Repeat Sentinel Lymph Node Surgery in Recurrent Breast Cancer: Peritumoral vs. Periareolar Injections.

BACKGROUND: In the setting of recurrent cancer, there is no standard methodology regarding the technical aspects of repeat sentinel lymph node (rSLN) surgery. We analyzed our institutional experience with attempted rSLN surgery to determine the optimal injection technique. MATERIALS AND METHODS: Single site, retrospective review of patients with prior lumpectomy for breast cancer who presented with recurrent or new ipsilateral breast cancer and underwent attempt at rSLN surgery from 2008 to 2017. Patients with prior mastectomy or no prior ipsilateral axillary operation were excluded. RESULTS: A total of 141 patients were included; 103 (73%) underwent successful rSLN biopsy procedure. Lymphoscintigraphy showed aberrant drainage in 32 (26%). Periareolar (PA) injection resulted in failed mapping in 23/99 (23%) and aberrant drainage in 25/85 (29%). By comparison, peritumoral (PT) injection had a 14/38 (37%) incidence of failed mapping and 7/37 (19%) aberrant drainage (P = .11 and .23, respectively). Of the patients with successful sentinel lymph node (SLN) biopsy procedure via PA injection, 11/76 (14%) were positive for metastatic disease as compared with 2/24 (8%) in PT injection. Sixteen patients had lymph node metastases; 13 (81%) were SLNs, including 3 positive aberrant SLNs. Five-year regional recurrence rates were 11.4% (95% confidence interval, 0%-21.5%) and 0% for PA and PT injection techniques, respectively. CONCLUSION: PA and PT injections had a similar incidence of SLN identification and aberrant drainage. Preoperative lymphoscintigraphy is beneficial in patients with recurrent breast cancer given the higher incidence of aberrant drainage in this population. Patients who underwent PA injections had a higher incidence of regional recurrences but this difference was not statistically significant.

Ultrasound-Guided Breast Biopsies: Basic and New Techniques.

Ultrasound-guided breast biopsies can be challenging to perform, especially when the target is adjacent to the nipple, skin, or implant or when the target is small and in very posterior, dense fibroglandular tissue. Oftentimes, a slightly modified approach can result in a diagnostic biopsy specimen with minimal complications. After a brief review of basic techniques for ultrasound-guided breast biopsies that includes a review of conventional breast biopsy devices, a presentation of procedural modifications and techniques to consider for more challenging cases is described. In particular, novel open-trough and tandem-needle techniques are detailed. Several cases using these techniques are then presented.

Background Parenchymal Uptake on Molecular Breast Imaging and Breast Cancer Risk: A Cohort Study.

