A Population-Based Study of Genes Previously Implicated in Breast Cancer.

BACKGROUND: Population-based estimates of the risk of breast cancer associated with germline pathogenic variants in cancer-predisposition genes are critically needed for risk assessment and management in women with inherited pathogenic variants. METHODS: In a population-based case-control study, we performed sequencing using a custom multigene amplicon-based panel to identify germline pathogenic variants in 28 cancer-predisposition genes among 32,247 women with breast cancer (case patients) and 32,544 unaffected women (controls) from population-based studies in the Cancer Risk Estimates Related to Susceptibility (CARRIERS) consortium. Associations between pathogenic variants in each gene and the risk of breast cancer were assessed. RESULTS: Pathogenic variants in 12 established breast cancer-predisposition genes were detected in 5.03% of case patients and in 1.63% of controls. Pathogenic variants in BRCA1 and BRCA2 were associated with a high risk of breast cancer, with odds ratios of 7.62 (95% confidence interval [CI], 5.33 to 11.27) and 5.23 (95% CI, 4.09 to 6.77), respectively. Pathogenic variants in PALB2 were associated with a moderate risk (odds ratio, 3.83; 95% CI, 2.68 to 5.63). Pathogenic variants in BARD1, RAD51C, and RAD51D were associated with increased risks of estrogen receptor-negative breast cancer and triple-negative breast cancer, whereas pathogenic variants in ATM, CDH1, and CHEK2 were associated with an increased risk of estrogen receptor-positive breast cancer. Pathogenic variants in 16 candidate breast cancer-predisposition genes, including the c.657_661del5 founder pathogenic variant in NBN, were not associated with an increased risk of breast cancer. CONCLUSIONS: This study provides estimates of the prevalence and risk of breast cancer associated with pathogenic variants in known breast cancer-predisposition genes in the U.S. population. These estimates can inform cancer testing and screening and improve clinical management strategies for women in the general population with inherited pathogenic variants in these genes. (Funded by the National Institutes of Health and the Breast Cancer Research Foundation.).

Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case-control study.

BACKGROUND: This study investigates whether quantitative breast density (BD) serves as an imaging biomarker for more intensive breast cancer screening by predicting interval, and node-positive cancers. METHODS: This case-control study of 1204 women aged 47-73 includes 599 cancer cases (302 screen-detected, 297 interval; 239 node-positive, 360 node-negative) and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection (screen-detected or interval); node-negative cancers; node-positive cancers, and all cancers vs. controls. RESULTS: FGV, VBD, VAS, and DG all discriminated interval cancers (all p < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers (p < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 (p < 0.01) as did VBD (0.63 and 0.53, respectively, p < 0.001). CONCLUSION: FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk.

Modeling the natural history of ductal carcinoma in situ based on population data.

BACKGROUND: The incidence of ductal carcinoma in situ (DCIS) has increased substantially since the introduction of mammography screening. Nevertheless, little is known about the natural history of preclinical DCIS in the absence of biopsy or complete excision. METHODS: Two well-established population models evaluated six possible DCIS natural history submodels. The submodels assumed 30%, 50%, or 80% of breast lesions progress from undetectable DCIS to preclinical screen-detectable DCIS; each model additionally allowed or prohibited DCIS regression. Preclinical screen-detectable DCIS could also progress to clinical DCIS or invasive breast cancer (IBC). Applying US population screening dissemination patterns, the models projected age-specific DCIS and IBC incidence that were compared to Surveillance, Epidemiology, and End Results data. Models estimated mean sojourn time (MST) in the preclinical screen-detectable DCIS state, overdiagnosis, and the risk of progression from preclinical screen-detectable DCIS. RESULTS: Without biopsy and surgical excision, the majority of DCIS (64-100%) in the preclinical screen-detectable state progressed to IBC in submodels assuming no DCIS regression (36-100% in submodels allowing for DCIS regression). DCIS overdiagnosis differed substantially between models and submodels, 3.1-65.8%. IBC overdiagnosis ranged 1.3-2.4%. Submodels assuming DCIS regression resulted in a higher DCIS overdiagnosis than submodels without DCIS regression. MST for progressive DCIS varied between 0.2 and 2.5 years. CONCLUSIONS: Our findings suggest that the majority of screen-detectable but unbiopsied preclinical DCIS lesions progress to IBC and that the MST is relatively short. Nevertheless, due to the heterogeneity of DCIS, more research is needed to understand the progression of DCIS by grades and molecular subtypes.

