatability and reproducibility were evaluated using within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), agreement index (AI), and repeatability coefficient (RC). Correlations were measured by Pearson's correlation coefficients., Results: In all, 71 cases (80%) passed QA evaluation: 44 at 1.5T, 27 at 3.0T; 60 pretreatment, 11 after 3 weeks of taxane-based treatment. ADC repeatability was excellent: wCV = 4.8% (95% confidence interval [CI] 4.0, 5.7%), ICC = 0.97 (95% CI 0.95, 0.98), AI = 0.83 (95% CI 0.76, 0.87), and RC = 0.16 * 10 -3 mm 2 /sec (95% CI 0.13, 0.19). The results were similar across field strengths and timepoint subgroups. Reproducibility was excellent: interreader ICC = 0.92 (95% CI 0.80, 0.97) and intrareader ICC = 0.91 (95% CI 0.78, 0.96)., Data Conclusion: Breast tumor ADC can be measured with excellent repeatability and reproducibility in a multi-institution setting using a standardized protocol and QA procedure. Improvements to DWI image quality could reduce loss of data in clinical trials., Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1617-1628., (© 2018 International Society for Magnetic Resonance in Medicine.)

Purpose: We propose a deep learning-based approach to breast mass classification in sonography and compare it with the assessment of four experienced radiologists employing breast imaging reporting and data system 4th edition lexicon and assessment protocol., Methods: Several transfer learning techniques are employed to develop classifiers based on a set of 882 ultrasound images of breast masses. Additionally, we introduce the concept of a matching layer. The aim of this layer is to rescale pixel intensities of the grayscale ultrasound images and convert those images to red, green, blue (RGB) to more efficiently utilize the discriminative power of the convolutional neural network pretrained on the ImageNet dataset. We present how this conversion can be determined during fine-tuning using back-propagation. Next, we compare the performance of the transfer learning techniques with and without the color conversion. To show the usefulness of our approach, we additionally evaluate it using two publicly available datasets., Results: Color conversion increased the areas under the receiver operating curve for each transfer learning method. For the better-performing approach utilizing the fine-tuning and the matching layer, the area under the curve was equal to 0.936 on a test set of 150 cases. The areas under the curves for the radiologists reading the same set of cases ranged from 0.806 to 0.882. In the case of the two separate datasets, utilizing the proposed approach we achieved areas under the curve of around 0.890., Conclusions: The concept of the matching layer is generalizable and can be used to improve the overall performance of the transfer learning techniques using deep convolutional neural networks. When fully developed as a clinical tool, the methods proposed in this paper have the potential to help radiologists with breast mass classification in ultrasound., (© 2018 American Association of Physicists in Medicine.)

Purpose To determine if the change in tumor apparent diffusion coefficient (ADC) at diffusion-weighted (DW) MRI is predictive of pathologic complete response (pCR) to neoadjuvant chemotherapy for breast cancer. Materials and Methods In this prospective multicenter study, 272 consecutive women with breast cancer were enrolled at 10 institutions (from August 2012 to January 2015) and were randomized to treatment with 12 weekly doses of paclitaxel (with or without an experimental agent), followed by 12 weeks of treatment with four cycles of anthracycline. Each woman underwent breast DW MRI before treatment, at early treatment (3 weeks), at midtreatment (12 weeks), and after treatment. Percentage change in tumor ADC from that before treatment (ΔADC) was measured at each time point. Performance for predicting pCR was assessed by using the area under the receiver operating characteristic curve (AUC) for the overall cohort and according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype. Results The final analysis included 242 patients with evaluable serial imaging data, with a mean age of 48 years ± 10 (standard deviation); 99 patients had HR-positive (hereafter, HR+)/HER2-negative (hereafter, HER2-) disease, 77 patients had HR-/HER2- disease, 42 patients had HR+/HER2+ disease, and 24 patients had HR-/HER2+ disease. Eighty (33%) of 242 patients experienced pCR. Overall, ΔADC was moderately predictive of pCR at midtreatment/12 weeks (AUC = 0.60; 95% confidence interval [CI]: 0.52, 0.68; P = .017) and after treatment (AUC = 0.61; 95% CI: 0.52, 0.69; P = .013). Across the four disease subtypes, midtreatment ΔADC was predictive only for HR+/HER2- tumors (AUC = 0.76; 95% CI: 0.62, 0.89; P < .001). In a test subset, a model combining tumor subtype and midtreatment ΔADC improved predictive performance (AUC = 0.72; 95% CI: 0.61, 0.83) over ΔADC alone (AUC = 0.57; 95% CI: 0.44, 0.70; P = .032.). Conclusion After 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy. © RSNA, 2018 Online supplemental material is available for this article.