BACKGROUND. Background parenchymal uptake (BPU) on molecular breast imaging (MBI) was identified in a case-control study as a breast cancer risk factor beyond mammographic density. To our knowledge, this finding has not yet been confirmed in a cohort study. OBJECTIVE. The objectives of this study were to examine the association of BPU with breast cancer and to estimate the absolute risk and discriminatory accuracy of BPU in a cohort study. METHODS. A retrospective cohort was established that included women without a history of breast cancer who underwent MBI from 2004 to 2015. Radiologists who were blinded to future breast cancer diagnoses assessed BPU on baseline MBI examinations as low (photopenic or minimal) or elevated (mild, moderate, or marked). Associations of BPU with breast cancer were estimated using multivariable Cox proportional hazards models of the time to diagnosis. The 5-year absolute risk was calculated for study subgroups. The discriminatory accuracy of BPU was also assessed. RESULTS. Among 2992 women (mean age, 56.3 years; SD, 10.6 years) who underwent MBI, breast cancer events occurred in 144 women (median follow-up, 7.3 years). Median time to diagnosis after MBI was 4.2 years (range, 0.5-11.6 years). Elevated BPU was associated with a greater breast cancer risk (hazard ratio [HR], 2.39; 95% CI, 1.68-3.41; p ≤ .001). This association remained in postmenopausal women (HR, 3.50; 95% CI, 2.31-5.31; p < .001) but was not significant in premenopausal women (HR, 1.29; 95% CI, 0.72-2.32; p = .39). The 5-year absolute risk of breast cancer was 4.3% (95% CI, 2.9-5.7%) for women with elevated BPU versus 2.5% (95% CI, 1.8-3.1%) for those with low BPU. Postmenopausal women with dense breasts and elevated BPU had a 5-year absolute risk of 8.1% (95% CI, 4.3-11.8%) versus 2.8% (1.8-3.8%) for those with low BPU. Among postmenopausal women, discriminatory accuracy for invasive cancer was improved with the addition of BPU versus use of the Gail risk score alone (C statistic, 65.1 vs 59.1; p = .04) or use of the Breast Cancer Surveillance Consortium risk score alone (C statistic, 66.4 vs 60.4; p = .04). CONCLUSION. BPU on MBI is an independent risk factor for breast cancer, with the strongest association observed among postmenopausal women with dense breasts. In postmenopausal women, BPU provides incremental discrimination in predicting breast cancer when combined with either the Gail model or the Breast Cancer Surveillance Consortium model. CLINICAL IMPACT. Observation of elevated BPU on MBI may identify a subset of women with dense breasts who would benefit most from supplemental screening or preventive options.

Longitudinal Changes in Volumetric Breast Density in Healthy Women across the Menopausal Transition.

BACKGROUND: Mammographic breast density declines during menopause. We assessed changes in volumetric breast density across the menopausal transition and factors that influence these changes. METHODS: Women without a history of breast cancer, who had full field digital mammograms during both pre- and postmenopausal periods, at least 2 years apart, were sampled from four facilities within the San Francisco Mammography Registry from 2007 to 2013. Dense breast volume (DV) was assessed using Volpara on mammograms across the time period. Annualized change in DV from pre- to postmenopause was estimated using linear mixed models adjusted for covariates and per-woman random effects. Multiplicative interactions were evaluated between premenopausal risk factors and time to determine whether these covariates modified the annualized changes. RESULTS: Among the 2,586 eligible women, 1,802 had one premenopausal and one postmenopausal mammogram, 628 had an additional perimenopausal mammogram, and 156 had two perimenopausal mammograms. Women experienced an annualized decrease in DV [-2.2 cm3 (95% confidence interval, -2.7 to -1.7)] over the menopausal transition. Declines were greater among women with a premenopausal DV above the median (54 cm3) versus below (DV, -3.5 cm3 vs. -1.0 cm3; P < 0.0001). Other breast cancer risk factors, including race, body mass index, family history, alcohol, and postmenopausal hormone therapy, had no effect on change in DV over the menopausal transition. CONCLUSIONS: High premenopausal DV was a strong predictor of greater reductions in DV across the menopausal transition. IMPACT: We found that few factors other than premenopausal density influence changes in DV across the menopausal transition, limiting targeted prevention efforts.

Combined effect of volumetric breast density and body mass index on breast cancer risk.