Cancer Yield and Patterns of Follow-up for BI-RADS Category 3 after Screening Mammography Recall in the National Mammography Database.

Background The literature supports the use of short-interval follow-up as an alternative to biopsy for lesions assessed as probably benign, Breast Imaging Reporting and Data System (BI-RADS) category 3, with an expected malignancy rate of less than 2%. Purpose To assess outcomes from 6-, 12-, and 24-month follow-up of probably benign findings first identified at recall from screening mammography in the National Mammography Database (NMD). Materials and Methods This retrospective study included women recalled from screening mammography with BI-RADS category 3 assessment at additional evaluation from January 2009 through March 2018 from 471 NMD facilities. Only the first BI-RADS category 3 occurrence for women aged 25 years or older with no personal history of breast cancer was analyzed, with biopsy or 2-year imaging follow-up. Cancer yield and positive predictive value of biopsies performed (PPV3) were determined at each follow-up. Results Among 45 202 women (median age, 55 years; range, 25-90 years) with a BI-RADS category 3 lesion, 1574 (3.5%) underwent biopsy at the time of lesion detection, yielding 72 cancers (cancer yield, 4.6%; 72 of 1574 women). For the remaining 43 628 women who accepted surveillance, 922 were seen within 90 days (with 78 lesions biopsied and 12 [15%] classified as malignant). The women still in surveillance (31 465 of 43 381 women [72.5%]) underwent follow-up mammography at 6 months. Of 3001 (9.5%) lesions biopsied, 456 (15.2%) were malignant (cancer yield, 1.5%; 456 of 31 465 women; 95% confidence interval [CI]: 1.3%, 1.6%). Among 18 748 of 25 997 women (72.1%) in surveillance who underwent follow-up at 12 months, 1219 (6.5%) underwent biopsy with 230 (18.9%) malignant lesions found (cancer yield, 1.2%; 230 of 18 748 women; 95% CI: 1.1%, 1.4%). Through 2-year follow-up, the biopsy rate was 11.2% (4894 of 43 628 women) with a cancer yield of 1.86% (810 malignancies found among 43 628 women; 95% CI: 1.73%, 1.98%) and a PPV3 of 16.6% (810 malignancies found among 4894 women). Conclusion In the National Mammography Database, Breast Imaging Reporting and Data System (BI-RADS) category 3 use is appropriate, with 1.86% cumulative cancer yield through 2-year follow-up. Of 810 malignancies, 468 (57.8%) were diagnosed at or before 6 months, validating necessity of short-interval follow-up of mammographic BI-RADS category 3 findings. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Moy in this issue.

Core Elements of Shared Decision-making for Women Considering Breast Cancer Screening: Results of a Modified Delphi Survey.

