Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women.

BACKGROUND: Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long-term breast cancer risk are limited. We examined LIBRA assessments and breast cancer risk and compared results to prior assessments using Cumulus, an established computer-assisted method requiring manual thresholding. METHODS: We conducted a cohort study among 21,150 non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were 40-74 years at enrollment, followed for up to 10 years, and had archived processed screening mammograms acquired on Hologic or General Electric full-field digital mammography (FFDM) machines and prior Cumulus density assessments available for analysis. Dense area (DA), non-dense area (NDA), and percent density (PD) were assessed using LIBRA software. Cox regression was used to estimate hazard ratios (HRs) for breast cancer associated with DA, NDA and PD modeled continuously in standard deviation (SD) increments, adjusting for age, mammogram year, body mass index, parity, first-degree family history of breast cancer, and menopausal hormone use. We also examined differences by machine type and breast view. RESULTS: The adjusted HRs for breast cancer associated with each SD increment of DA, NDA and PD were 1.36 (95% confidence interval, 1.18-1.57), 0.85 (0.77-0.93) and 1.44 (1.26-1.66) for LIBRA and 1.44 (1.33-1.55), 0.81 (0.74-0.89) and 1.54 (1.34-1.77) for Cumulus, respectively. LIBRA results were generally similar by machine type and breast view, although associations were strongest for Hologic machines and mediolateral oblique views. Results were also similar during the first 2 years, 2-5 years and 5-10 years after the baseline mammogram. CONCLUSION: Associations with breast cancer risk were generally similar for LIBRA and Cumulus density measures and were sustained for up to 10 years. These findings support the suitability of fully automated LIBRA assessments on processed FFDM images for large-scale research on breast density and cancer risk.

Reproductive Factors and Mammographic Density: Associations Among 24,840 Women and Comparison of Studies Using Digitized Film-Screen Mammography and Full-Field Digital Mammography.

Breast density is a modifiable factor that is strongly associated with breast cancer risk. We sought to understand the influence of newer technologies of full-field digital mammography (FFDM) on breast density research and to determine whether results are comparable across studies using FFDM and previous studies using traditional film-screen mammography. We studied 24,840 screening-age (40-74 years) non-Hispanic white women who were participants in the Research Program on Genes, Environment and Health of Kaiser Permanente Northern California and underwent screening mammography with either Hologic (Hologic, Inc., Marlborough, Massachusetts) or General Electric (General Electric Company, Boston, Massachusetts) FFDM machines between 2003 and 2013. We estimated the associations of parity, age at first birth, age at menarche, and menopausal status with percent density and dense area as measured by a single radiological technologist using Cumulus software (Canto Software, Inc., San Francisco, California). We found that associations between reproductive factors and mammographic density measured using processed FFDM images were generally similar in magnitude and direction to those from prior studies using film mammography. Estimated associations for both types of FFDM machines were in the same direction. There was some evidence of heterogeneity in the magnitude of the effect sizes by machine type, which we accounted for using random-effects meta-analysis when combining results. Our findings demonstrate the robustness of quantitative mammographic density measurements across FFDM and film mammography platforms.

Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS.