BACKGROUND: Breast density and body mass index (BMI) are used for breast cancer risk stratification. We evaluate whether the positive association between volumetric breast density and breast cancer risk is strengthened with increasing BMI. METHODS: The San Francisco Mammography Registry and Mayo Clinic Rochester identified 781 premenopausal and 1850 postmenopausal women with breast cancer diagnosed between 2007 and 2015 that had a screening digital mammogram at least 6 months prior to diagnosis. Up to three controls (N = 3535) were matched per case on age, race, date, mammography machine, and state. Volumetric percent density (VPD) and dense volume (DV) were measured with Volpara™. Breast cancer risk was assessed with logistic regression stratified by menopause status. Multiplicative interaction tests assessed whether the association of density measures was differential by BMI categories. RESULTS: The increased risk of breast cancer associated with VPD was strengthened with higher BMI for both premenopausal (pinteraction = 0.01) and postmenopausal (pinteraction = 0.0003) women. For BMI < 25, 25-30, and ≥ 30 kg/m2, ORs for breast cancer for a 1 SD increase in VPD were 1.24, 1.65, and 1.97 for premenopausal, and 1.20, 1.55, and 2.25 for postmenopausal women, respectively. ORs for breast cancer for a 1 SD increase in DV were 1.39, 1.33, and 1.51 for premenopausal (pinteraction = 0.58), and 1.31, 1.34, and 1.65 (pinteraction = 0.03) for postmenopausal women for BMI < 25, 25-30 and ≥ 30 kg/m2, respectively. CONCLUSIONS: The effect of volumetric percent density on breast cancer risk is strongest in overweight and obese women. These associations have clinical relevance for informing prevention strategies.

Classification of Background Parenchymal Uptake on Molecular Breast Imaging Using a Convolutional Neural Network.

PURPOSE: Background parenchymal uptake (BPU), which describes the level of radiotracer uptake in normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor. Our objective was to develop and validate a deep learning model using image convolution to automatically categorize BPU on MBI. METHODS: MBI examinations obtained for clinical and research purposes from 2004 to 2015 were reviewed to classify the BPU pattern using a standardized five-category scale. Two expert radiologists provided interpretations that were used as the reference standard for modeling. The modeling consisted of training and validating a convolutional neural network to predict BPU. Model performance was summarized in data reserved to test the performance of the algorithm at the per-image and per-breast levels. RESULTS: Training was performed on 24,639 images from 3,133 unique patients. The model performance on the withheld testing data (6,172 images; 786 patients) was evaluated. Using direct matching on the predicted classification resulted in an accuracy of 69.4% (95% CI, 67.4% to 71.3%), and if prediction within one category was considered, accuracy increased to 96.0% (95% CI, 95.2% to 96.7%). When considering the breast-level prediction of BPU, the accuracy remained strong, with 70.3% (95% CI, 68.0% to 72.6%) and 96.2% (95% CI, 95.3% to 97.2%) for the direct match and allowance for one category, respectively. CONCLUSION: BPU provided a robust target for training a convolutional neural network. A validated computer algorithm will allow for objective, reproducible encoding of BPU to foster its integration into risk-stratification algorithms.

Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment.

Purpose To identify phenotypes of mammographic parenchymal complexity by using radiomic features and to evaluate their associations with breast density and other breast cancer risk factors. Materials and Methods Computerized image analysis was used to quantify breast density and extract parenchymal texture features in a cross-sectional sample of women screened with digital mammography from September 1, 2012, to February 28, 2013 (n = 2029; age range, 35-75 years; mean age, 55.9 years). Unsupervised clustering was applied to identify and reproduce phenotypes of parenchymal complexity in separate training (n = 1339) and test sets (n = 690). Differences across phenotypes by age, body mass index, breast density, and estimated breast cancer risk were assessed by using Fisher exact, χ2, and Kruskal-Wallis tests. Conditional logistic regression was used to evaluate preliminary associations between the detected phenotypes and breast cancer in an independent case-control sample (76 women diagnosed with breast cancer and 158 control participants) matched on age. Results Unsupervised clustering in the screening sample identified four phenotypes with increasing parenchymal complexity that were reproducible between training and test sets (P = .001). Breast density was not strongly correlated with phenotype category (R2 = 0.24 for linear trend). The low- to intermediate-complexity phenotype (prevalence, 390 of 2029 [19%]) had the lowest proportion of dense breasts (eight of 390 [2.1%]), whereas similar proportions were observed across other phenotypes (from 140 of 291 [48.1%] in the high-complexity phenotype to 275 of 511 [53.8%] in the low-complexity phenotype). In the independent case-control sample, phenotypes showed a significant association with breast cancer (P = .001), resulting in higher discriminatory capacity when added to a model with breast density and body mass index (area under the curve, 0.84 vs 0.80; P = .03 for comparison). Conclusion Radiomic phenotypes capture mammographic parenchymal complexity beyond conventional breast density measures and established breast cancer risk factors. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Pinker in this issue.