BACKGROUND: The United States Preventive Services Task Force recommends individualized breast cancer screening for average-risk women before age 50, advised by risk assessment and shared decision-making (SDM). However, the foundational principles of this recommendation that would inform decision support tools for patients and primary care physicians at the point of care have not been codified. Determining the core elements of SDM for breast cancer screening as valued by patients and primary care providers (PCPs) is necessary for implementing effective SDM tools. The aim of this study is to affirm core elements of SDM in the context of clinical interactions, through a Delphi consensus process. METHODS: A Delphi was conducted with 30 participants (10 women aged 40-49, 10 PCPs, and 10 healthcare decision scientists), to codify core elements of breast cancer screening SDM. The criterion for establishing consensus was a threshold of 80% agreement. The Delphi concluded with an 83% response rate. RESULTS: Of 48 items fielded, 44 met the threshold on the high-importance end of the response scale and were accepted as core elements. Core elements across three thematic categories-information delivery and patient education, interpersonal clinician-patient communication, and framework of the decision-received panelists' support in nearly equal measure. Panelists unanimously agreed that SDM should include provision of clearly understandable information, including that of personal breast cancer risk factors, and benefits and harms of mammography screening, and that PCPs should convey they are listening, knowledgeable, and demonstrate cultural sensitivity. DISCUSSION: This research codifies the core elements of SDM for mammography in women 40-49, augmenting the evidence to inform discussions between patients and physicians. These core elements of SDM have the potential to operationalize SDM for breast cancer screening in an effort to improve public health outcomes.

Framing the Clinical Encounter: Shared Decision-Making, Mammography Screening, and Decision Satisfaction.

The study examines whether physicians' framing of clinical interactions is related to patient shared decision-making (SDM) satisfaction when using a clinical decision support tool (CDST) concerning mammographic screening. To answer this question, we combined (a) system log data from a CDST, (b) content coding of the physicians' message framing while using the CDST, and (c) a post-visit patient survey to assess SDM satisfaction concerning screening mammography. Results suggest that two types of message frames - consequence frames and numerical frames - moderated the relationship of the CDST on SDM satisfaction. When the CDST displayed low risk of breast cancer for a patient, physicians were able to improve the cognitive aspects of SDM satisfaction by framing the consequences of mammography screening in positive terms. However, when the physician delivered the numerical information in relative, rather than absolute terms, the patient's SDM satisfaction was reduced. Our study advances previous message framing effect research in health communication from experimental settings to clinical encounters. It also discusses the importance of delivering risk-congruent frames in clinical settings.

Age-based versus Risk-based Mammography Screening in Women 40-49 Years Old: A Cross-sectional Study.

Background Risk-based screening in women 40-49 years old has not been evaluated in routine screening mammography practice. Purpose To use a cross-sectional study design to compare the trade-offs of risk-based and age-based screening for women 45 years of age or older to determine short-term outcomes. Materials and Methods A retrospective cross-sectional study was performed by using a database of 20 539 prospectively interpreted consecutive digital screening mammograms in 10 280 average-risk women aged 40-49 years who were screened at an academic medical center between January 1, 2006, and December 31, 2013. Two hypothetical screening scenarios were compared: an age-based (≥45 years) scenario versus a risk-based (a 5-year risk of breast cancer greater than that of an average 50-year-old) scenario. Risk factors for risk-based screening included family history, race, age, prior breast biopsy, and breast density. Outcomes included breast cancers detected at mammography, false-positive mammograms, and benign biopsy findings. Short-term outcomes were compared by using the χ2 test. Results The screening population included 71 148 screening mammograms in 24 928 women with a mean age of 55.5 years ± 8.9 (standard deviation) (age range, 40-74 years). In women 40-49 years old, usual care included 50 screening-detected cancers, 1787 false-positive mammograms, and 384 benign biopsy results. The age-based (≥45 years) screening strategy revealed more cancers than did the risk-based strategy (34 [68%] vs 13 [26%] of 50; P < .001), while prompting more false-positive mammograms (899 [50.3%] vs 216 [12.1%] of 1787; P < .001) and benign biopsy results (175 [45.6%] vs 49 [12.8%] of 384; P < .001). The risk-based strategy demonstrated low levels of eligibility (few screenings) in the 40-44-year age group. Differences in outcomes in the 45-49-year age group explained the overall hypothetical screening strategy differences. Conclusion Risk-based screening for women 40-49 years old includes few women in the 40-44-year age range. Significant trade-offs in the 45-49-year age group explain the overall difference between hypothetical screening scenarios, both of which reduce the benefits as well as the harms of mammography for women 40-49 years old. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Joe and Hayward in this issue.

Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening.

PURPOSE: Due to limitations in the ability to identify non-progressive disease, ductal carcinoma in situ (DCIS) is usually managed similarly to localized invasive breast cancer. We used simulation modeling to evaluate the potential impact of a hypothetical test that identifies non-progressive DCIS. METHODS: A discrete-event model simulated a cohort of U.S. women undergoing digital screening mammography. All women diagnosed with DCIS underwent the hypothetical DCIS prognostic test. Women with test results indicating progressive DCIS received standard breast cancer treatment and a decrement to quality of life corresponding to the treatment. If the DCIS test indicated non-progressive DCIS, no treatment was received and women continued routine annual surveillance mammography. A range of test performance characteristics and prevalence of non-progressive disease were simulated. Analysis compared discounted quality-adjusted life years (QALYs) and costs for test scenarios to base-case scenarios without the test. RESULTS: Compared to the base case, a perfect prognostic test resulted in a 40% decrease in treatment costs, from $13,321 to $8005 USD per DCIS case. A perfect test produced 0.04 additional QALYs (16 days) for women diagnosed with DCIS, added to the base case of 5.88 QALYs per DCIS case. The results were sensitive to the performance characteristics of the prognostic test, the proportion of DCIS cases that were non-progressive in the model, and the frequency of mammography screening in the population. CONCLUSION: A prognostic test that identifies non-progressive DCIS would substantially reduce treatment costs but result in only modest improvements in quality of life when averaged over all DCIS cases.

Association between Screening Mammography Recall Rate and Interval Cancers in the UK Breast Cancer Service Screening Program: A Cohort Study.

Purpose To determine whether low levels of recall lead to increased interval cancers and the magnitude of this effect. Materials and Methods The authors retrospectively analyzed prospectively collected data from the UK National Health Service Breast Screening Programme during a 36-month period (April 1, 2005 to March 31, 2008), with 3-year follow-up in women aged 50-70 years. Data on recall, cancers detected at screening, and interval cancers were available for each of the 84 breast screening units and for each year (n = 252). The association between interval cancers and recalls was modeled by using Poisson regression on aggregated data and according to age (5-year intervals) and screening type (prevalent vs incident). Results The authors analyzed 5 126 689 screening episodes, demonstrating an average recall to assessment rate (RAR) of 4.56% (range, 1.64%-8.42%; standard deviation, 1.15%), cancer detection rate of 8.1 per 1000 women screened, and interval cancer rate (ICR) of 3.1 per 1000 women screened. Overall, a significant negative association was found between RAR and ICR (Poisson regression coefficient: -0.039 [95% confidence interval: -0.062, -0.017]; P = .001), with approximately one fewer interval cancer for every additional 80-84 recalls. Subgroup analysis revealed similar negative correlations in women aged 50-54 years (P = .002), 60-64 years (P = .01), and 65-69 years (P = .008) as well as in incident screens (P = .001) and prevalent screens (P = .04). No significant relationship was found in women aged 55-59 years (P = .46). Conclusion There was a statistically significant negative correlation between RAR and ICR, which suggests the merit of a minimum threshold for RAR. © RSNA, 2018 Online supplemental material is available for this article.

ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database.