Purpose To compare three metrics of breast density on full-field digital mammographic (FFDM) images as predictors of future breast cancer risk. Materials and Methods This institutional review board-approved study included 125 women with invasive breast cancer and 274 age- and race-matched control subjects who underwent screening FFDM during 2004-2013 and provided informed consent. The percentage of density and dense area were assessed semiautomatically with software (Cumulus 4.0; University of Toronto, Toronto, Canada), and volumetric percentage of density and dense volume were assessed automatically with software (Volpara; Volpara Solutions, Wellington, New Zealand). Clinical Breast Imaging Reporting and Data System (BI-RADS) classifications of breast density were extracted from mammography reports. Odds ratios and 95% confidence intervals (CIs) were estimated by using conditional logistic regression stratified according to age and race and adjusted for body mass index, parity, and menopausal status, and the area under the receiver operating characteristic curve (AUC) was computed. Results The adjusted odds ratios and 95% CIs for each standard deviation increment of the percentage of density, dense area, volumetric percentage of density, and dense volume were 1.61 (95% CI: 1.19, 2.19), 1.49 (95% CI: 1.15, 1.92), 1.54 (95% CI: 1.12, 2.10), and 1.41 (95% CI: 1.11, 1.80), respectively. Odds ratios for women with extremely dense breasts compared with those with scattered areas of fibroglandular density were 2.06 (95% CI: 0.85, 4.97) and 2.05 (95% CI: 0.90, 4.64) for BI-RADS and Volpara density classifications, respectively. Clinical BI-RADS was more accurate (AUC, 0.68; 95% CI: 0.63, 0.74) than Volpara (AUC, 0.64; 95% CI: 0.58, 0.70) and continuous measures of percentage of density (AUC, 0.66; 95% CI: 0.60, 0.72), dense area (AUC, 0.66; 95% CI: 0.60, 0.72), volumetric percentage of density (AUC, 0.64; 95% CI: 0.58, 0.70), and density volume (AUC, 0.65; 95% CI: 0.59, 0.71), although the AUC differences were not statistically significant. Conclusion Mammographic density on FFDM images was positively associated with breast cancer risk by using the computer assisted methods and BI-RADS. BI-RADS classification was as accurate as computer-assisted methods for discrimination of patients from control subjects. © RSNA, 2016.

Case-control study of mammographic density and breast cancer risk using processed digital mammograms.

BACKGROUND: Full-field digital mammography (FFDM) has largely replaced film-screen mammography in the US. Breast density assessed from film mammograms is strongly associated with breast cancer risk, but data are limited for processed FFDM images used for clinical care. METHODS: We conducted a case-control study nested among non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were aged 40 to 74 years and had screening mammograms acquired on Hologic FFDM machines. Cases (n = 297) were women with a first invasive breast cancer diagnosed after a screening FFDM. For each case, up to five controls (n = 1149) were selected, matched on age and year of FFDM and image batch number, and who were still under follow-up and without a history of breast cancer at the age of diagnosis of the matched case. Percent density (PD) and dense area (DA) were assessed by a radiological technologist using Cumulus. Conditional logistic regression was used to estimate odds ratios (ORs) for breast cancer associated with PD and DA, modeled continuously in standard deviation (SD) increments and categorically in quintiles, after adjusting for body mass index, parity, first-degree family history of breast cancer, breast area, and menopausal hormone use. RESULTS: Median intra-reader reproducibility was high with a Pearson's r of 0.956 (range 0.902 to 0.983) for replicate PD measurements across 23 image batches. The overall mean was 20.02 (SD, 14.61) for PD and 27.63 cm(2) (18.22 cm(2)) for DA. The adjusted ORs for breast cancer associated with each SD increment were 1.70 (95 % confidence interval, 1.41-2.04) for PD, and 1.54 (1.34-1.77) for DA. The adjusted ORs for each quintile were: 1.00 (ref.), 1.49 (0.91-2.45), 2.57 (1.54-4.30), 3.22 (1.91-5.43), 4.88 (2.78-8.55) for PD, and 1.00 (ref.), 1.43 (0.85-2.40), 2.53 (1.53-4.19), 2.85 (1.73-4.69), 3.48 (2.14-5.65) for DA. CONCLUSIONS: PD and DA measured using Cumulus on processed FFDM images are positively associated with breast cancer risk, with similar magnitudes of association as previously reported for film-screen mammograms. Processed digital mammograms acquired for routine clinical care in a general practice setting are suitable for breast density and cancer research.

CT-Guided Wire Localization for Involved Axillary Lymph Nodes After Neo-adjuvant Chemotherapy in Patients With Initially Node-Positive Breast Cancer.