Quantitative background parenchymal uptake on molecular breast imaging and breast cancer risk: a case-control study.

BACKGROUND: Background parenchymal uptake (BPU), which refers to the level of Tc-99m sestamibi uptake within normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor, independent of mammographic density. Prior analyses have used subjective categories to describe BPU. We evaluate a new quantitative method for assessing BPU by testing its reproducibility, comparing quantitative results with previously established subjective BPU categories, and determining the association of quantitative BPU with breast cancer risk. METHODS: Two nonradiologist operators independently performed region-of-interest analysis on MBI images viewed in conjunction with corresponding digital mammograms. Quantitative BPU was defined as a unitless ratio of the average pixel intensity (counts/pixel) within the fibroglandular tissue versus the average pixel intensity in fat. Operator agreement and the correlation of quantitative BPU measures with subjective BPU categories assessed by expert radiologists were determined. Percent density on mammograms was estimated using Cumulus. The association of quantitative BPU with breast cancer (per one unit BPU) was examined within an established case-control study of 62 incident breast cancer cases and 177 matched controls. RESULTS: Quantitative BPU ranged from 0.4 to 3.2 across all subjects and was on average higher in cases compared to controls (1.4 versus 1.2, p < 0.007 for both operators). Quantitative BPU was strongly correlated with subjective BPU categories (Spearman's r = 0.59 to 0.69, p < 0.0001, for each paired combination of two operators and two radiologists). Interoperator and intraoperator agreement in the quantitative BPU measure, assessed by intraclass correlation, was 0.92 and 0.98, respectively. Quantitative BPU measures showed either no correlation or weak negative correlation with mammographic percent density. In a model adjusted for body mass index and percent density, higher quantitative BPU was associated with increased risk of breast cancer for both operators (OR = 4.0, 95% confidence interval (CI) 1.6-10.1, and 2.4, 95% CI 1.2-4.7). CONCLUSION: Quantitative measurement of BPU, defined as the ratio of average counts in fibroglandular tissue relative to that in fat, can be reliably performed by nonradiologist operators with a simple region-of-interest analysis tool. Similar to results obtained with subjective BPU categories, quantitative BPU is a functional imaging biomarker of breast cancer risk, independent of mammographic density and hormonal factors.

Automated and real-time segmentation of suspicious breast masses using convolutional neural network.

In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review Board protocol, a prospective study of the automatic segmentation of suspicious breast masses was performed. The cohort consisted of 258 female patients who were clinically identified with suspicious breast masses and underwent clinical US scan and breast biopsy. The computer-aided detection tool effectively segmented the breast masses, achieving a mean Dice coefficient of 0.82, a true positive fraction (TPF) of 0.84, and a false positive fraction (FPF) of 0.01. By avoiding positioning of an initial seed, the algorithm is able to segment images in real time (13-55 ms per image), and can have potential clinical applications. The algorithm is at par with a conventional seeded algorithm, which had a mean Dice coefficient of 0.84 and performs significantly better (P< 0.0001) than the original U-net algorithm.

Improved visualization of breast tissue on a dedicated breast PET system through ergonomic redesign of the imaging table.