Purpose To determine the utilization and positive predictive value (PPV) of the American College of Radiology (ACR) Breast Imaging Data and Reporting System (BI-RADS) category 4 subdivisions in diagnostic mammography in the National Mammography Database (NMD). Materials and Methods This study involved retrospective review of diagnostic mammography data submitted to the NMD from January 1, 2008 to December 30, 2014. Utilization rates of BI-RADS category 4 subdivisions were compared by year, facility (type, location, census region), and examination (indication, finding type) characteristics. PPV3 (positive predictive value for biopsies performed) was calculated overall and according to category 4 subdivision. The χ2 test was used to test for significant associations. Results Of 1 309 950 diagnostic mammograms, 125 447 (9.6%) were category 4, of which 33.3% (41 841 of 125 447) were subdivided. Subdivision utilization rates were higher (P < .001) in practices that were community, suburban, or in the West; for examination indication of prior history of breast cancer; and for the imaging finding of architectural distortion. Of 41 841 category 4 subdivided examinations, 4A constituted 55.6% (23 258 of 41 841) of the examinations; 4B, 31.8% (13 302 of 41 841) of the examinations; and 4C, 12.6% (5281 of 41 841) of the examinations. Pathologic outcomes were available in 91 563 examinations, and overall category 4 PPV3 was 21.1% (19 285 of 91 563). There was a statistically significant difference in PPV3 according to category 4 subdivision (P < .001): The PPV of 4A was 7.6% (1274 of 16 784), that of 4B was 22% (2317 of 10 408), and that of 4C was 69.3% (2839 of 4099). Conclusion Although BI-RADS suggests their use, subdivisions were utilized in the minority (33.3% [41 841 of 125 447]) of category 4 diagnostic mammograms, with variability based on facility and examination characteristics. When subdivisions were used, PPV3s were in BI-RADS-specified malignancy ranges. This analysis supports the use of subdivisions in broad practice and, given benefits for patient care, should motivate increased utilization. © RSNA, 2018 Online supplemental material is available for this article.

A National Study of the Use of Asymptomatic Systemic Imaging for Surveillance Following Breast Cancer Treatment (AFT-01).

BACKGROUND: Although not guideline recommended, studies suggest 50% of locoregional breast cancer patients undergo systemic imaging during follow-up, prompting its inclusion as a Choosing Wisely measure of potential overuse. Most studies rely on administrative data that cannot delineate scan intent (prompted by signs/symptoms vs. asymptomatic surveillance). This is a critical gap as intent is the only way to distinguish overuse from appropriate care. OBJECTIVE: Our aim was to assess surveillance systemic imaging post-breast cancer treatment in a national sample accounting for scan intent. METHODS: A stage-stratified random sample of 10 women with stage II-III breast cancer in 2006-2007 was selected from each of 1217 Commission on Cancer-accredited facilities, for a total of 10,838 patients. Registrars abstracted scan type (computed tomography [CT], non-breast magnetic resonance imaging, bone scan, positron emission tomography/CT) and intent (cancer-related vs. not, asymptomatic surveillance vs. not) from medical records for 5 years post-diagnosis. Data were merged with each patient's corresponding National Cancer Database record, containing sociodemographic and tumor/treatment information. RESULTS: Of 10,838 women, 30% had one or more, and 12% had two or more, systemic surveillance scans during a 4-year follow-up period. Patients were more likely to receive surveillance imaging in the first follow-up year (lower proportions during subsequent years) and if they had estrogen receptor/progesterone receptor-negative tumors. CONCLUSIONS: Locoregional breast cancer patients undergo asymptomatic systemic imaging during follow-up despite guidelines recommending against it, but at lower rates than previously reported. Providers appear to use factors that confer increased recurrence risk to tailor decisions about systemic surveillance imaging, perhaps reflecting limitations of data on which current guidelines are based. ClinicalTrials.gov Identifier: NCT02171078.

Key Elements of Mammography Shared Decision-Making: a Scoping Review of the Literature.