Resection of biopsy-proven involved axillary lymph nodes (iALNs) is important to reduce the false-negative rates of sentinel lymph node (SLN) biopsy after neo-adjuvant chemotherapy (NAC) in patients with initially node-positive breast cancer. Preoperative wire localization for iALNs marked with clips placed during biopsy is a technique that may help the removal of iALNs after NAC. However, ultrasound (US)-guided localization is often difficult because the clips cannot always be reliably visible on US. Computed tomography (CT)-guided wire localization can be used; however, to date there have been no reports on CT-guided wire localization for iALNs. The aim of this study was to describe a series of patients who received CT-guided wire localization for iALN removal after NAC and to evaluate the feasibility of this technique. We retrospectively analyzed five women with initially node-positive breast cancer (age, 41-52 years) who were scheduled for SLN biopsy after NAC and received preoperative CT-guided wire localization for iALNs. CT visualized all the clips that were not identified on post-NAC US. The wire tip was deployed beyond or at the target, with the shortest distance between the wire and the index clip ranging from 0 to 2.5 mm. The total procedure time was 21-38 minutes with good patient tolerance and no complications. In four of five cases, CT wire localization aided in identification and resection of iALNs that were not identified with lymphatic mapping. Residual nodal disease was confirmed in two cases: both had residual disease in wire-localized lymph nodes in addition to SLNs. Although further studies with more cases are required, our results suggest that CT-guided wire localization for iALNs is a feasible technique that facilitates identification and removal of the iALNs as part of SLN biopsy after NAC in situations where US localization is unsuccessful.

Impact of Breast Density Legislation on Breast Cancer Risk Assessment and Supplemental Screening: A Survey of 110 Radiology Facilities.

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.

Variable spatiotemporal resolution three-dimensional Dixon sequence for rapid dynamic contrast-enhanced breast MRI.

PURPOSE: To investigate a new variable spatiotemporal resolution dynamic contrast-enhanced (DCE) MRI method termed DIfferential Subsampling with Cartesian Ordering (DISCO), for imaging of breast cancer. MATERIALS AND METHODS: DISCO combines variable density, pseudorandom k-space segmentation and two-point Dixon fat-water separation for high spatiotemporal resolution breast DCE MRI. During the contrast wash-in phase, view sharing is used to achieve high temporal resolution. Forty patients referred for breast MRI were imaged, 26 using the proposed DISCO sequence and 14 using a conventional low-spatial-resolution dynamic sequence (VIBRANT-FLEX) on a 3 Tesla scanner. DISCO dynamic images from 14 patients were compared with VIBRANT-FLEX images from 14 other patients. The image quality assessed by radiologist image ranking in a blinded manner, and the temporal characteristics of the two sequences were compared. RESULTS: A spatial resolution of 1.1 × 1.1 × 1.2 mm(3) (160 slices, 28 cm field of view) was achieved with axial bilateral coverage in 120 s. Dynamic images with ∼ 9 s effective temporal resolution were generated during the 2-min contrast wash-in phase. The image quality of DISCO dynamic images ranked significantly higher than low spatial resolution VIBRANT-FLEX images (19.5 versus 9.5, Mann-Whitney U-test P = 0.00914), with no significant differences in the maximum slope of aortic enhancement. CONCLUSION: DISCO is a promising variable-spatiotemporal-resolution imaging sequence for capturing the dynamics of rapidly enhancing tumors as well as structural features postcontrast. A near 1-mm isotropic spatial resolution was achieved with postcontrast static phase images in 120 s and dynamic phase images acquired in 9 s per phase.

3D T2-weighted spin echo imaging in the breast.

PURPOSE: To evaluate the performance of 2D versus 3D T2-weighted spin echo imaging in the breast. MATERIALS AND METHODS: 2D and 3D T2-weighted images were acquired in 25 patients as part of a clinically indicated breast magnetic resonance imaging (MRI) exam. Lesion-to-fibroglandular tissue signal ratio was measured in 16 identified lesions. Clarity of lesion morphology was assessed through a blinded review by three radiologists. Instances demonstrating the potential diagnostic contribution of 3D versus 2D T2-weighted imaging in the breast were noted through unblinded review by a fourth radiologist. RESULTS: The lesion-to-fibroglandular tissue signal ratio was well correlated between 2D and 3D T2-weighted images (R(2) = 0.93). Clarity of lesion morphology was significantly better with 3D T2-weighted imaging for all observers based on a McNemar test (P ≤ 0.02, P ≤ 0.01, P ≤ 0.03). Instances indicating the potential diagnostic contribution of 3D T2-weighted imaging included improved depiction of signal intensity and improved alignment between DCE and T2-weighted findings. CONCLUSION: In this pilot study, 3D T2-weighted imaging provided comparable contrast and improved depiction of lesion morphology in the breast in comparison to 2D T2-weighted imaging. Based on these results further investigation to determine the diagnostic impact of 3D T2-weighted imaging in breast MRI is warranted.

