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  • 1
    Online Resource
    Online Resource
    Effect of Menstrual Cycle Phase on Background Parenchymal Uptake at Molecular Breast Imaging
    Elsevier BV ; 2015
    In:  Academic Radiology Vol. 22, No. 9 ( 2015-09), p. 1147-1156
    Details
    In: Academic Radiology, Elsevier BV, Vol. 22, No. 9 ( 2015-09), p. 1147-1156
    Type of Medium: Online Resource
    ISSN: 1076-6332
    URL: Article
    DOI: 10.1016/j.acra.2015.04.003
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
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  • 2
    Online Resource
    Online Resource
    Stroma modifies relationships between risk factor exposure and age-related epithelial involution in benign breast
    Elsevier BV ; 2018
    In:  Modern Pathology Vol. 31, No. 7 ( 2018-07), p. 1085-1096
    Details
    In: Modern Pathology, Elsevier BV, Vol. 31, No. 7 ( 2018-07), p. 1085-1096
    Type of Medium: Online Resource
    ISSN: 0893-3952
    URL: Article
    DOI: 10.1038/s41379-018-0033-7
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 2041318-X
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  • 3
    Online Resource
    Online Resource
    Model for Individualized Prediction of Breast Cancer Risk After a Benign Breast Biopsy
    American Society of Clinical Oncology (ASCO) ; 2015
    In:  Journal of Clinical Oncology Vol. 33, No. 8 ( 2015-03-10), p. 923-929
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 33, No. 8 ( 2015-03-10), p. 923-929
    Abstract: Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT). Methods We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD–to–breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT. Results The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247). Conclusion We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2014.55.4865
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2015
    detail.hit.zdb_id: 2005181-5
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  • 4
    Online Resource
    Online Resource
    A new model for predicting breast cancer risk in women with atypical hyperplasia.
    American Society of Clinical Oncology (ASCO) ; 2015
    In:  Journal of Clinical Oncology Vol. 33, No. 28_suppl ( 2015-10-01), p. 2-2
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 33, No. 28_suppl ( 2015-10-01), p. 2-2
    Abstract: 2 Background: Women with atypical hyperplasia (AH) on breast biopsy have an aggregate increased risk of breast cancer (BC), but accurate personalized risk prediction is desirable to facilitate individual clinical management decisions. Currently used models provide poor BC risk prediction for women with AH. Our goal was to develop and validate an improved risk prediction model for women with AH. Methods: From a cohort of 13,538 women with benign breast disease from 1967-2001, pathology review confirmed 699 with AH. Clinical risk factors and histologic features of the tissue biopsy were recorded, and BC events were ascertained from study questionnaires, tumor registry, and review of medical records. Using a lasso approach, 23 variables were assessed for model inclusion. Lasso-identified features were then fit in a Cox regression model to estimate BC risk. Model discrimination was assessed with C-statistics in the model-building set and in a separate external validation set. Calibration was assessed by comparing observed to predicted breast cancer counts. Results: The model-building set comprised 699 women with 142 BC events (median follow-up 8.1 years), and the external validation set comprised 461 women with 114 BC events (median follow-up 11.4 years). The final model included three covariates: age at biopsy, age squared, and number of foci of AH. Model performance was good, with a C-statistic of 0.622 (SE = 0.027) in the model-building set and 0.594 (SE = 0.029) in the external validation set. The model is well-calibrated, with observed to expected numbers of BCs nearly equal across all post-biopsy follow-up years. Conclusions: We propose a new model for predicting BC risk in women with AH based on age at biopsy and number of foci of atypia. This model provides absolute risk estimates for women with AH, has good discriminatory ability, is well-calibrated, and validates in an external cohort.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/jco.2015.33.28_suppl.2
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2015
    detail.hit.zdb_id: 2005181-5
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  • 5
    Online Resource
    Online Resource
    Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer
    American Society of Clinical Oncology (ASCO) ; 2015
    In:  Journal of Clinical Oncology Vol. 33, No. 28 ( 2015-10-01), p. 3137-3143
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 33, No. 28 ( 2015-10-01), p. 3137-3143
    Abstract: Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density. Methods We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC. Results We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P 〈 .001). Conclusion The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2015.60.8869
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2015
    detail.hit.zdb_id: 2005181-5
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  • 6
    Online Resource
    Online Resource
    Impact of a breast cancer (BC) polygenic risk score (PRS) on the decision to take preventive endocrine therapy (ET): The Genetic Risk Estimate (GENRE) trial.