BACKGROUND: Breast lesions closer than 2 cm to the chest wall are difficult to position in the field of view of dedicated breast PET (db-PET) systems. This inability to detect such lesions is a significant limitation of these systems. The primary objective of this study was to determine if modifications to the design of the imaging table and detector used for a db-PET system would enable improved visualization of breast tissue close to the chest wall. All studies were performed on a commercially available db-PET system (Mammi-PET). A central square section of the imaging table, containing the standard 180-mm circular aperture, was modified such that it could be removed and replaced by thinner sections with a larger aperture. Additional changes were made to the cover plate of the detector array and the patient mattress. A total of 60 patients were studied. After administration of F-18 FDG, 30 patients were imaged with a 220-mm-diameter aperture and the standard aperture, and 30 patients with a 200-mm aperture and the standard aperture. On all scans, the length of breast tissue in the field of view was measured as the greatest extent of tissue from the nipple back to the posterior edge of the breast. Image quality and patient comfort were recorded. RESULTS: Averaged over both breasts, relative to the standard aperture, the increase in breast length was 12.5 + 7.7 mm with the 220-mm aperture, and 12.3 + 6.5 mm with the 200-mm aperture (p < 0.05 for both apertures). In ~ 5% of cases, the larger apertures resulted in some degradation in image quality due to closer proximity to cardiac/hepatic activity. In 10-20% of cases, movement of the breast tissue was observed as the detector ring was moved to scan the anterior region of the breast. The patient survey indicated no significant difference in the comfort level between the standard aperture and either of the prototype apertures. CONCLUSIONS: Modifications to the image table and system resulted in a significant gain in the volume of breast tissue that could be imaged on the db-PET system and should allow better visualization of lesions close to the chest wall.

Longitudinal Changes in Volumetric Breast Density with Tamoxifen and Aromatase Inhibitors.

Background: Reductions in breast density with tamoxifen and aromatase inhibitors may be an intermediate marker of treatment response. We compare changes in volumetric breast density among breast cancer cases using tamoxifen or aromatase inhibitors (AI) to untreated women without breast cancer.Methods: Breast cancer cases with a digital mammogram prior to diagnosis and after initiation of tamoxifen (n = 366) or AI (n = 403) and a sample of controls (n = 2170) were identified from the Mayo Clinic Mammography Practice and San Francisco Mammography Registry. Volumetric percent density (VPD) and dense breast volume (DV) were measured using Volpara (Matakina Technology) and Quantra (Hologic) software. Linear regression estimated the effect of treatment on annualized changes in density.Results: Premenopausal women using tamoxifen experienced annualized declines in VPD of 1.17% to 1.70% compared with 0.30% to 0.56% for controls and declines in DV of 7.43 to 15.13 cm3 compared with 0.28 to 0.63 cm3 in controls, for Volpara and Quantra, respectively. The greatest reductions were observed among women with ≥10% baseline density. Postmenopausal AI users had greater declines in VPD than controls (Volpara P = 0.02; Quantra P = 0.03), and reductions were greatest among women with ≥10% baseline density. Declines in VPD among postmenopausal women using tamoxifen were only statistically greater than controls when measured with Quantra.Conclusions: Automated software can detect volumetric breast density changes among women on tamoxifen and AI.Impact: If declines in volumetric density predict breast cancer outcomes, these measures may be used as interim prognostic indicators. Cancer Epidemiol Biomarkers Prev; 26(6); 930-7. ©2017 AACR.

Mammographic breast density and risk of breast cancer in women with atypical hyperplasia: an observational cohort study from the Mayo Clinic Benign Breast Disease (BBD) cohort.