The California breast density information group: a collaborative response to the issues of breast density, breast cancer risk, and breast density notification legislation.

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.

Freehand MRI-guided preoperative needle localization of breast lesions after MRI-guided vacuum-assisted core needle biopsy without marker placement.

PURPOSE: To evaluate the feasibility of magnetic resonance imaging (MRI)-guided preoperative needle localization (PNL) of breast lesions previously sampled by MRI-guided vacuum-assisted core needle biopsy (VACNB) without marker placement. MATERIALS AND METHODS: We reviewed 15 women with 16 breast lesions undergoing MRI-guided VACNB without marker placement who subsequently underwent MRI-guided PNL, both on an open 0.5T magnet using freehand techniques. Mammograms and specimen radiographs were rated for lesion visibility; MRI images were rated for lesion visibility and hematoma formation. Imaging findings were correlated with pathology. RESULTS: The average prebiopsy lesion size was 16 mm (range 4-50 mm) with 13/16 lesions located in mammographically dense breasts. Eight hematomas formed during VACNB (average size 13 mm, range 8-19 mm). PNL was performed for VACNB pathologies of cancer (5), high-risk lesions (5), or benign but discordant findings (6) at 2-78 days following VACNB. PNL targeted the lesion (2), hematoma (4), or surrounding breast architecture (10). Wire placement was successful in all 16 lesions. Final pathology showed six cancers, five high-risk lesions, and five benign findings. CONCLUSION: MRI-guided PNL is successful in removing lesions that have previously undergone VACNB without marker placement by targeting the residual lesion, hematoma, or surrounding breast architecture, even in mammographically dense breasts.

Alcohol and Tobacco Use in Relation to Mammographic Density in 23,456 Women.

BACKGROUND: Percent density (PD) is a strong risk factor for breast cancer that is potentially modifiable by lifestyle factors. PD is a composite of the dense (DA) and nondense (NDA) areas of a mammogram, representing predominantly fibroglandular or fatty tissues, respectively. Alcohol and tobacco use have been associated with increased breast cancer risk. However, their effects on mammographic density (MD) phenotypes are poorly understood. METHODS: We examined associations of alcohol and tobacco use with PD, DA, and NDA in a population-based cohort of 23,456 women screened using full-field digital mammography machines manufactured by Hologic or General Electric. MD was measured using Cumulus. Machine-specific effects were estimated using linear regression, and combined using random effects meta-analysis. RESULTS: Alcohol use was positively associated with PD (P trend = 0.01), unassociated with DA (P trend = 0.23), and inversely associated with NDA (P trend = 0.02) adjusting for age, body mass index, reproductive factors, physical activity, and family history of breast cancer. In contrast, tobacco use was inversely associated with PD (P trend = 0.0008), unassociated with DA (P trend = 0.93), and positively associated with NDA (P trend<0.0001). These trends were stronger in normal and overweight women than in obese women. CONCLUSIONS: These findings suggest that associations of alcohol and tobacco use with PD result more from their associations with NDA than DA. IMPACT: PD and NDA may mediate the association of alcohol drinking, but not tobacco smoking, with increased breast cancer risk. Further studies are needed to elucidate the modifiable lifestyle factors that influence breast tissue composition, and the important role of the fatty tissues on breast health.

Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk.

Mammographic density (MD) phenotypes are strongly associated with breast cancer risk and highly heritable. In this GWAS meta-analysis of 24,192 women, we identify 31 MD loci at P < 5 × 10-8, tripling the number known to 46. Seventeen identified MD loci also are associated with breast cancer risk in an independent meta-analysis (P < 0.05). Mendelian randomization analyses show that genetic estimates of dense area (DA), nondense area (NDA), and percent density (PD) are all significantly associated with breast cancer risk (P < 0.05). Pathway analyses reveal distinct biological processes involving DA, NDA and PD loci. These findings provide additional insights into the genetic basis of MD phenotypes and their associations with breast cancer risk.