Background: Preoperative breast and lung markers have significant drawbacks, including migration, patient discomfort, and scheduling difficulties. SignalMark is a novel localizer device with a unique signal on Doppler ultrasound., Objective: We aimed to evaluate intraoperative identification of SignalMark microspheres compared with HydroMARK ® clips. We also assessed the safety and efficacy of SignalMark in the lung., Methods: Twelve breasts of lactating pigs were injected with SignalMark or HydroMARK ® by a breast radiologist, and subsequently identified using a standard ultrasound machine by three surgeons blinded to marker location. Time to identification of each marker was recorded, with a maximum allotted time of 300 s. To further demonstrate efficacy in lung parenchyma, a second cohort of pigs underwent lung injections., Results: A total of eight SignalMark markers and four HydroMARK ® clips were placed in pig breasts. Overall, the surgeons correctly identified SignalMark 95.8% of the time (n = 23/24) and HydroMARK ® clips 41.7% of the time (n = 5/12) within 300 s (p < 0.001). The mean time to identification was significantly faster for SignalMark, at 80.8 ± 20.1 s, than for HydroMARK ® , at 209.4 ± 35.2 s (p < 0.002). For the lung injections, all 10 SignalMark markers were visible on Doppler ultrasound at the time of placement, and at the 7- and 21-day time points., Conclusions: Surgeons identified SignalMark in significantly less time than HydroMARK ® clips in a simulated intraoperative setting, and SignalMark was easily viewed in the lung. These results suggest that SignalMark is a feasible option for efficient intraoperative localization of non-palpable breast and lung tumors using ultrasound guidance.

An important limitation of conventional two-dimensional mammography is the overlap of dense breast tissue obscuring masses. Digital breast tomosynthesis (DBT) has emerged as a mammographic technology that overcomes this limitation and is considered an overall better mammogram. DBT has been shown to detect significantly more breast cancer than conventional two-dimensional mammography, and to decrease the number of callbacks for diagnostic evaluation from screening mammography. Usually the callback evaluation is deemed to be overlap of tissue requiring no further management. DBT is used in the screening and diagnostic setting, and for guidance of wire localization or core biopsy, performing more accurately in the dense breast., (Copyright © 2017 Elsevier Inc. All rights reserved.)

[This corrects the article on p. 304 in vol. 20, PMID: 28970857.].

Purpose: This retrospective study investigated if the initially assigned category 4A or 3 in concordant benign lesions, after ultrasound (US)-guided core needle biopsy, could affect follow-up compliance., Methods: Eight hundred thirty-eight concordant benign lesions, after core needle biopsy (674, initial category 4A group and 164, category 3 group) and follow-up US, were included in our study. If an immediate surgical excision-a surgical excision before the next follow-up-exists, those cases with pathologic reports were collected. Statistical comparisons for the result of follow-up US compliance, additional biopsy, and malignant rates among 6-month, 12-month, and long-term intervals were performed by using the chi-square test. The log-rank test was used to compare compliance rates in the timing of first follow-up between the two groups, with a significance level of 0.05., Results: The number of immediate surgical excision was higher in the category 4A group (11.1%) than in the category 3 group (6.1%); only one cancer was found in the category 4A group. The patients' compliance rate in a 6-month follow-up showed an increase ( p =0.003) in the category 4A group. The additional biopsy rate was higher in the initial category 4A group (10.9%) than in the category 3 group (1.9%) with statistical significance ( p =0.001); four cancers were found on additional biopsy in the category 4 group. No cancer was detected in the initial category 3 group, both on immediate surgical excision and follow-up., Conclusion: The initial category 4A or 3 of the Breast Imaging Reporting and Data System could be a significant factor that affects immediate surgical excision and follow-up compliance. Cancers were detected only in the initial category 4A group of concordant benign lesions. More attention should be paid to the concordant benign lesions from the initial category 4A group than from the category 3 group., Competing Interests: CONFLICT OF INTEREST: The authors declare that they have no competing interests.

