All Publications

  • Longitudinal Changes in Volumetric Breast Density in Healthy Women across the Menopausal Transition. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology Engmann, N. J., Scott, C., Jensen, M. R., Winham, S. J., Ma, L., Brandt, K. R., Mahmoudzadeh, A., Whaley, D. H., Hruska, C. B., Wu, F. F., Norman, A. D., Hiatt, R. A., Heine, J., Shepherd, J., Pankratz, V. S., Miglioretti, D. L., Kerlikowske, K., Vachon, C. M. 2019; 28 (8): 1324-1330


    Mammographic breast density declines during menopause. We assessed changes in volumetric breast density across the menopausal transition and factors that influence these changes.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.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.High premenopausal DV was a strong predictor of greater reductions in DV across the menopausal transition.We found that few factors other than premenopausal density influence changes in DV across the menopausal transition, limiting targeted prevention efforts.

    View details for DOI 10.1158/1055-9965.EPI-18-1375

    View details for PubMedID 31186265

    View details for PubMedCentralID PMC6677580

  • Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case-case analysis. Medical physics Hinton, B., Ma, L., Mahmoudzadeh, A. P., Malkov, S., Fan, B., Greenwood, H., Joe, B., Lee, V., Strand, F., Kerlikowske, K., Shepherd, J. 2019; 46 (3): 1309-1316


    Women with radiographically dense or texturally complex breasts are at increased risk for interval cancer, defined as cancers diagnosed after a normal screening examination. The purpose of this study was to create masking measures and apply them to identify interval risk in a population of women who experienced either screen-detected or interval cancers after controlling for breast density.We examined full-field digital screening mammograms acquired from 2006 to 2015. Examinations associated with 182 interval cancers were matched to 173 screen-detected cancers on age, race, exam date and time since last imaging examination. Local Image Quality Factor (IQF) values were calculated and used to create IQF maps that represented mammographic masking. We used various statistics to define global masking measures of these maps. Association of these masking measures with interval cancer vs screen-detected cancer was estimated using conditional logistic regression in a univariate and adjusted model for Breast Imaging-Reporting and Data System (BI-RADS) density. Receiver operator curves were calculated in each case to compare specificity vs sensitivity, and area under those curves were generated. Proportion of screen-detected cancer was estimated for stratifications of IQF features.Several masking features showed significant association with interval compared to screen-detected cancers after adjusting for BI-RADS density (up to P = 2.52E-6), and the 10th percentile of the IQF value (P = 1.72E-3) showed the strongest improvement in the area under the receiver operator curve, increasing from 0.65 using only BI-RADS density to 0.69. The highest masking group had a 32% proportion of screen-detected cancers while the low masking group had a 69% proportion.We conclude that computer vision methods using model observers may improve quantifying the probability of breast cancer detection beyond using breast density alone.

    View details for DOI 10.1002/mp.13410

    View details for PubMedID 30697755

    View details for PubMedCentralID PMC6416079

  • Combined effect of volumetric breast density and body mass index on breast cancer risk. Breast cancer research and treatment Engmann, N. J., Scott, C. G., Jensen, M. R., Winham, S., Miglioretti, D. L., Ma, L., Brandt, K., Mahmoudzadeh, A., Whaley, D. H., Hruska, C., Wu, F., Norman, A. D., Hiatt, R. A., Heine, J., Shepherd, J., Pankratz, V. S., Vachon, C. M., Kerlikowske, K. 2019; 177 (1): 165-173


    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.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.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.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.

    View details for DOI 10.1007/s10549-019-05283-z

    View details for PubMedID 31129803

    View details for PubMedCentralID PMC6640105

  • Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study. Cancer imaging : the official publication of the International Cancer Imaging Society Hinton, B., Ma, L., Mahmoudzadeh, A. P., Malkov, S., Fan, B., Greenwood, H., Joe, B., Lee, V., Kerlikowske, K., Shepherd, J. 2019; 19 (1): 41


    To 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.Full-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.Pre-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.Pre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection.