BACKGROUND: New guidelines recommend shared decision-making (SDM) for women and their clinician in consideration of breast cancer screening, particularly for women ages 35-50 where guidelines for routine mammography are controversial. A number of models offer general guidelines for SDM across clinical practice, yet they do not offer specific guidance about conducting SDM in mammography. We conducted a scoping review of the literature to identify the key elements of breast cancer screening SDM and synthesize these key elements for utilization by primary care clinicians. METHODS: The Cochrane Database of Systematic Reviews; Cumulative Index to Nursing and Allied Health Literature (CINAHL Plus); PsycInfo, PubMed (MEDLINE), Scopus, and SocIndex databases were searched. Inclusion criteria were original studies from peer-reviewed publications (from 2009 or later) reporting breast cancer screening (mammography), medical decision-making, and patient-centered care. Study populations needed to include female patients 18+ years of age facing a real-life breast cancer screening decision. Article findings were specific to shared decision-making and/or use of a decision aid. Data extracted includes study design, population, setting, intervention, and critical findings related to breast cancer screening SDM elements. Scoping analysis includes descriptive analysis of study features and content analysis to identify the SDM key elements. RESULTS: Twenty-four articles were retained. Three thematic categories of key elements emerged from the extracted elements: information delivery/patient education (specific content and delivery modes), interpersonal clinician-patient communication (aspects of interpersonal relationship impacting SDM), and framework of the decision (sociocultural factors beyond direct SDM deliberation). A number of specific breast cancer screening SDM elements relevant to primary care clinical practice are delineated. DISCUSSION: The findings underscore the importance of the relationship between the patient and clinician and the necessity of spelling out each step in the SDM process. The clinician needs to be explicit in telling a woman that she has a choice about whether to get a mammogram and the benefits and harms of screening mammography. Finally, clinicians need to be aware of sociocultural factors that can influence their relationships and their patients' decision-making processes and attempt to identify and address these factors.

Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports.

After years of development, the RadLex terminology contains a large set of controlled terms for the radiology domain, but gaps still exist. We developed a data-driven approach to discover new terms for RadLex by mining a large corpus of radiology reports using natural language processing (NLP) methods. Our system, developed for mammography, discovers new candidate terms by analyzing noun phrases in free-text reports to extend the mammography part of RadLex. Our NLP system extracts noun phrases from free-text mammography reports and classifies these noun phrases as "Has Candidate RadLex Term" or "Does Not Have Candidate RadLex Term." We tested the performance of our algorithm using 100 free-text mammography reports. An expert radiologist determined the true positive and true negative RadLex candidate terms. We calculated precision/positive predictive value and recall/sensitivity metrics to judge the system's performance. Finally, to identify new candidate terms for enhancing RadLex, we applied our NLP method to 270,540 free-text mammography reports obtained from three academic institutions. Our method demonstrated precision/positive predictive value of 0.77 (159/206 terms) and a recall/sensitivity of 0.94 (159/170 terms). The overall accuracy of the system is 0.80 (235/293 terms). When we ran our system on the set of 270,540 reports, it found 31,800 unique noun phrases that are potential candidates for RadLex. Our data-driven approach to mining radiology reports can identify new candidate terms for expanding the breast imaging lexicon portion of RadLex and may be a useful approach for discovering new candidate terms from other radiology domains.

Utility of BI-RADS Assessment Category 4 Subdivisions for Screening Breast MRI.

OBJECTIVE: BI-RADS for mammography and ultrasound subdivides category 4 assessments by likelihood of malignancy into categories 4A (> 2% to ≤ 10%), 4B (> 10% to ≤ 50%), and 4C (> 50% to < 95%). Category 4 is not subdivided for breast MRI because of a paucity of data. The purpose of the present study is to determine the utility of categories 4A, 4B, and 4C for MRI by calculating their positive predictive values (PPVs) and comparing them with BI-RADS-specified rates of malignancy for mammography and ultrasound. MATERIALS AND METHODS: All screening breast MRI examinations performed from July 1, 2010, through June 30, 2013, were included in this study. We identified in medical records prospectively assigned MRI BI-RADS categories, including category 4 subdivisions, which are used routinely in our practice. Benign versus malignant outcomes were determined by pathologic analysis, findings from 12 months or more clinical or imaging follow-up, or a combination of these methods. Distribution of BI-RADS categories and positive predictive value level 2 (PPV2; based on recommendation for tissue diagnosis) for categories 4 (including its subdivisions) and 5 were calculated. RESULTS: Of 860 screening breast MRI examinations performed for 566 women (mean age, 47 years), 82 with a BI-RADS category 4 assessment were identified. A total of 18 malignancies were found among 84 category 4 and 5 assessments, for an overall PPV2 of 21.4% (18/84). For category 4 subdivisions, PPV2s were as follows: for category 4A, 2.5% (1/40); for category 4B, 27.6% (8/29); for category 4C, 83.3% (5/6); and for category 4 (not otherwise specified), 28.6% (2/7). CONCLUSION: Category 4 subdivisions for MRI yielded malignancy rates within BI-RADS-specified ranges, supporting their use for benefits to patient care and more meaningful practice audits.