Objective: We hypothesize that radiologists' estimated percentage likelihood assessments for the presence of ductal carcinoma in situ (DCIS) and invasive cancer may predict histologic outcomes., Materials and Methods: Two hundred fifty cases categorized as BI-RADS category 4 or 5 at four University of California Medical Centers were retrospectively reviewed by 10 academic radiologists with a range of 1-39 years in practice. Readers assigned BI-RADS category (1, 2, 3, 4a, 4b, 4c, or 5), estimated percentage likelihood of DCIS or invasive cancer (0-100%), and confidence rating (1 = low, 5 = high) after reviewing screening and diagnostic mammograms and ultrasound images. ROC curves were generated., Results: Sixty-two percent (156/250) of lesions were benign and 38% (94/250) were malignant. There were 26 (10%) DCIS, 20 (8%) invasive cancers, and 48 (19%) cases of DCIS and invasive cancer. AUC values were 0.830-0.907 for invasive cancer and 0.731-0.837 for DCIS alone. Sensitivity of 82% (56/68), specificity of 84% (153/182), positive predictive value (PPV) of 66% (56/85), negative predictive value (NPV) of 93% (153/165), and accuracy of 84% ([56 + 153]/250) were calculated using an estimated percentage likelihood of 20% or higher as the prediction threshold for invasive cancer for the radiologist with the highest AUC (0.907; 95% CI, 0.864-0.951). Every 20% increase in the estimated percentage likelihood of invasive cancer increased the odds of invasive cancer by approximately two times (odds ratio, 2.4). For DCIS, using a threshold of 40% or higher, sensitivity of 81% (21/26), specificity of 79% (178/224), PPV of 31% (21/67), NPV of 97% (178/183), and accuracy of 80% ([21 + 178]/250) were calculated. Similarly, these values were calculated at thresholds of 2% or higher (BI-RADS category 4) and 95% or higher (BI-RADS category 5) to predict the presence of malignancy., Conclusion: Using likelihood estimates, radiologists may predict the presence of invasive cancer with fairly high accuracy. Radiologist-assigned estimated percentage likelihood can predict the presence of DCIS, albeit with lower accuracy than that for invasive cancer.

