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PurposeDynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy.Experimental designBreast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint.ResultsTen patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC.ConclusionThe automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.

Background: In the setting of widespread COVID-19 vaccination and booster administration, there is an increased incidence of axillary lymphadenopathy identified during breast imaging.Purpose: To investigate how breast imaging radiologists manage unilateral axillary lymphadenopathy (UAL) after a recent ipsilateral COVID-19 vaccination.Methods: A 26-question survey was distributed to 12 California breast imaging facilities in June 2022. Results: There were 10 responses to the survey (83% response rate). All respondents considered recent ipsilateral COVID-19 vaccination relevant to the interpretation of UAL. Seven respondents (70%) also  considered non-COVID-19 vaccinations relevant. All respondents documented recent COVID-19 vaccinations, but 4 (40%) had no information for other vaccines. Eight respondents (80%) delayed screening after COVID-19 vaccination during initial vaccination efforts, and 3 (30%) still required or suggested delaying screening at the time of the survey. Breast Imaging Reporting and Data System (BI-RADS) categorization for UAL with no abnormal findings in the ipsilateral breast varied by facility and modality. BI-RADS categorization for UAL previously assigned to BI-RADS 0 or associated with suspicious ipsilateral breast findings varied, but practices tended to demonstrate a high level of suspicion unless the UAL showed improvement on follow-up imaging. For unchanged UAL on initial follow-up, 7 (70%) assign BI-RADS 3, and 3 (30%) assign BI-RADS 4.Conclusion: Despite available guidelines, there was no consensus approach to managing UAL after vaccination among academic and community-based breast imaging radiologists in California. Management was more uniform for a subset of patients perceived to be at higher risk for lymph node metastases, with most or all respondents recommending biopsy when there was  a suspicious finding in the ipsilateral breast, concurrent ipsilateral breast cancer, or concurrent malignant tumors not in the breast known to metastasize to the axilla.

OBJECTIVE: Breast cancer is the most frequent type of cancer among women. This multi-center study assessed the ability of 3D contrast-enhanced ultrasound to characterize suspicious breast lesions using clinical assessments and quantitative parameters. METHODS: Women with suspicious breast lesions scheduled for biopsy were enrolled in this prospective, study. Following 2D grayscale ultrasound and power Doppler imaging (PDI), a contrast agent (Definity; Lantheus) was administrated. Contrast-enhanced 3D harmonic imaging (HI; transmitting/receiving at 5.0/10.0 MHz), as well as 3D subharmonic imaging (SHI; transmitting/receiving at 5.8/2.9 MHz), were performed using a modified Logiq 9 scanner (GE Healthcare). Five radiologists independently scored the imaging modes (including standard-of-care imaging) using a 7-point BIRADS scale as well as lesion vascularity and diagnostic confidence. Parametric volumes were constructed from time-intensity curves for vascular heterogeneity, perfusion, and area under the curve. Diagnostic accuracy was determined relative to pathology using receiver operating characteristic (ROC) and reverse, step-wise logistical regression analyses. The κ-statistic was calculated for inter-reader agreement. RESULTS: Data were successfully acquired in 219 cases and biopsies indicated 164 (75%) benign and 55 (25%) malignant lesions. SHI depicted more anastomoses and vascularity than HI (P  .27). Ultrasound achieved accuracies of 82 to 85%, which was significantly better than standard-of-care imaging (72%; P

PurposeRestriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues.MethodsThe diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging.ResultsA total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D1,3 = 0, D2,3 = 1.5 × 10-3 , and D3,3 = 10.8 × 10-3 mm2 /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent.ConclusionBreast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.

Diffusion-weighted MRI (DW-MRI) offers a potential adjunct to dynamic contrast-enhanced MRI to discriminate benign from malignant breast lesions by yielding quantitative information about tissue microstructure. Multi-component modeling of the DW-MRI signal over an extended b-value range (up to 3000 s/mm2) theoretically isolates the slowly diffusing (restricted) water component in tissues. Previously, a three-component restriction spectrum imaging (RSI) model demonstrated the ability to distinguish malignant lesions from healthy breast tissue. We further evaluated the utility of this three-component model to differentiate malignant from benign lesions and healthy tissue in 12 patients with known malignancy and synchronous pathology-proven benign lesions. The signal contributions from three distinct diffusion compartments were measured to generate parametric maps corresponding to diffusivity on a voxel-wise basis. The three-component model discriminated malignant from benign and healthy tissue, particularly using the restricted diffusion C1 compartment and product of the restricted and intermediate diffusion compartments (C1 and C2). However, benign lesions and healthy tissue did not significantly differ in diffusion characteristics. Quantitative discrimination of these three tissue types (malignant, benign, and healthy) in non-pre-defined lesions may enhance the clinical utility of DW-MRI in reducing excessive biopsies and aiding in surveillance and surgical evaluation without repeated exposure to gadolinium contrast.