    View details for DOI 10.1186/s40644-019-0227-3

    View details for PubMedID 31228956

    View details for PubMedCentralID PMC6589178

  • Automated symptom and treatment side effect monitoring for improved quality of life among adults with diabetic peripheral neuropathy in primary care: a pragmatic, cluster, randomized, controlled trial DIABETIC MEDICINE Adams, A. S., Schmittdiel, J. A., Altschuler, A., Bayliss, E. A., Neugebauer, R., Ma, L., Dyer, W., Clark, J., Cook, B., Willyoung, D., Jaffe, M., Young, J. D., Kim, E., Boggs, J. M., Prosser, L. A., Wittenberg, E., Callaghan, B., Shainline, M., Hippler, R. M., Grant, R. W. 2019; 36 (1): 52–61


    To evaluate the effectiveness of automated symptom and side effect monitoring on quality of life among individuals with symptomatic diabetic peripheral neuropathy.We conducted a pragmatic, cluster randomized controlled trial (July 2014 to July 2016) within a large healthcare system. We randomized 1834 primary care physicians and prospectively recruited from their lists 1270 individuals with neuropathy who were newly prescribed medications for their symptoms. Intervention participants received automated telephone-based symptom and side effect monitoring with physician feedback over 6 months. The control group received usual care plus three non-interactive diabetes educational calls. Our primary outcomes were quality of life (EQ-5D) and select symptoms (e.g. pain) measured 4-8 weeks after starting medication and again 8 months after baseline. Process outcomes included receiving a clinically effective dose and communication between individuals with neuropathy and their primary care provider over 12 months. Interviewers collecting outcome data were blinded to intervention assignment.Some 1252 participants completed the baseline measures [mean age (sd): 67 (11.7), 53% female, 57% white, 8% Asian, 13% black, 20% Hispanic]. In total, 1179 participants (93%) completed follow-up (619 control, 560 intervention). Quality of life scores (intervention: 0.658 ± 0.094; control: 0.653 ± 0.092) and symptom severity were similar at baseline. The intervention had no effect on primary [EQ-5D: -0.002 (95% CI -0.01, 0.01), P = 0.623; pain: 0.295 (-0.75, 1.34), P = 0.579; sleep disruption: 0.342 (-0.18, 0.86), P = 0.196; lower extremity functioning: -0.079 (-1.27, 1.11), P = 0.896; depression: -0.462 (-1.24, 0.32); P = 0.247] or process outcomes.Automated telephone monitoring and feedback alone were not effective at improving quality of life or symptoms for people with symptomatic diabetic peripheral (NCT02056431).

    View details for DOI 10.1111/dme.13840

    View details for Web of Science ID 000454409900006

    View details for PubMedID 30343489

    View details for PubMedCentralID PMC7236318

  • Automated volumetric breast density measures: differential change between breasts in women with and without breast cancer. Breast cancer research : BCR Brandt, K. R., Scott, C. G., Miglioretti, D. L., Jensen, M. R., Mahmoudzadeh, A. P., Hruska, C., Ma, L., Wu, F. F., Cummings, S. R., Norman, A. D., Engmann, N. J., Shepherd, J. A., Winham, S. J., Kerlikowske, K., Vachon, C. M. 2019; 21 (1): 118


    Given that breast cancer and normal dense fibroglandular tissue have similar radiographic attenuation, we examine whether automated volumetric density measures identify a differential change between breasts in women with cancer and compare to healthy controls.Eligible cases (n = 1160) had unilateral invasive breast cancer and bilateral full-field digital mammograms (FFDMs) at two time points: within 2 months and 1-5 years before diagnosis. Controls (n = 2360) were matched to cases on age and date of FFDMs. Dense volume (DV) and volumetric percent density (VPD) for each breast were assessed using Volpara™. Differences in DV and VPD between mammograms (median 3 years apart) were calculated per breast separately for cases and controls and their difference evaluated by using the Wilcoxon signed-rank test. To simulate clinical practice where cancer laterality is unknown, we examined whether the absolute difference between breasts can discriminate cases from controls using area under the ROC curve (AUC) analysis, adjusting for age, BMI, and time.Among cases, the VPD and DV between mammograms of the cancerous breast decreased to a lesser degree (- 0.26% and - 2.10 cm3) than the normal breast (- 0.39% and - 2.74 cm3) for a difference of 0.13% (p value < 0.001) and 0.63 cm3 (p = 0.002), respectively. Among controls, the differences between breasts were nearly identical for VPD (- 0.02 [p = 0.92]) and DV (0.05 [p = 0.77]). The AUC for discriminating cases from controls using absolute difference between breasts was 0.54 (95% CI 0.52, 0.56) for VPD and 0.56 (95% CI, 0.54, 0.58) for DV.There is a small relative increase in volumetric density measures over time in the breast with cancer which is not found in the normal breast. However, the magnitude of this difference is small, and this measure alone does not appear to be a good discriminator between women with and without breast cancer.