BACKGROUND: Atypical hyperplasia (AH) and mammographic breast density (MBD) are established risk factors for breast cancer (BC), but their joint contributions are not well understood. We examine associations of MBD and BC by histologic impression, including AH, in a subcohort of women from the Mayo Clinic Benign Breast Disease Cohort. METHODS: Women with a diagnosis of BBD and mammogram between 1985 and 2001 were eligible. Histologic impression was assessed via pathology review and coded as non-proliferative disease (NP), proliferative disease without atypia (PDWA) and AH. MBD was assessed clinically using parenchymal pattern (PP) or BI-RADS criteria and categorized as low, moderate or high. Percent density (PD) was also available for a subset of women. BC and clinical information were obtained by questionnaires, medical records and the Mayo Clinic Tumor Registry. Women were followed from date of benign biopsy to BC, death or last contact. Standardized incidence ratios (SIRs) compared the observed number of BCs to expected counts. Cox regression estimated multivariate-adjusted MBD hazard ratios. RESULTS: Of the 6271 women included in the study, 1132 (18.0%) had low MBD, 2921 (46.6%) had moderate MBD, and 2218 (35.4%) had high MBD. A total of 3532 women (56.3%) had NP, 2269 (36.2%) had PDWA and 470 (7.5%) had AH. Over a median follow-up of 14.3 years, 528 BCs were observed. The association of MBD and BC risk differed by histologic impression (p-interaction = 0.03), such that there was a strong MBD and BC association among NP (p < 0.001) but non-significant associations for PDWA (p = 0.27) and AH (p = 0.96). MBD and BC associations for AH women were not significant within subsets defined by type of MBD measure (PP vs. BI-RADS), age at biopsy, number of foci of AH, type of AH (lobular vs. ductal) and body mass index, and after adjustment for potential confounding variables. Women with atypia who also had high PD (>50%) demonstrated marginal evidence of increased BC risk (SIR 4.98), but results were not statistically significant. CONCLUSION: We found no evidence of an association between MBD and subsequent BC risk in women with AH.

Correlating Tumor Stiffness with Immunohistochemical Subtypes of Breast Cancers: Prognostic Value of Comb-Push Ultrasound Shear Elastography for Differentiating Luminal Subtypes.

PURPOSE: The purpose of our study is to correlate quantitatively measured tumor stiffness with immunohistochemical (IHC) subtypes of breast cancer. Additionally, the influence of prognostic histologic features (cancer grade, size, lymph node status, and histological type and grade) to the tumor elasticity and IHC profile relationship will be investigated. METHODS: Under an institutional review board (IRB) approved protocol, B-mode ultrasound (US) and comb-push ultrasound shear elastography (CUSE) were performed on 157 female patients with suspicious breast lesions. Out of 157 patients 83 breast cancer patients confirmed by pathology were included in this study. The association between CUSE mean stiffness values and the aforementioned prognostic features of the breast cancer tumors were investigated. RESULTS: Our results demonstrate that the most statistically significant difference (p = 0.0074) with mean elasticity is tumor size. When considering large tumors (size ≥ 8mm), thus minimizing the statistical significance of tumor size, a significant difference (p< 0.05) with mean elasticity is obtained between luminal A of histological grade I and luminal B (Ki-67 > 20%) subtypes. CONCLUSION: Tumor size is an independent factor influencing mean elasticity. The Ki-67 proliferation index and histological grade were dependent factors influencing mean elasticity for the differentiation between luminal subtypes. Future studies on a larger group of patients may broaden the clinical significance of these findings.

Background parenchymal uptake on molecular breast imaging as a breast cancer risk factor: a case-control study.