Background Suppression of background parenchymal enhancement (BPE) is commonly observed after neoadjuvant chemotherapy (NAC) at contrast-enhanced breast MRI. It was hypothesized that nonsuppressed BPE may be associated with inferior response to NAC. Purpose To investigate the relationship between lack of BPE suppression and pathologic response. Materials and Methods A retrospective review was performed for women with menopausal status data who were treated for breast cancer by one of 10 drug arms (standard NAC with or without experimental agents) between May 2010 and November 2016 in the Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2, or I-SPY 2 TRIAL (NCT01042379). Patients underwent MRI at four points: before treatment (T0), early treatment (T1), interregimen (T2), and before surgery (T3). BPE was quantitatively measured by using automated fibroglandular tissue segmentation. To test the hypothesis effectively, a subset of examinations with BPE with high-quality segmentation was selected. BPE change from T0 was defined as suppressed or nonsuppressed for each point. The Fisher exact test and the Z tests of proportions with Yates continuity correction were used to examine the relationship between BPE suppression and pathologic complete response (pCR) in hormone receptor (HR)-positive and HR-negative cohorts. Results A total of 3528 MRI scans from 882 patients (mean age, 48 years ± 10 [standard deviation]) were reviewed and the subset of patients with high-quality BPE segmentation was determined (T1, 433 patients; T2, 396 patients; T3, 380 patients). In the HR-positive cohort, an association between lack of BPE suppression and lower pCR rate was detected at T2 (nonsuppressed vs suppressed, 11.8% [six of 51] vs 28.9% [50 of 173]; difference, 17.1% [95% CI: 4.7, 29.5]; P = .02) and T3 (nonsuppressed vs suppressed, 5.3% [two of 38] vs 27.4% [48 of 175]; difference, 22.2% [95% CI: 10.9, 33.5]; P = .003). In the HR-negative cohort, patients with nonsuppressed BPE had lower estimated pCR rate at all points, but the P values for the association were all greater than .05. Conclusions In hormone receptor-positive breast cancer, lack of background parenchymal enhancement suppression may indicate inferior treatment response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

OBJECTIVE: To determine the early impact of the COVID-19 pandemic on breast imaging centers in California and Texas and compare regional differences. METHODS: An 11-item survey was emailed to American College of Radiology accredited breast imaging facilities in California and Texas in August 2020. A question subset addressed March-April government restrictions on elective services (during the shutdown and after reopening). Comparisons were made between states with chi-square and Fishers tests, and timeframes with McNemars and paired t-tests. RESULTS: There were 54 respondents (54/240, 23%, 26 California, 28 Texas). Imaging volumes fell during the shutdown and remained below pre-pandemic levels after reopening, with reduction in screening greatest (ultrasound 12% of baseline, mammography 13%, MRI 23%), followed by diagnostic MRI (43%), procedures (44%), and diagnostics (45%). California reported higher volumes during the shutdown (procedures, MRI) and after reopening (diagnostics, procedures, MRI) versus Texas (P = 0.001-0.02). Most screened patients (52/54, 96% symptoms and 42/54, 78% temperatures), and 100% (53/53) modified check-in and check-out. Reading rooms or physician work were altered for social distancing (31/54, 57%). Physician mask (45/48, 94%), gown (15/48, 31%), eyewear (22/48, 46%), and face shield (22/48, 46%) use during procedures increased after reopening versus pre-pandemic (P

ObjectiveTo determine the impact of the COVID-19 pandemic on breast imaging education.MethodsA 22-item survey addressing four themes during the early pandemic (time on service, structured education, clinical training, future plans) was emailed to Society of Breast Imaging members and members-in-training in July 2020. Responses were compared using McNemar's and Mann-Whitney U tests; a general linear model was used for multivariate analysis.ResultsOf 136 responses (136/2824, 4.8%), 96 U.S. responses from radiologists with trainees, residents, and fellows were included. Clinical exposure declined during the early pandemic, with almost no medical students on service (66/67, 99%) and fewer clinical days for residents (78/89, 88%) and fellows (48/68, 71%). Conferences shifted to remote live format (57/78, 73%), with some canceled (15/78, 19%). Compared to pre-pandemic, resident diagnostic (75/78, 96% vs 26/78, 33%) (P < 0.001) and procedural (73/78, 94% vs 21/78, 27%) (P < 0.001) participation fell, as did fellow diagnostic (60/61, 98% vs 47/61, 77%) (P = 0.001) and procedural (60/61, 98% vs 43/61, 70%) (P < 0.001) participation. Most thought that the pandemic negatively influenced resident and fellow screening (64/77, 83% and 43/60, 72%, respectively), diagnostic (66/77, 86% and 37/60, 62%), and procedural (71/77, 92% and 37/61, 61%) education. However, a majority thought that decreased time on service (36/67, 54%) and patient contact (46/79, 58%) would not change residents' pursuit of a breast imaging fellowship.ConclusionThe pandemic has had a largely negative impact on breast imaging education, with reduction in exposure to all aspects of breast imaging. However, this may not affect career decisions.

PurposeDiffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model.Experimental designPatients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C 1 and C 2 and their product, C 1 C 2, and signal fractions F 1, F 2, and F 1 F 2 were compared with the image defined on maximum b-value (DWI max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC.ResultsMean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for C 1 C 2, 0.136 (95% CI, 0.092-0.180) for C 1, 0.068 (95% CI, 0.049-0.087) for C 2, 0.462 (95% CI, 0.425-0.499) for F 1 F 2, 0.832 (95% CI, 0.797-0.868) for F 1, 0.176 (95% CI, 0.150-0.203) for F 2, 0.159 (95% CI, 0.114-0.204) for DWI max, 0.731 (95% CI, 0.692-0.770) for ADC, and 0.684 (95% CI, 0.660-0.709) for K app. Mean ROC AUC for C 1 C 2 was 0.984 (95% CI, 0.977-0.991).ConclusionsThe C 1 C 2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Magnetic resonance imaging (MRI) is used for preoperative evaluation, high-risk screening, and other select indications for breast cancer. However, the interpretation of breast MR images in pregnant and lactating women is complicated by physiologic changes of the breast that may result in marked background enhancement. Breast MRI with contrast administration is contraindicated in pregnancy. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted (DW)-MRI method that theoretically reflects signal from cells with high nuclear-to-cytoplasm ratio without gadolinium-based contrast. This report describes a case in which RSI notably increased tumor conspicuity in a lactating woman, compared to contrast-enhanced (CE)-MRI and conventional DW-MRI.

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 patients 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.

Rationale and objectivesBreast 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 methodsBreast 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.ResultsVascularity 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.Conclusion3D 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.

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.

BackgroundQuantitative 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.PurposeTo 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 typeProspective.SubjectsIn all, 89 women from nine institutions undergoing neoadjuvant chemotherapy for invasive breast cancer.Field strength/sequenceDWI was acquired before and after patient repositioning using a four b-value, single-shot echo-planar sequence at 1.5T or 3.0T.AssessmentA 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 testsRepeatability 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.ResultsIn 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 mm2 /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 conclusionBreast 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 evidence2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1617-1628.

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.

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.

BackgroundPreoperative 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.ObjectiveWe aimed to evaluate intraoperative identification of SignalMark microspheres compared with HydroMARK® clips. We also assessed the safety and efficacy of SignalMark in the lung.MethodsTwelve 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.ResultsA 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

ObjectiveWe 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 methodsTwo 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.ResultsSixty-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.ConclusionUsing 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.

Simple Summary: Incidental intraductal papilloma without atypia (IPA), lobular hyperplasia (LCIS or ALH), flat epithelial atypia (FEA) and complex sclerosing lesion (CSL) are high-risk lesions of the breast. These lesions are not routinely excised when diagnosed in isolation because they have low rates of upgrade to invasive cancer. When identified concurrently with invasive breast cancer, however, the upgrade rate is not well characterized. We sought to both characterize the upgrade rate for these lesions when diagnosed concurrently with invasive breast cancer, and to identify features of the high-risk lesions predictive of upgrade to cancer. In our cohort of 65 patients who were concurrently diagnosed with a high-risk breast lesion and an invasive cancer, 5 patients (7.7%) had an upgrade of their high-risk lesion to carcinoma. The rate of upgrade was higher for high-risk lesions that were ipsilateral to malignancy and within 5 cm of it, and lower for other lesions. High-risk breast lesions including incidental intraductal papilloma without atypia (IPA), lobular hyperplasia (LCIS or ALH), flat epithelial atypia (FEA) and complex sclerosing lesion (CSL) are not routinely excised due to low upgrade rates to carcinoma. We aim to identify features of these lesions predictive of upgrade when identified concurrently with invasive disease. Methods: A single-center retrospective cohort study was performed for patients who underwent multi-site lumpectomies with invasive disease at one site and a high-risk lesion at another site between 2006 and 2021. A multinomial logistic regression was performed. Results: Sixty-five patients met the inclusion criteria. Four patients (6.2%) had an upgrade to in situ disease (DCIS) and one (1.5%) to invasive carcinoma. Three upgraded high-risk lesions were ipsilateral to the concurrent carcinoma and two were contralateral. In the multivariate model, a high-risk lesion within 5 cm of an ipsilateral malignancy was associated with increased risk of upgrade. The 3.8% upgrade rate for high-risk lesions located greater than 5 cm from ipsilateral malignancy or in the contralateral breast suggests that omission of excisional biopsy may be considered. Excisional biopsy of lesions within 5 cm of ipsilateral malignancy is recommended given the 25% upgrade risk in our series. [ABSTRACT FROM AUTHOR]

ObjectiveDNA 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 methodsA 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.ResultsPatients (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.ConclusionGenomic 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.

Purpose: Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy. Experimental design: Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint. Results: Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC. [ABSTRACT FROM AUTHOR]