    American Society of Clinical Oncology (ASCO) ; 2019
    In:  Journal of Clinical Oncology Vol. 37, No. 15_suppl ( 2019-05-20), p. 1501-1501
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 37, No. 15_suppl ( 2019-05-20), p. 1501-1501
    Abstract: 1501 Background: Despite BC risk reduction of 50-65% by preventive endocrine therapy (ET), very few at-risk women choose to take them. A woman’s perceived BC risk correlates with uptake of ET. A PRS comprised of 77 BC genetic susceptibility loci (Single Nucleotide Polymorphisms (SNP) improves the accuracy of risk prediction for BC. We examined the impact of the addition of individualized PRS BC risk prediction to standard risk calculator estimates on intent to take BC prevention medication. Methods: Eligible women had ≥5% 10 yr BC Tyrer-Cuzick risk (IBIS) or 5 year Gail score ≥3%, with no history of BC or hereditary BC syndrome. Standard BC risk estimates (IBIS or Gail) were incorporated into the counselling on BC preventive ET. A self-reported questionnaire at baseline quantified intention to take ET and explored factors associated with this decision. Blood samples were obtained and genotyped for 77 SNPs, individualized PRS were calculated then incorporated into IBIS and Gail predictions for 5 yr, 10 yr, & lifetime BC risk. At a second visit, PRS risk & prevention recommendations were revisited. Post visit questionnaires assessed change in intent to take ET. Multivariable linear regression was performed to assess impact of baseline variables on change in intent to take medication. Results: From 2016 to 2017, 151 women in Canada & USA were enrolled, median age: 56.1 (range 36-76.4), 35.6% were premenopausal, 98.7% were Caucasian. Median 5yr, 10yr, & lifetime IBIS risk estimates were 3.8% (2.0-11.5), 7.9% (5.0-23.1), and 25.3% (5.5 to 92.2). PRS increased BC risk estimates in 84 (55.6%) and reduced BC risk estimates in 67 (44%) women. After PRS risk counselling, intention to take ET significantly changed (p 〈 0.001): 41.9% of those with increased PRS were more inclined, and 46.7% of women with decreased PRS were less inclined to take ET. On multivariable regression, increase in PRS (p 〈 0.0001) and less concern about ET side effects (p 〈 0.0001) were associated with greater intent to take ET. Conclusions: In high risk women, PRS significantly changed BC risk estimates & intent to take preventive ET. Further assessments of the impact of PRS scores on compliance with ET are warranted. Clinical trial information: NCT02517593.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2019.37.15_suppl.1501
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2019
    detail.hit.zdb_id: 2005181-5
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  • 7
    Online Resource
    Online Resource
    Polygenic risk score for breast cancer in high-risk women.
    American Society of Clinical Oncology (ASCO) ; 2018
    In:  Journal of Clinical Oncology Vol. 36, No. 15_suppl ( 2018-05-20), p. 1508-1508
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 36, No. 15_suppl ( 2018-05-20), p. 1508-1508
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2018.36.15_suppl.1508
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2018
    detail.hit.zdb_id: 2005181-5
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  • 8
    Online Resource
    Online Resource
    Amenorrhea in lung cancer patients.