    View details for DOI 10.1186/s13058-019-1198-9

    View details for PubMedID 31660981

    View details for PubMedCentralID PMC6819393

  • Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study. Annals of internal medicine Kerlikowske, K., Scott, C. G., Mahmoudzadeh, A. P., Ma, L., Winham, S., Jensen, M. R., Wu, F. F., Malkov, S., Pankratz, V. S., Cummings, S. R., Shepherd, J. A., Brandt, K. R., Miglioretti, D. L., Vachon, C. M. 2018; 168 (11): 757-765


    In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead.To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures.Case-control.San Francisco Mammography Registry and Mayo Clinic.1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants.Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity.Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively.Neither automated nor clinical BI-RADS density was assessed on tomosynthesis, an emerging breast screening method.Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density.National Cancer Institute.

    View details for DOI 10.7326/M17-3008

    View details for PubMedID 29710124

    View details for PubMedCentralID PMC6447426

  • Combining quantitative and qualitative breast density measures to assess breast cancer risk. Breast cancer research : BCR Kerlikowske, K., Ma, L., Scott, C. G., Mahmoudzadeh, A. P., Jensen, M. R., Sprague, B. L., Henderson, L. M., Pankratz, V. S., Cummings, S. R., Miglioretti, D. L., Vachon, C. M., Shepherd, J. A. 2017; 19 (1): 97


    Accurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk.We conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume.Compared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84-4.47, and OR 2.56, 95% CI 1.87-3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75-3.09, and OR 1.50, 95% CI 0.82-2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623-0.654, vs. C-statistic 0.614, 95% CI 0.598-0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%.Risk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.

    View details for DOI 10.1186/s13058-017-0887-5

    View details for PubMedID 28830497

    View details for PubMedCentralID PMC5567482

  • Improvements in access and care through the Affordable Care Act. The American journal of managed care Schmittdiel, J. A., Barrow, J. C., Wiley, D., Ma, L., Sam, D., Chau, C. V., Shetterly, S. M. 2017; 23 (3): e95-e97


    To examine the impact of enrolling in a healthcare plan through the Affordable Care Act (ACA) healthcare exchanges on self-reported access to care.Cohort study using self-reported data of patients newly enrolled in Kaiser Permanente California and Kaiser Permanente Colorado through the ACA healthcare exchanges for coverage beginning January 1, 2014.Baseline and follow-up surveys conducted via mail and telephone, with response rates of 45% and 51%, respectively.We found significant increases in the percentage of people who reported having a personal healthcare provider (59% vs 73%; P <.01) and significant decreases in those who reported delaying needed medical care due to costs (37% vs 25%; P <.01) before and after ACA enrollment. There was also a significant increase in the percentage of patients who reported receiving a flu shot during the prior year (41% vs 52%; P <.01). Among the people who reported having less than 4 months of healthcare coverage in 2013, these improvements were even more pronounced. This group also showed significant increases in the percentages who felt they had a place to go when they needed medical care (43% vs 56%; P <.01) and who reported they received advice to quit smoking or using tobacco (46% vs 72%; P <.05).These findings are an important addition to the evidence base that the ACA is improving the healthcare experience and reducing barriers due to costs for individuals obtaining insurance coverage through the healthcare exchanges.

    View details for PubMedID 28385029

    View details for PubMedCentralID PMC5536832

  • Longitudinal Changes in Volumetric Breast Density with Tamoxifen and Aromatase Inhibitors. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology Engmann, N. J., Scott, C. G., Jensen, M. R., Ma, L., Brandt, K. R., Mahmoudzadeh, A. P., Malkov, S., Whaley, D. H., Hruska, C. B., Wu, F. F., Winham, S. J., Miglioretti, D. L., Norman, A. D., Heine, J. J., Shepherd, J., Pankratz, V. S., Vachon, C. M., Kerlikowske, K. 2017; 26 (6): 930-937


    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.