Age at Menarche and Late Adolescent Adiposity Associated with Mammographic Density on Processed Digital Mammograms in 24,840 Women.

Background: High mammographic density is strongly associated with increased breast cancer risk. Some, but not all, risk factors for breast cancer are also associated with higher mammographic density.Methods: The study cohort (N = 24,840) was drawn from the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California and included non-Hispanic white females ages 40 to 74 years with a full-field digital mammogram (FFDM). Percent density (PD) and dense area (DA) were measured by a radiological technologist using Cumulus. The association of age at menarche and late adolescent body mass index (BMI) with PD and DA were modeled using linear regression adjusted for confounders.Results: Age at menarche and late adolescent BMI were negatively correlated. Age at menarche was positively associated with PD (P value for trend <0.0001) and DA (P value for trend <0.0001) in fully adjusted models. Compared with the reference category of ages 12 to 13 years at menarche, menarche at age >16 years was associated with an increase in PD of 1.47% (95% CI, 0.69-2.25) and an increase in DA of 1.59 cm2 (95% CI, 0.48-2.70). Late adolescent BMI was inversely associated with PD (P < 0.0001) and DA (P < 0.0001) in fully adjusted models.Conclusions: Age at menarche and late adolescent BMI are both associated with Cumulus measures of mammographic density on processed FFDM images.Impact: Age at menarche and late adolescent BMI may act through different pathways. The long-term effects of age at menarche on cancer risk may be mediated through factors besides mammographic density. Cancer Epidemiol Biomarkers Prev; 26(9); 1450-8. ©2017 AACR.

Automatic abstraction of imaging observations with their characteristics from mammography reports.

BACKGROUND: Radiology reports are usually narrative, unstructured text, a format which hinders the ability to input report contents into decision support systems. In addition, reports often describe multiple lesions, and it is challenging to automatically extract information on each lesion and its relationships to characteristics, anatomic locations, and other information that describes it. The goal of our work is to develop natural language processing (NLP) methods to recognize each lesion in free-text mammography reports and to extract its corresponding relationships, producing a complete information frame for each lesion. MATERIALS AND METHODS: We built an NLP information extraction pipeline in the General Architecture for Text Engineering (GATE) NLP toolkit. Sequential processing modules are executed, producing an output information frame required for a mammography decision support system. Each lesion described in the report is identified by linking it with its anatomic location in the breast. In order to evaluate our system, we selected 300 mammography reports from a hospital report database. RESULTS: The gold standard contained 797 lesions, and our system detected 815 lesions (780 true positives, 35 false positives, and 17 false negatives). The precision of detecting all the imaging observations with their modifiers was 94.9, recall was 90.9, and the F measure was 92.8. CONCLUSIONS: Our NLP system extracts each imaging observation and its characteristics from mammography reports. Although our application focuses on the domain of mammography, we believe our approach can generalize to other domains and may narrow the gap between unstructured clinical report text and structured information extraction needed for data mining and decision support.

Phase II Study of Gemcitabine, Carboplatin, and Iniparib As Neoadjuvant Therapy for Triple-Negative and BRCA1/2 Mutation-Associated Breast Cancer With Assessment of a Tumor-Based Measure of Genomic Instability: PrECOG 0105.