Rationale and Objectives: The study aimed to determine the inter-observer agreement among academic breast radiologists when using the Breast Imaging Reporting and Data System (BI-RADS) lesion descriptors for suspicious findings on diagnostic mammography., Materials and Methods: Ten experienced academic breast radiologists across five medical centers independently reviewed 250 de-identified diagnostic mammographic cases that were previously assessed as BI-RADS 4 or 5 with subsequent pathologic diagnosis by percutaneous or surgical biopsy. Each radiologist assessed the presence of the following suspicious mammographic findings: mass, asymmetry (one view), focal asymmetry (two views), architectural distortion, and calcifications. For any identified calcifications, the radiologist also described the morphology and distribution. Inter-observer agreement was determined with Fleiss kappa statistic. Agreement was also calculated by years of experience., Results: Of the 250 lesions, 156 (62%) were benign and 94 (38%) were malignant. Agreement among the 10 readers was strongest for recognizing the presence of calcifications (k = 0.82). There was substantial agreement among the readers for the identification of a mass (k = 0.67), whereas agreement was fair for the presence of a focal asymmetry (k = 0.21) or architectural distortion (k = 0.28). Agreement for asymmetries (one view) was slight (k = 0.09). Among the categories of calcification morphology and distribution, reader agreement was moderate (k = 0.51 and k = 0.60, respectively). Readers with more experience (10 or more years in clinical practice) did not demonstrate higher levels of agreement compared to those with less experience., Conclusions: Strength of agreement varies widely for different types of mammographic findings, even among dedicated academic breast radiologists. More subtle findings such as asymmetries and architectural distortion demonstrated the weakest agreement. Studies that seek to evaluate the predictive value of certain mammographic features for malignancy should take into consideration the inherent interpretive variability for these findings., (Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Breast density notification laws, passed in 19 states as of October 2014, mandate that patients be informed of their breast density. The purpose of this study is to assess the impact of this legislation on radiology practices, including performance of breast cancer risk assessment and supplemental screening studies. A 20-question anonymous web-based survey was emailed to radiologists in the Society of Breast Imaging between August 2013 and March 2014. Statistical analysis was performed using Fisher's exact test. Around 121 radiologists from 110 facilities in 34 USA states and 1 Canadian site responded. About 50% (55/110) of facilities had breast density legislation, 36% of facilities (39/109) performed breast cancer risk assessment (one facility did not respond). Risk assessment was performed as a new task in response to density legislation in 40% (6/15) of facilities in states with notification laws. However, there was no significant difference in performing risk assessment between facilities in states with a law and those without (p < 0.831). In anticipation of breast density legislation, 33% (16/48), 6% (3/48), and 6% (3/48) of facilities in states with laws implemented handheld whole breast ultrasound (WBUS), automated WBUS, and tomosynthesis, respectively. The ratio of facilities offering handheld WBUS was significantly higher in states with a law than in states without (p < 0.001). In response to breast density legislation, more than 33% of facilities are offering supplemental screening with WBUS and tomosynthesis, and many are performing formal risk assessment for determining patient management., (© 2016 Wiley Periodicals, Inc.)

Initially developed in 1993, the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon serves to standardize breast imaging reports, improve communication with referring physicians, and provide a quality assurance tool. The long-awaited BI-RADS fifth edition consolidates, improves, and expands the lexicon for mammography, breast ultrasonography (US), and breast magnetic resonance (MR) imaging. The new edition has increased the number of imaging examples to nearly 600. The breast MR imaging lexicon is significantly expanded since it first appeared in the fourth edition. New terms have been added to the US lexicon to reflect technologic advances. Minor but important changes have been made to the mammography section. Calcification descriptors in the lexicon are now consolidated into two categories: benign and suspicious. The controversial "intermediate concern" grouping has been eliminated, and a table in the lexicon summarizes the literature supporting the recommendation to biopsy such calcifications. New descriptors such as "developing asymmetry" are illustrated, and abstracts are provided to reference their significance. A generous guidance section is included after the lexicon description for each modality. Useful frequently asked questions are succinctly answered, and the literature to support each answer is included in the reference section for each modality. This review article illustrates and highlights changes to the BI-RADS lexicon and provides readers with a general overview to familiarize them with the fifth edition. (©)RSNA, 2016.

Ability to visualize breast lesion vascularity and quantify the vascular heterogeneity using contrast-enhanced 3-D harmonic (HI) and subharmonic (SHI) ultrasound imaging was investigated in a clinical population. Patients (n = 134) identified with breast lesions on mammography were scanned using power Doppler imaging, contrast-enhanced 3-D HI, and 3-D SHI on a modified Logiq 9 scanner (GE Healthcare). A region of interest corresponding to ultrasound contrast agent flow was identified in 4D View (GE Medical Systems) and mapped to raw slice data to generate a map of time-intensity curves for the lesion volume. Time points corresponding to baseline, peak intensity, and washout of ultrasound contrast agent were identified and used to generate and compare vascular heterogeneity plots for malignant and benign lesions. Vascularity was observed with power Doppler imaging in 84 lesions (63 benign and 21 malignant). The 3-D HI showed flow in 8 lesions (5 benign and 3 malignant), whereas 3-D SHI visualized flow in 68 lesions (49 benign and 19 malignant). Analysis of vascular heterogeneity in the 3-D SHI volumes found benign lesions having a significant difference in vascularity between central and peripheral sections (1.71 ± 0.96 vs. 1.13 ± 0.79 dB, p < 0.001, respectively), whereas malignant lesions showed no difference (1.66 ± 1.39 vs. 1.24 ± 1.14 dB, p = 0.24), indicative of more vascular coverage. These preliminary results suggest quantitative evaluation of vascular heterogeneity in breast lesions using contrast-enhanced 3-D SHI is feasible and able to detect variations in vascularity between central and peripheral sections for benign and malignant lesions.