    View details for DOI 10.1158/1055-9965.EPI-16-0882

    View details for PubMedID 28148596

    View details for PubMedCentralID PMC5457346

  • Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones. Breast cancer research and treatment Shieh, Y., Hu, D., Ma, L., Huntsman, S., Gard, C. C., Leung, J. W., Tice, J. A., Ziv, E., Kerlikowske, K., Cummings, S. R. 2017; 166 (2): 603-612


    Models that predict the risk of estrogen receptor (ER)-positive breast cancers may improve our ability to target chemoprevention. We investigated the contributions of sex hormones to the discrimination of the Breast Cancer Surveillance Consortium (BCSC) risk model and a polygenic risk score comprised of 83 single nucleotide polymorphisms.We conducted a nested case-control study of 110 women with ER-positive breast cancers and 214 matched controls within a mammography screening cohort. Participants were postmenopausal and not on hormonal therapy. The associations of estradiol, estrone, testosterone, and sex hormone binding globulin with ER-positive breast cancer were evaluated using conditional logistic regression. We assessed the individual and combined discrimination of estradiol, the BCSC risk score, and polygenic risk score using the area under the receiver operating characteristic curve (AUROC).Of the sex hormones assessed, estradiol (OR 3.64, 95% CI 1.64-8.06 for top vs bottom quartile), and to a lesser degree estrone, was most strongly associated with ER-positive breast cancer in unadjusted analysis. The BCSC risk score (OR 1.32, 95% CI 1.00-1.75 per 1% increase) and polygenic risk score (OR 1.58, 95% CI 1.06-2.36 per standard deviation) were also associated with ER-positive cancers. A model containing the BCSC risk score, polygenic risk score, and estradiol levels showed good discrimination for ER-positive cancers (AUROC 0.72, 95% CI 0.65-0.79), representing a significant improvement over the BCSC risk score (AUROC 0.58, 95% CI 0.50-0.65).Adding estradiol and a polygenic risk score to a clinical risk model improves discrimination for postmenopausal ER-positive breast cancers.

    View details for DOI 10.1007/s10549-017-4430-2

    View details for PubMedID 28791495

    View details for PubMedCentralID PMC5669824

  • Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status. Breast cancer research : BCR Malkov, S., Shepherd, J. A., Scott, C. G., Tamimi, R. M., Ma, L., Bertrand, K. A., Couch, F., Jensen, M. R., Mahmoudzadeh, A. P., Fan, B., Norman, A., Brandt, K. R., Pankratz, V. S., Vachon, C. M., Kerlikowske, K. 2016; 18 (1): 122


    Several studies have shown that mammographic texture features are associated with breast cancer risk independent of the contribution of breast density. Thus, texture features may provide novel information for risk stratification. We examined the association of a set of established texture features with breast cancer risk by tumor type and estrogen receptor (ER) status, accounting for breast density.This study combines five case-control studies including 1171 breast cancer cases and 1659 controls matched for age, date of mammogram, and study. Mammographic breast density and 46 breast texture features, including first- and second-order features, Fourier transform, and fractal dimension analysis, were evaluated from digitized film-screen mammograms. Logistic regression models evaluated each normalized feature with breast cancer after adjustment for age, body mass index, first-degree family history, percent density, and study.Of the mammographic features analyzed, fractal dimension and second-order statistics features were significantly associated (p < 0.05) with breast cancer. Fractal dimensions for the thresholds equal to 10% and 15% (FD_TH_10 [corrected] and FD_TH_15) [corrected] were associated with an increased risk of breast cancer while thresholds from 60% to 85% (FD_TH_60 to FD_TH_85) [corrected] were associated with a decreased risk. Increasing the FD_TH_75 [corrected] and Energy feature values were associated with a decreased risk of breast cancer while increasing Entropy was associated with an increased [corrected] risk of breast cancer. For example, 1 standard deviation increase of FD_TH_75 [corrected] was associated with a 13% reduced risk of breast cancer (odds ratio = 0.87, 95% confidence interval 0.79-0.95). Overall, the direction of associations between features and ductal carcinoma in situ (DCIS) and invasive cancer, and estrogen receptor positive and negative cancer were similar.Mammographic features derived from film-screen mammograms are associated with breast cancer risk independent of percent mammographic density. Some texture features also demonstrated associations for specific tumor types. For future work, we plan to assess risk prediction combining mammographic density and features assessed on digital images.

    View details for DOI 10.1186/s13058-016-0778-1

    View details for PubMedID 27923387

    View details for PubMedCentralID PMC5139106

  • Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening. Radiology Brandt, K. R., Scott, C. G., Ma, L., Mahmoudzadeh, A. P., Jensen, M. R., Whaley, D. H., Wu, F. F., Malkov, S., Hruska, C. B., Norman, A. D., Heine, J., Shepherd, J., Pankratz, V. S., Kerlikowske, K., Vachon, C. M. 2016; 279 (3): 710-9


    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.