BACKGROUND: Molecular breast imaging (MBI) is a functional test used for supplemental screening of women with mammographically dense breasts. Additionally, MBI depicts variable levels of background parenchymal uptake (BPU) within nonmalignant, dense fibroglandular tissue. We investigated whether BPU is a risk factor for breast cancer. METHODS: We conducted a retrospective case-control study of 3027 eligible women who had undergone MBI between February 2004 and February 2014. Sixty-two incident breast cancer cases were identified. A total of 179 controls were matched on age, menopausal status, and MBI year. Two radiologists blinded to case status independently assessed BPU as one of four categories: photopenic, minimal to mild, moderate, or marked. Conditional logistic regression analysis was performed to estimate the associations (OR) of BPU categories (moderate or marked vs. minimal to mild or photopenic) and breast cancer risk, adjusted for other risk factors. RESULTS: The median age was 60.2 years (range 38-86 years) for cases vs. 60.2 years (range 38-88 years) for controls (p = 0.88). Women with moderate or marked BPU had a 3.4-fold (95 % CI 1.6-7.3) and 4.8-fold (95 % CI 2.1-10.8) increased risk of breast cancer, respectively, compared with women with photopenic or minimal to mild BPU, for two radiologists. The results were similar after adjustment for BI-RADS density (OR 3.3 [95 % CI 1.6-7.2] and OR 4.6 [95 % CI 2.1-10.5]) or postmenopausal hormone use (OR 3.6 [95 % CI 1.7-7.7] and OR 5.0 [95 % CI 2.2-11.4]). The association of BPU with breast cancer remained in analyses limited to postmenopausal women only (OR 3.8 [95 % CI 1.5-9.3] and OR 4.1 [95 % CI 1.6-10.2]) and invasive breast cancer cases only (OR 3.6 [95 % CI 1.5-8.8] and OR 4.4 [95 % CI 1.7-11.1]). Variable BPU was observed among women with similar mammographic density; the distribution of BPU categories differed across density categories (p < 0.0001). CONCLUSIONS: This study provides the first evidence for BPU as a risk factor for breast cancer. Among women with dense breasts, who comprise >40 % of the screening population, BPU may serve as a functional imaging biomarker to identify the subset at greatest risk.

Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening.

Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted κ statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with κ values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra, and BI-RADS classifications, respectively. Clinical BI-RADS assessment showed better discrimination of case status (C = 0.60; 95% CI: 0.58, 0.61) than did Volpara (C = 0.58; 95% CI: 0.56, 0.59) and Quantra (C = 0.56; 95% CI: 0.54, 0.58) BI-RADS classifications. Conclusion Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts. This could have substantial effects on clinical practice patterns. (©) RSNA, 2015 Online supplemental material is available for this article.

Update on Breast Cancer Detection Using Comb-Push Ultrasound Shear Elastography.

In this work, tissue stiffness estimates are used to differentiate between benign and malignant breast masses in a group of pre-biopsy patients. The rationale is that breast masses are often stiffer than healthy tissue; furthermore, malignant masses are stiffer than benign masses. The comb-push ultrasound shear elastography (CUSE) method is used to noninvasively assess a tissue's mechanical properties. CUSE utilizes a sequence of simultaneous multiple laterally spaced acoustic radiation force (ARF) excitations and detection to reconstruct the region of interest (ROI) shear wave speed map, from which a tissue stiffness property can be quantified. In this study, the tissue stiffnesses of 73 breast masses were interrogated. The mean shear wave speeds for benign masses (3.42 ± 1.32 m/s) were lower than malignant breast masses (6.04 ± 1.25 m/s). These speed values correspond to higher stiffness in malignant breast masses (114.9 ± 40.6 kPa) than benign masses (39.4 ± 28.1 kPa and p <; 0.001), when tissue elasticity is quantified by Young's modulus. A Young's modulus >83 kPa is established as a cut-off value for differentiating between malignant and benign suspicious breast masses, with a receiver operating characteristic curve (ROC) of 89.19% sensitivity, 88.69% specificity, and 0.911 for the area under the curve (AUC).

The contributions of breast density and common genetic variation to breast cancer risk.

We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.

Comb-push ultrasound shear elastography of breast masses: initial results show promise.