Patients with human epidermal growth factor receptor 2 (HER2neu)-positive breast invasive cancer are known to have larger, more aggressive tumors. Little research exists on the relationship between HER2neu status and extent of ductal carcinoma In Situ (DCIS). A retrospective review of a single-institution database was performed for patients with DCIS between the years 2002 and 2011. A single blinded breast radiologist reviewed preoperative imaging. Pathology was reviewed for extent of DCIS. Primary outcome was mastectomy. Multivariate logistic regression was used to determine adjusted mastectomy risk. There were 166 cases, 34 HER2neu-positive. HER2neu receptor-positive patients had larger lesions on imaging: 4.0 versus 2.7 cm, by 2.9 versus 1.5 cm (P = 0.0499 and 0.0182). HER2neu-positive patients with DCIS were more likely than HER2neu-negative to undergo mastectomy than lumpectomy (53 vs 28%, P = 0.006). Pathology revealed a trend toward larger lesions in HER2neu-positive patients (2.96 vs 2.22 cm, nonsignificant). Patients with HER2neu-positive disease were three times more likely to undergo mastectomy (odds ratio, 2.9; 95% confidence interval, 1.23 to 6.78). Patients with HER2neu-positive DCIS had greater extent of disease by imaging and were more likely to undergo mastectomy than HER2neu-negative. These findings will help surgeons counsel patients on surgical treatment.

Introduction: Calcifications can be indicative of malignancy, but calcifications also can be a byproduct of necrotic tissue as cancer cells die. Current treatment regimens require excision of calcifications. The objective of this study was to examine the correlation between the extent of calcification on mammography and actual tumor size after neoadjuvant chemotherapy (NAC) as well as magnetic resonance imaging (MRI) for comparison., Methods: We retrospectively reviewed all patients at the University of California, San Diego, who underwent NAC for breast cancer between 2007 and 2013. Pearson correlation coefficients were computed between breast imaging and pathological measurements., Results: There were 136 patients total. Average age was 51 years. Fifty-three patients had calcifications on imaging (calc+); 83 did not (calc-). In the calc- group, extent of disease measured by mammogram (MMG) and MRI correlated moderately well with pathological tumor size (0.46 and 0.48, p = not significant). In the calc+ group, MRI was more likely to correlate with pathology than MMG (0.55 vs. -0.12, p = 0.01). Twenty-five calc+ patients had increased calcification after NAC; six of these had complete pathologic response. MRI correlated better with tumor size on pathology in patients with anti-HER2neu-based regimens than in patients with cytotoxic chemotherapy-alone regimens (0.88 vs. 0.4, p = 0.0001). MRI also is more accurate at predicting pathological tumor size in patients with triple negative disease (p = 0.002)., Conclusions: Magnetic resonance imaging correlated well while MMG calcification measurements correlated poorly with tumor size on final pathology. Extent of calcifications on diagnostic mammography may not be accurate in preoperative evaluation of breast cancers after NAC.