    View details for DOI 10.1148/radiol.2015151261

    View details for PubMedID 26694052

    View details for PubMedCentralID PMC4886704

  • Breast cancer risk prediction using a clinical risk model and polygenic risk score. Breast cancer research and treatment Shieh, Y., Hu, D., Ma, L., Huntsman, S., Gard, C. C., Leung, J. W., Tice, J. A., Vachon, C. M., Cummings, S. R., Kerlikowske, K., Ziv, E. 2016; 159 (3): 513-25


    Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome-wide association studies. We conducted a nested case-control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95 % CI 1.69-3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95 % CI 0.57-0.64), and an Asian-specific PRS had AUROC 0.64 (95 % CI 0.53-0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p = 0.01). The BCSC-PRS model classified 18 % of cases as high-risk (5-year risk ≥3 %), compared with 7 % using the BCSC model. The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Further consideration of the PRS's role in decision-making around screening and prevention strategies is merited.

    View details for DOI 10.1007/s10549-016-3953-2

    View details for PubMedID 27565998

    View details for PubMedCentralID PMC5033764

  • The Effect of Change in Body Mass Index on Volumetric Measures of Mammographic Density CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION Hart, V., Reeves, K. W., Sturgeon, S. R., Reich, N. G., Sievert, L., Kerlikowske, K., Ma, L., Shepherd, J., Tice, J. A., Mahmoudzadeh, A., Malkov, S., Sprague, B. L. 2015; 24 (11): 1724-1730


    Understanding how changes in body mass index (BMI) relate to changes in mammographic density is necessary to evaluate adjustment for BMI gain/loss in studies of change in density and breast cancer risk. Increase in BMI has been associated with a decrease in percent density, but the effect on change in absolute dense area or volume is unclear.We examined the association between change in BMI and change in volumetric breast density among 24,556 women in the San Francisco Mammography Registry from 2007 to 2013. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry measurements. Cross-sectional and longitudinal associations between BMI and dense volume (DV), non-dense volume (NDV), and percent dense volume (PDV) were assessed using multivariable linear regression models, adjusted for demographics, risk factors, and reproductive history.In cross-sectional analysis, BMI was positively associated with DV [β, 2.95 cm(3); 95% confidence interval (CI), 2.69-3.21] and inversely associated with PDV (β, -2.03%; 95% CI, -2.09, -1.98). In contrast, increasing BMI was longitudinally associated with a decrease in both DV (β, -1.01 cm(3); 95% CI, -1.59, -0.42) and PDV (β, -1.17%; 95% CI, -1.31, -1.04). These findings were consistent for both pre- and postmenopausal women.Our findings support an inverse association between change in BMI and change in PDV. The association between increasing BMI and decreasing DV requires confirmation.Longitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should evaluate adjustment for change in BMI.

    View details for DOI 10.1158/1055-9965.EPI-15-0330

    View details for Web of Science ID 000365598600011

    View details for PubMedID 26315554

    View details for PubMedCentralID PMC4633314

  • Dense and Nondense Mammographic Area and Risk of Breast Cancer by Age and Tumor Characteristics CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION Bertrand, K. A., Scott, C. G., Tamimi, R. M., Jensen, M. R., Pankratz, V., Norman, A. D., Visscher, D. W., Couch, F. J., Shepherd, J., Chen, Y., Fan, B., Wu, F., Ma, L., Beck, A. H., Cummings, S. R., Kerlikowske, K., Vachon, C. M. 2015; 24 (5): 798-809


    Mammographic density (MD) is a strong breast cancer risk factor. We previously reported associations of percent mammographic density (PMD) with larger and node-positive tumors across all ages, and estrogen receptor (ER)-negative status among women ages <55 years. To provide insight into these associations, we examined the components of PMD [dense area (DA) and nondense area (NDA)] with breast cancer subtypes.Data were pooled from six studies including 4,095 breast cancers and 8,558 controls. DA and NDA were assessed from digitized film-screen mammograms and standardized across studies. Breast cancer odds by density phenotypes and age according to histopathologic characteristics and receptor status were calculated using polytomous logistic regression.DA was associated with increased breast cancer risk [OR for quartiles: 0.65, 1.00 (Ref), 1.22, 1.55; P(trend) <0.001] and NDA was associated with decreased risk [ORs for quartiles: 1.39, 1.00 (Ref), 0.88, 0.72; P(trend) <0.001] across all ages and invasive tumor characteristics. There were significant trends in the magnitude of associations of both DA and NDA with breast cancer by increasing tumor size (P(trend) < 0.001) but no differences by nodal status. Among women <55 years, DA was more strongly associated with increased risk of ER(+) versus ER(-) tumors (P(het) = 0.02), while NDA was more strongly associated with decreased risk of ER(-) versus ER(+) tumors (P(het) = 0.03).DA and NDA have differential associations with ER(+) versus ER(-) tumors that vary by age.DA and NDA are important to consider when developing age- and subtype-specific risk models.