Mammography Screening: Gaps in Patient's and Physician's Needs for Shared Decision-Making.

As shared decision-making increasingly influences screening mammography, understanding similarities and differences between patients and physician perspectives becomes crucially important. This study compares women's and physicians' experiences of mammography shared decision-making. Results reflect the critical gaps which exist between women's expectations and physicians' confidence in shared decision-making regarding screening mammography.

Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.

BACKGROUND: The objective of this study was to demonstrate that computer-extracted image phenotypes (CEIPs) of biopsy-proven breast cancer on magnetic resonance imaging (MRI) can accurately predict pathologic stage. METHODS: The authors used a data set of deidentified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. In total, 91 biopsy-proven breast cancers were analyzed from patients who had information available on pathologic stage (stage I, n = 22; stage II, n = 58; stage III, n = 11) and surgically verified lymph node status (negative lymph nodes, n = 46; ≥ 1 positive lymph node, n = 44; no lymph nodes examined, n = 1). Tumors were characterized according to 1) radiologist-measured size and 2) CEIP. Then, models were built that combined 2 CEIPs to predict tumor pathologic stage and lymph node involvement, and the models were evaluated in a leave-1-out, cross-validation analysis with the area under the receiver operating characteristic curve (AUC) as the value of interest. RESULTS: Tumor size was the most powerful predictor of pathologic stage, but CEIPs that captured biologic behavior also emerged as predictive (eg, stage I and II vs stage III demonstrated an AUC of 0.83). No size measure was successful in the prediction of positive lymph nodes, but adding a CEIP that described tumor "homogeneity" significantly improved discrimination (AUC = 0.62; P = .003) compared with chance. CONCLUSIONS: The current results indicate that MRI phenotypes have promise for predicting breast cancer pathologic stage and lymph node status. Cancer 2016;122:748-757. © 2015 American Cancer Society.

MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.

Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R2 = 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence. © RSNA, 2016 Online supplemental material is available for this article.

Utility of Clinical Breast Examinations in Detecting Local-Regional Breast Events After Breast-Conservation in Women with a Personal History of High-Risk Breast Cancer.

BACKGROUND: Although breast cancer follow-up guidelines emphasize the importance of clinical examinations, prior studies suggest a small fraction of local-regional events occurring after breast conservation are detected by examination alone. Our objective was to examine how local-regional events are detected in a contemporary, national cohort of high-risk breast cancer survivors. METHODS: A stage-stratified sample of stage II/III breast cancer patients diagnosed in 2006-2007 (n = 11,099) were identified from 1217 facilities within the National Cancer Data Base. Additional data on local-regional and distant breast events, method of event detection, imaging received, and mortality were collected. We further limited the cohort to patients with breast conservation (n = 4854). Summary statistics describe local-regional event rates and detection method. RESULTS: Local-regional events were detected in 5.5 % (n = 265) of patients. Eighty-three percent were ipsilateral or contralateral in-breast events, and 17 % occurred within ipsilateral lymph nodes. Forty-eight percent of local-regional events were detected on asymptomatic breast imaging, 29 % by patients, and 10 % on clinical examination. Overall, 0.5 % of the 4854 patients had a local-regional event detected on examination. Examinations detected a higher proportion of lymph node events (8/45) compared with in-breast events (18/220). No factors were associated with method of event detection. DISCUSSION: Clinical examinations, as an adjunct to screening mammography, have a modest effect on local-regional event detection. This contradicts current belief that examinations are a critical adjunct to mammographic screening. These findings can help to streamline follow-up care, potentially improving follow-up efficiency and quality.

