Comparative Effectiveness of Digital Versus Film-Screen Mammography in Community Practice in the United States: A Cohort Study

Few studies have examined the comparative effectiveness of digital versus film-screen mammography in U.S. community practice

The Influence of Race/Ethnicity and Place of Service on Breast Reconstruction for Medicare Beneficiaries with Mastectomy

Racial disparities in breast reconstruction for breast cancer are documented. Place of service has contributed to disparities in cancer care; but the interaction of race/ethnicity and place of service has not been explicitly examined. We examined whether place of service modified the effect of race/ethnicity on receipt of reconstruction. We included women with a mastectomy for incident breast cancer in SEER-Medicare from 2005-2009. Using Medicare claims, we determined breast reconstruction within 6 months. Facility characteristics included: rural/urban location, teaching status, NCI Cancer Center designation, cooperative oncology group membership, Disproportionate Share Hospital (DSH) status, and breast surgery volume. Using multivariable logistic regression, we analyzed reconstruction in relation to minority status and facility characteristics. Of the 17,958 women, 14.2% were racial/ethnic women of color and a total of 9.3% had reconstruction. Caucasians disproportionately received care at non-teaching hospitals (53% v. 42%) and did not at Disproportionate Share Hospitals (77% v. 86%). Women of color had 55% lower odds of reconstruction than Caucasians (OR = 0.45; 95% CI 0.37-0.55). Those in lower median income areas had lower odds of receiving reconstruction, regardless of race/ethnicity. Odds of reconstruction reduced at rural, non-teaching and cooperative oncology group hospitals, and lower surgery volume facilities. Facility effects on odds of reconstruction were similar in analyses stratified by race/ethnicity status. Race/ethnicity and facility characteristics have independent effects on utilization of breast reconstruction, with no significant interaction. This suggests that, regardless of a woman\u27s race/ethnicity, the place of service influences the likelihood of reconstruction

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

Predicting invasive breast cancer versus DCIS in different age groups.

BackgroundIncreasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age.MethodsWe analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women ≥ 65 (older group), women 50-64 (middle age group), and women < 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC).ResultsThe models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group--mass margins, and in the younger group--mass size were positive predictors of invasive cancer.ConclusionsClinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age

Survival Point Estimate Prediction in Matched and Non-Matched Case-Control Subsample Designed Studies

Providing information about the risk of disease and clinical factors that may increase or decrease a patient\u27s risk of disease is standard medical practice. Although case-control studies can provide evidence of strong associations between diseases and risk factors, clinicians need to be able to communicate to patients the age-specific risks of disease over a defined time interval for a set of risk factors. An estimate of absolute risk cannot be determined from case-control studies because cases are generally chosen from a population whose size is not known (necessary for calculation of absolute risk) and where duration of follow-up is not known (necessary for calculation of incidence). This problem can sometimes be overcome by using a nested case-control design. We have collected data on a National Cancer Institute funded population-based cohort study. This study contains a matched set of cases and controls within the cohort. This design is more cost-efficient than a full cohort study since expensive predictor variables (genomic measures, sex hormone levels, mammographic breast density) are measured on all of the cases, but on only a sample of the cohort who did not develop the outcome of interest (the controls). In addition, this design avoids the potential biases of conventional case-control studies that draw cases and controls from different populations. Importantly, the presence or absence of the outcome of interest has been established for the entire cohort within the same time period. The specifics of the sampling in our study do not adhere to the assumptions for absolute risk estimation methods previously developed in the literature. Here we introduce a novel method which provides locally efficient estimators to predict the absolute risk of a cohort from measures only taken on the matched case-control participants. The proposed method is evaluated using simulation studies and survival data from women with ductal carcinoma in situ, a non-invasive form of breast cancer. A generalization of the proposed method is related to other similar sampling designs such as nested case-control, case-cohort, and two-stage case-control

Increased Risk of Developing Breast Cancer after a False-Positive Screening Mammogram

Women with a history of false-positive mammogram result may be at increased risk of developing subsequent breast cancer

Validation of a Medicare Claims-based Algorithm for Identifying Breast Cancers Detected at Screening Mammography

The breast cancer detection rate is a benchmark measure of screening mammography quality, but its computation requires linkage of mammography interpretive performance information with cancer incidence data. A Medicare claims-based measure of detected breast cancers could simplify measurement of this benchmark and facilitate mammography quality assessment and research

Abrogated Response to Cellular Stress Identifies DCIS Associated with Subsequent Tumor Events and Defines Basal-like Breast Tumors

SummaryApproximately 15%–30% of women diagnosed with ductal carcinoma in situ (DCIS) develop a subsequent tumor event within 10 years after surgical lumpectomy. To date, little is known about the molecular pathways that confer this differential risk for developing subsequent disease. In this study, we demonstrate that expression of biomarkers indicative of an abrogated response to cellular stress predicts DCIS with worse outcome and is a defining characteristic of basal-like invasive tumors. Mechanistic studies identify the Rb pathway as a key regulator of this response. Conversely, biomarkers indicative of an intact response to cellular stress are strongly associated with a disease-free prognosis. Assessment of these biomarkers in DCIS begins to allow prediction of tumor formation years before it actually occurs

Cumulative Probability of False-Positive Recall or Biopsy Recommendation After 10 Years of Screening Mammography: A Cohort Study

False-positive mammography results are common. Biennial screening may decrease the cumulative probability of false-positive results across many years of repeat screening but could also delay cancer diagnosis