PURPOSE: This study was designed to assess efficacy, safety, and predictors of response to iniparib in combination with gemcitabine and carboplatin in early-stage triple-negative and BRCA1/2 mutation-associated breast cancer. PATIENTS AND METHODS: This single-arm phase II study enrolled patients with stage I to IIIA (T ≥ 1 cm) estrogen receptor-negative (≤ 5%), progesterone receptor-negative (≤ 5%), and human epidermal growth factor receptor 2-negative or BRCA1/2 mutation-associated breast cancer. Neoadjuvant gemcitabine (1,000 mg/m(2) intravenously [IV] on days 1 and 8), carboplatin (area under curve of 2 IV on days 1 and 8), and iniparib (5.6 mg/kg IV on days 1, 4, 8, and 11) were administered every 21 days for four cycles, until the protocol was amended to six cycles. The primary end point was pathologic complete response (no invasive carcinoma in breast or axilla). All patients underwent comprehensive BRCA1/2 genotyping, and homologous recombination deficiency was assessed by loss of heterozygosity (HRD-LOH) in pretreatment core breast biopsies. RESULTS: Among 80 patients, median age was 48 years; 19 patients (24%) had germline BRCA1 or BRCA2 mutations; clinical stage was I (13%), IIA (36%), IIB (36%), and IIIA (15%). Overall pathologic complete response rate in the intent-to-treat population (n = 80) was 36% (90% CI, 27 to 46). Mean HRD-LOH scores were higher in responders compared with nonresponders (P = .02) and remained significant when BRCA1/2 germline mutations carriers were excluded (P = .021). CONCLUSION: Preoperative combination of gemcitabine, carboplatin, and iniparib is active in the treatment of early-stage triple-negative and BRCA1/2 mutation-associated breast cancer. The HRD-LOH assay was able to identify patients with sporadic triple-negative breast cancer lacking a BRCA1/2 mutation, but with an elevated HRD-LOH score, who achieved a favorable pathologic response. Confirmatory controlled trials are warranted.

Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer.

OBJECTIVE: To predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) using features derived from dynamic contrast-enhanced (DCE) MRI. MATERIALS AND METHODS: 60 patients with triple-negative early-stage breast cancer receiving NAC were evaluated. Features assessed included clinical data, patterns of tumor response to treatment determined by DCE-MRI, MRI breast imaging-reporting and data system descriptors, and quantitative lesion kinetic texture derived from the gray-level co-occurrence matrix (GLCM). All features except for patterns of response were derived before chemotherapy; GLCM features were determined before and after chemotherapy. Treatment response was defined by the presence of residual invasive tumor and/or positive lymph nodes after chemotherapy. Statistical modeling was performed using Lasso logistic regression. RESULTS: Pre-chemotherapy imaging features predicted all measures of response except for residual tumor. Feature sets varied in effectiveness at predicting different definitions of treatment response, but in general, pre-chemotherapy imaging features were able to predict pathological complete response with area under the curve (AUC)=0.68, residual lymph node metastases with AUC=0.84 and residual tumor with lymph node metastases with AUC=0.83. Imaging features assessed after chemotherapy yielded significantly improved model performance over those assessed before chemotherapy for predicting residual tumor, but no other outcomes. CONCLUSIONS: DCE-MRI features can be used to predict whether triple-negative breast cancer patients will respond to NAC. Models such as the ones presented could help to identify patients not likely to respond to treatment and to direct them towards alternative therapies.

Updates and revisions to the BI-RADS magnetic resonance imaging lexicon.

This article summarizes the updates and revisions to the second edition of the BI-RADS MRI lexicon. A new feature in the lexicon is background parenchymal enhancement and its descriptors. Another major focus is on revised terminology for masses and non-mass enhancement. A section on breast implants and associated lexicon terms has also been added. Because diagnostic breast imaging increasingly includes multimodality evaluation, the new edition of the lexicon also contains revised recommendations for combined reporting with mammography and ultrasound if these modalities are included as comparison, and clarification on the use of final assessment categories in MR imaging.

Qualitative and quantitative image-based biomarkers of therapeutic response in triple-negative breast cancer.

Experimental targeted treatments for neoadjuvant chemotherapy for triple-negative breast cancer are currently underway, and a current challenge is predicting which patients will respond to these therapies. In this study, we use data from dynamic contrast-enhanced MRI (DCE-MRI) images to predict whether patients with triple negative breast cancer will respond to an experimental neoadjuvant chemotherapy regimen. Using pre-therapy image-based features that are both qualitative (e.g., morphological BI-RADS categories) and quantitative (e.g., lesion texture), we built a model that was able to predict whether patients will have residual invasive cancer with lymph nodes metastases following therapy (receiver operating characteristic area under the curve of 0.83, sensitivity=0.73, specificity=0.83). This model's performance is at a level that is potentially clinically valuable for predicting which patients may or may not benefit from similar treatments in the future.