PURPOSE OR OBJECTIVE: To evaluate the performance of Comb-push Ultrasound Shear Elastography (CUSE) for classification of breast masses. MATERIALS AND METHODS: CUSE is an ultrasound-based quantitative two-dimensional shear wave elasticity imaging technique, which utilizes multiple laterally distributed acoustic radiation force (ARF) beams to simultaneously excite the tissue and induce shear waves. Female patients who were categorized as having suspicious breast masses underwent CUSE evaluations prior to biopsy. An elasticity estimate within the breast mass was obtained from the CUSE shear wave speed map. Elasticity estimates of various types of benign and malignant masses were compared with biopsy results. RESULTS: Fifty-four female patients with suspicious breast masses from our ongoing study are presented. Our cohort included 31 malignant and 23 benign breast masses. Our results indicate that the mean shear wave speed was significantly higher in malignant masses (6 ± 1.58 m/s) in comparison to benign masses (3.65 ± 1.36 m/s). Therefore, the stiffness of the mass quantified by the Young's modulus is significantly higher in malignant masses. According to the receiver operating characteristic curve (ROC), the optimal cut-off value of 83 kPa yields 87.10% sensitivity, 82.61% specificity, and 0.88 for the area under the curve (AUC). CONCLUSION: CUSE has the potential for clinical utility as a quantitative diagnostic imaging tool adjunct to B-mode ultrasound for differentiation of malignant and benign breast masses.

Preliminary in vivo breast vibro-acoustography results with a quasi-2-d array transducer: a step forward.

We previously investigated the application of a novel imaging modality, vibro-acoustography (VA), using an annular confocal transducer (confocal VA) integrated into a clinical prone stereotactic mammography system, to detect various breast abnormalities. To shorten the scanning time and provide improved coverage of the breast, we have evolved our imaging system by implementing VA on a clinical ultrasound scanner equipped with a ''quasi-2-D'' array transducer. We call this technique ''quasi-2-D vibro-acoustography'' (Q2-DVA). A clinical ultrasound scanner (GE Vivid 7) was modified to perform both ultrasound imaging and VAusing an array transducer consisting of a matrix of 12 rows by 70 columns of ultrasound elements. The newly designed system was used to perform VA on patients with either benign or cancerous lesions. Our results indicate that benign and malignant solid breast lesions were easily detected using our newly modified VA system. It was also possible to detect microcalcifications within the breast. Our results suggest that with further development, Q2-DVA could provide high-resolution diagnostic information in the clinical setting and may be used either as a stand-alone or as a complementary tool in support of other clinical imaging modalities.

Breast vibro-acoustography: initial experience in benign lesions.

BACKGROUND: Vibro-acoustography (VA) is a newly developed imaging technology that is based on low-frequency vibrations induced in the object by the radiation force of ultrasound. VA is sensitive to the dynamic characteristics of tissue. Here, we evaluate the performance of VA in identifying benign lesions and compare the results to those of mammography. METHODS: An integrated mammography-VA system designed for in vivo breast imaging was tested on a group of female volunteers, age ≥ 18 years, with suspected breast lesions based on clinical examination. A set of VA scans was acquired after each corresponding mammography. Most lesions were classified as benign based on their histological results. However, in 4 cases, initial diagnosis based on clinical imaging determined that the lesions were cysts. These cysts were aspirated with needle aspiration and disappeared completely under direct ultrasound visualization. Therefore, no biopsies were performed on these cases and lesions were classified as benign based on clinical findings per clinical standards. To define the VA characteristics of benign breast masses, we adopted the features that are normally attributed to such masses in mammography. In a blinded assessment, three radiologists evaluated the VA images independently. The diagnostic accuracy of VA for detection of benign lesions was assessed by comparing the reviewers' evaluations with clinical data. RESULTS: Out of a total 29 benign lesions in the group, the reviewers were able to locate all lesions on VA images and mammography, 100% with (95% confidence interval (CI): 88% to 100%). Two reviewers were also able to correctly classify 83% (95% CI: 65% to 92%), and the third reviewer 86% (95% CI: 65% to 95%) of lesions, as benign on VA images and 86% (95% CI: 69% to 95%) on mammography. CONCLUSIONS: The results suggest that the mammographic characteristics of benign lesion may also be used to identify such lesions in VA. Furthermore, the results show the ability of VA to detect benign breast abnormalities with a performance comparable to mammography. Therefore, the VA technology has the potential to be utilized as a complementary tool for breast imaging applications. Additional studies are needed to compare the capabilities of VA and traditional ultrasound imaging.