Objective: DNA sequencing tests are enabling physicians to interrogate the molecular profiles of patients' tumors, but most oncologists have not been trained in advanced genomics. We initiated a molecular tumor board to provide expert multidisciplinary input for these patients., Materials and Methods: A team that included clinicians, basic scientists, geneticists, and bioinformatics/pathway scientists with expertise in various cancer types attended. Molecular tests were performed in a Clinical Laboratory Improvement Amendments environment., Results: Patients (n = 34, since December 2012) had received a median of three prior therapies. The median time from physician order to receipt of molecular diagnostic test results was 27 days (range: 14-77 days). Patients had a median of 4 molecular abnormalities (range: 1-14 abnormalities) found by next-generation sequencing (182- or 236-gene panels). Seventy-four genes were involved, with 123 distinct abnormalities. Importantly, no two patients had the same aberrations, and 107 distinct abnormalities were seen only once. Among the 11 evaluable patients whose treatment had been informed by molecular diagnostics, 3 achieved partial responses (progression-free survival of 3.4 months, ≥6.5 months, and 7.6 months). The most common reasons for being unable to act on the molecular diagnostic results were that patients were ineligible for or could not travel to an appropriately targeted clinical trial and/or that insurance would not cover the cognate agents., Conclusion: Genomic sequencing is revealing complex molecular profiles that differ by patient. Multidisciplinary molecular tumor boards may help optimize management. Barriers to personalized therapy include access to appropriately targeted drugs., (©AlphaMed Press.)

Our understanding of the biology of breast cancer has dramatically expanded over the past decade, revealing that breast cancer is a heterogeneous group of diseases. This new knowledge can generate insights to improve screening performance and the management of ductal carcinoma in situ. In this article, the authors review the current state of the science of breast cancer and tools that can be used to improve screening and risk assessment. They describe several opportunities to improve clinical screening: (1) radiologists interpreting mammograms should aim to differentiate between the risk for invasive cancer and ductal carcinoma in situ to better assess the time frame for disease progression and the need for and optimal timing of biopsy; (2) imaging features associated with low risk, slow-growing cancer versus high risk, fast-growing cancer should be better defined and taught; and (3) as we learn more about assessing an individual's risk for developing breast cancer, we should incorporate these factors into a strategy for personalized screening to maximize benefit and minimize harm., (Copyright © 2013 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

In anticipation of breast density notification legislation in the state of California, which would require notification of women with heterogeneously and extremely dense breast tissue, a working group of breast imagers and breast cancer risk specialists was formed to provide a common response framework. The California Breast Density Information Group identified key elements and implications of the law, researching scientific evidence needed to develop a robust response. In particular, issues of risk associated with dense breast tissue, masking of cancers by dense tissue on mammograms, and the efficacy, benefits, and harms of supplementary screening tests were studied and consensus reached. National guidelines and peer-reviewed published literature were used to recommend that women with dense breast tissue at screening mammography follow supplemental screening guidelines based on breast cancer risk assessment. The goal of developing educational materials for referring clinicians and patients was reached with the construction of an easily accessible Web site that contains information about breast density, breast cancer risk assessment, and supplementary imaging. This multi-institutional, multidisciplinary approach may be useful for organizations to frame responses as similar legislation is passed across the United States. Online supplemental material is available for this article., (© RSNA, 2013.)

There is no difference in disease-free or overall survival in patients who undergo adjuvant versus neoadjuvant chemotherapy. Thus, neoadjuvant chemotherapy is recommended in patients with locally advanced breast cancer who would like to consider breast conservation, and is also the primary treatment in patients with inflammatory breast cancer. Magnetic resonance has emerged as the most sensitive imaging modality to assess the response of tumor to neoadjuvant chemotherapy., (Copyright © 2013 Elsevier Inc. All rights reserved.)