    View details for DOI 10.1158/1055-9965.EPI-14-1136

    View details for Web of Science ID 000353702800005

    View details for PubMedID 25716949

    View details for PubMedCentralID PMC4417380

  • Language Barriers, Location of Care, and Delays in Follow-up of Abnormal Mammograms MEDICAL CARE Karliner, L. S., Ma, L., Hofmann, M., Kerlikowske, K. 2012; 50 (2): 171-178


    Breast cancer is frequently diagnosed after an abnormal mammography result. Language barriers can complicate communication of those results.We evaluated the association of non-English language with delay in follow-up.Retrospective cohort study of women at 3 mammography facilities participating in the San Francisco Mammography Registry with an abnormal mammogram result from 1997 to 2008. We measured median time from report of abnormal result to first follow-up test.Of 13,014 women with 16,109 abnormal mammograms, 4027 (31%) had a non-English patient language. Clinical facilities differed in proportion of non-English speakers and in time to first follow-up test: facility A (38%; 25 d), facility B (18%; 14 d), and facility C (51%; 41 d). Most mammography examinations (67%) had breast imaging and reporting data system 0 (incomplete) assessment, requiring radiographic follow-up. At 30 days of follow-up, 67% of all English speakers with incomplete assessments had a follow-up examination compared with 50% of all non-English speakers (P<0.0001). The facility with the least delay and the lowest proportion of non-English speakers, had the biggest difference by language; compared with English speakers and adjusting for education, non-English speakers had twice the odds ratio of >30-day delay in follow-up (odds ratio=2.3; 95% confidence interval, 1.4-3.9).There are considerable differences among facilities in delays in diagnostic follow-up of abnormal mammography results. More attention must be paid to understanding mammography facility factors, such as wait time to schedule diagnostic mammography and radiology workload, to improve rates of timely follow-up, particularly for those facilities disproportionately serving vulnerable non-English speaking patients.

    View details for DOI 10.1097/MLR.0b013e31822dcf2d

    View details for Web of Science ID 000299315600009

    View details for PubMedID 21993060

    View details for PubMedCentralID PMC3918470

  • Volume of mammographic density and risk of breast cancer. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology Shepherd, J. A., Kerlikowske, K., Ma, L., Duewer, F., Fan, B., Wang, J., Malkov, S., Vittinghoff, E., Cummings, S. R. 2011; 20 (7): 1473-82


    Assessing the volume of mammographic density might more accurately reflect the amount of breast volume at risk of malignant transformation and provide a stronger indication of risk of breast cancer than methods based on qualitative scores or dense breast area.We prospectively collected mammograms for women undergoing screening mammography. We determined the diagnosis of subsequent invasive or ductal carcinoma in situ for 275 cases, selected 825 controls matched for age, ethnicity, and mammography system, and assessed three measures of breast density: percent dense area, fibroglandular volume, and percent fibroglandular volume.After adjustment for familial breast cancer history, body mass index, history of breast biopsy, and age at first live birth, the ORs for breast cancer risk in the highest versus lowest measurement quintiles were 2.5 (95% CI: 1.5-4.3) for percent dense area, 2.9 (95% CI: 1.7-4.9) for fibroglandular volume, and 4.1 (95% CI: 2.3-7.2) for percent fibroglandular volume. Net reclassification indexes for density measures plus risk factors versus risk factors alone were 9.6% (P = 0.07) for percent dense area, 21.1% (P = 0.0001) for fibroglandular volume, and 14.8% (P = 0.004) for percent fibroglandular volume. Fibroglandular volume improved the categorical risk classification of 1 in 5 women for both women with and without breast cancer.Volumetric measures of breast density are more accurate predictors of breast cancer risk than risk factors alone and than percent dense area.Risk models including dense fibroglandular volume may more accurately predict breast cancer risk than current risk models.

    View details for DOI 10.1158/1055-9965.EPI-10-1150

    View details for PubMedID 21610220

    View details for PubMedCentralID PMC3132306