The National Mammography Database: Preliminary Data.

OBJECTIVE: The purposes of our study were to analyze screening mammography data submitted to the National Mammography Database (NMD) since its inception to confirm data collection feasibility, to draw parallels to data from the Breast Cancer Surveillance Consortium (BCSC), and to examine trends over time. We also retrospectively evaluated practice-level variation in terms of practice type, practice setting, census region, and annual volume. MATERIALS AND METHODS: Data from 90 mammography facilities in the NMD registry were analyzed. The registry receives mammography data collected as part of standard clinical practice, including self-reported demographic information, clinical findings, screening mammography interpretation, and biopsy results. Outcome metrics calculated were cancer detection rate, recall rate, and positive predictive values for biopsy recommended (PPV2) and biopsy performed (PPV3). RESULTS: The NMD successfully collected and analyzed data for 3,181,437 screening mammograms performed between January 2008 and December 2012. Mean values for outcomes were cancer detection rate of 3.43 per 1000 (95% CI, 3.2-3.7), recall rate of 10% (95% CI, 9.3-10.7%), PPV2 of 18.5% (95% CI, 16.7-20.2%), and PPV3 of 29.2% (95% CI, 26.2-32.3%). No statistically significant difference was seen in performance measurements on the basis of practice type, practice setting, census region, or annual volume. NMD performance measurements parallel those reported by the BCSC. CONCLUSION: The NMD has become the fastest growing mammography registry in the United States, providing nationwide performance metrics and permitting comparison with published benchmarks. Our study shows the feasibility of using the NMD to audit mammography facilities and to provide current, ongoing benchmark data.

Using automatically extracted information from mammography reports for decision-support.

OBJECTIVE: To evaluate a system we developed that connects natural language processing (NLP) for information extraction from narrative text mammography reports with a Bayesian network for decision-support about breast cancer diagnosis. The ultimate goal of this system is to provide decision support as part of the workflow of producing the radiology report. MATERIALS AND METHODS: We built a system that uses an NLP information extraction system (which extract BI-RADS descriptors and clinical information from mammography reports) to provide the necessary inputs to a Bayesian network (BN) decision support system (DSS) that estimates lesion malignancy from BI-RADS descriptors. We used this integrated system to predict diagnosis of breast cancer from radiology text reports and evaluated it with a reference standard of 300 mammography reports. We collected two different outputs from the DSS: (1) the probability of malignancy and (2) the BI-RADS final assessment category. Since NLP may produce imperfect inputs to the DSS, we compared the difference between using perfect ("reference standard") structured inputs to the DSS ("RS-DSS") vs NLP-derived inputs ("NLP-DSS") on the output of the DSS using the concordance correlation coefficient. We measured the classification accuracy of the BI-RADS final assessment category when using NLP-DSS, compared with the ground truth category established by the radiologist. RESULTS: The NLP-DSS and RS-DSS had closely matched probabilities, with a mean paired difference of 0.004±0.025. The concordance correlation of these paired measures was 0.95. The accuracy of the NLP-DSS to predict the correct BI-RADS final assessment category was 97.58%. CONCLUSION: The accuracy of the information extracted from mammography reports using the NLP system was sufficient to provide accurate DSS results. We believe our system could ultimately reduce the variation in practice in mammography related to assessment of malignant lesions and improve management decisions.