A method was developed to map tissue properties of the entire breast including sound speed and attenuation using fully 3D nonlinear inverse-scattering tomography. Clinical measurements suggest that in breast tissue benign and cancerous lesions may be identified in part by these inherent acoustic parameters. Sound speed accuracy and linearity are very high over a wide range (1325-1700 m/sec) with ~1.5 mm resolution at 2 MHz in transmission mode. Attenuation tomograms provide image contrast over a wide range (0-4 dB/cm/MHz) and assist classification of masses. High resolution 0.6 mm volumetric reflection tomograms are acquired with bandwidth 2-8 MHz, are refraction-corrected with the transmission tissue data and are precisely registered in 3D with the transmission volumes. USCT promises an automated whole-breast scan providing a global view of the entire breast in 3D, facilitating comparison to prior exams in a reproducible geometry. Scanner design, automated operation and results of our trial with over 125 subjects with confirmed breast masses will be presented with detailed comparison to conventional sonography and MRI.

Accelerated partial breast irradiation (APBI) is a technique that allows irradiation of only that part of the breast that is at greatest risk for recurrence of breast cancer. Because only a portion of the breast is irradiated, APBI can be performed in a relatively short period of time, usually in 5 days rather than the traditional 6 weeks. When used in carefully selected patients, APBI also allows normal portions of the breast parenchyma and regional vital organs to be spared from unnecessary irradiation. Common post-APBI imaging findings include focal skin thickening, seroma, scar, and skin retraction. Studies are underway that will compare a cohort of patients who underwent whole-breast irradiation with a cohort who underwent APBI to help determine whether the two techniques lead to significantly different imaging findings. Additional multicenter studies will be needed to document and analyze any such differences. In the future, APBI may play a significant role in selected patients, with pretherapy dynamic contrast material-enhanced magnetic resonance imaging of the breast possibly aiding in the selection process., (© RSNA, 2011.)

Human acellular tissue matrix (AlloDerm) use in breast reconstruction has become popular. Traditionally used for prosthesis coverage, it is also used in our practice as a filler for lumpectomy defects and a contouring device. However, no report presently exists that describes its appearance on oncologic surveillance studies. We performed a retrospective review of all charts of patients having undergone cancer-related breast reconstruction using AlloDerm as a filler at a single institution between 2005 and 2009. Postoperative mammograms and dynamic contrast enhanced breast magnetic resonance imaging were reviewed with a dedicated breast imager. Sixteen women underwent surgery involving placement of an AlloDerm roll in the breast. Postoperative films were unavailable for two of them. Of the remaining 14 patients, nine had postoperative mammograms only, three had postoperative dynamic contrast enhanced breast magnetic resonance imaging only, and two patients had both. In all cases, evaluation of the postoperative images was not affected by the presence of AlloDerm. In our short-term, retrospective experience, we find that a thorough radiographic evaluation of the breast tissue remains possible when AlloDerm rolls are used in reconstruction.

Mammography is the only imaging study that has been proven in multiple large randomized trials to decrease breast cancer mortality. Mammography, however, has its limitations and, as such, other modalities that can complement it are being studied. One of these is dynamic contrast-enhanced breast MRI, which has emerged as an important adjunctive modality and is at present the most sensitive modality that we have to evaluate the breast. The American College of Radiology, in its 2004 practice guidelines, has outlined the 12 current indications for breast MRI. This manuscript reviews and provides examples of each of these.

Magnetic resonance (MR) imaging of the breast has evolved into an important adjunctive tool in breast imaging with multiple and ever-increasing indications for its use. As with other types of MR imaging, there are a number of technical artifacts and pitfalls that can potentially limit interpretation of the images by masking or simulating disease. Because of the coils and computer-aided detection software specific to breast MR imaging, there are additional technical considerations that are unique to this type of MR imaging. Motion and misregistration artifacts, wraparound artifact, susceptibility artifact, poor fat saturation, lack of contrast material, and poor timing of the contrast material bolus are some of the artifacts and pitfalls that can make interpretation of breast MR images challenging and lead to misdiagnosis. Other important considerations in proper interpretation of breast MR images include acquisition of a sufficient medical history, knowledge of benign and abnormal lesion enhancement, morphologic versus kinetic assessment, evaluation of areas outside the breast, and positioning. By using the recommended strategies, one can reduce or eliminate common artifacts and pitfalls in breast MR imaging that prevent proper interpretation of the results of this important diagnostic tool., (Copyright RSNA, 2007.)