    American Society of Clinical Oncology (ASCO) ; 2017
    In:  Journal of Clinical Oncology Vol. 35, No. 15_suppl ( 2017-05-20), p. e20092-e20092
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 35, No. 15_suppl ( 2017-05-20), p. e20092-e20092
    Abstract: e20092 Background: More than 5,000 premenopausal women are diagnosed (dx’d) with lung cancer annually in the United States. Improvements in treatment are contributing to a growing population of young survivors. Limited data exist regarding the risk of treatment-related amenorrhea, a surrogate for infertility and early menopause, after systemic therapies for lung cancer. Methods: Since 1997, we mailed annual surveys to patients seen at Mayo Clinic for lung cancer who consented to join a research cohort. Surveys queried menopausal status and age of menopause. Those who were dx’d under age 50, who were treated with curative intent, and who reported that they were premenopausal at the time of the cancer dx were included in this analysis. “Immediate” treatment-related menopause was defined as reporting age of menopause as the same as the age at diagnosis or 1 year (yr) older. Results: Of 182 survey respondents to date (mean time from dx to 1 st survey: 26 mos, SD 24 mos, range 12-202 mos), 85 (mean age 44 yrs, SD 5, range 34-48) received chemo during the yr after diagnosis, 26 of whom also received targeted therapy during that yr. Lung cancer dx occurred 1958-2016. Platinum drugs, taxanes, and etoposide were the most common chemotherapies. 46% of chemo recipients (mean age 47, SD 2, range 41-49) experienced immediate menopause, 9% (mean age 43 yrs, SD 5, range 35-48) experienced menopause at least 2 yrs after the age of dx, and 45% (mean age 40 yrs, SD 5, range 25-48) remained pre- or peri-menopausal at their final survey (on average 3 years after dx, SD 2, range 1-10). Only 3 patients received targeted therapy alone, and the remaining 94 (mean age 42 yrs, SD 6 years) received no systemic therapy within a year of diagnosis. 15% of these 94 (mean age 45 yrs, SD 3, range 41-49) experienced immediate menopause, 16% (mean age 43 yrs, SD 4, range 36-49) experienced menopause 2+ yrs after the age of diagnosis, and 69% (mean age 41 yrs, SD 7, range 20-49) remained pre- or perimenopausal at their final survey (on average 4 yrs after dx, SD 3, range 1-10). Conclusions: Chemotherapy for lung cancer causes amenorrhea in a substantial proportion of women dx’d with lung cancer while premenopausal. Further research on fertility and menopausal symptoms after lung cancer treatment, and on differences between regimens, is warranted.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2017.35.15_suppl.e20092
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2017
    detail.hit.zdb_id: 2005181-5
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  • 9
    Online Resource
    Online Resource
    Model for Predicting Breast Cancer Risk in Women With Atypical Hyperplasia
    American Society of Clinical Oncology (ASCO) ; 2018
    In:  Journal of Clinical Oncology Vol. 36, No. 18 ( 2018-06-20), p. 1840-1846
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 36, No. 18 ( 2018-06-20), p. 1840-1846
    Abstract: Women with atypical hyperplasia (AH) on breast biopsy have an aggregate increased risk of breast cancer (BC), but existing risk prediction models do not provide accurate individualized estimates of risk in this subset of high-risk women. Here, we used the Mayo benign breast disease cohort to develop and validate a model of BC risk prediction that is specifically for women with AH, which we have designated as AH-BC. Patients and Methods Retrospective cohorts of women age 18 to 85 years with pathologically confirmed benign AH from Rochester, MN, and Nashville, TN, were used for model development and external validation, respectively. Clinical risk factors and histologic features of the tissue biopsy were selected using L1-penalized Cox proportional hazards regression. Identified features were included in a Fine and Gray regression model to estimate BC risk, with death as a competing risk. Model discrimination and calibration were assessed in the model-building set and an external validation set. Results The model-building set consisted of 699 women with AH, 142 of whom developed BC (median follow-up, 8.1 years), and the external validation set consisted of 461 women with 114 later BC events (median follow-up, 11.4 years). The final AH-BC model included three covariates: age at biopsy, age at biopsy squared, and number of foci of AH. At 10 years, the AH-BC model demonstrated good discrimination (0.63 [95% CI, 0.57 to 0.70]) and calibration (0.87 [95% CI, 0.66 to 1.24] ). In the external validation set, the model showed acceptable discrimination (0.59 [95% CI, 0.51 to 0.67]) and calibration (0.91 [95% CI, 0.65 to 1.42] ). Conclusion We have created a new model with which to refine BC risk prediction for women with AH. The AH-BC model demonstrates good discrimination and calibration, and it validates in an external data set.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2017.75.9480
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2018
    detail.hit.zdb_id: 2005181-5
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  • 10
    Online Resource
    Online Resource
    Evaluating the Influence of Quality Control Decisions and Software Algorithms on SNP Calling for the Affymetrix 6.0 SNP Array Platform
    S. Karger AG ; 2011
    In:  Human Heredity Vol. 71, No. 4 ( 2011), p. 221-233
    Details
    In: Human Heredity, S. Karger AG, Vol. 71, No. 4 ( 2011), p. 221-233
    Abstract: 〈 i 〉 Objective: 〈 /i 〉 Our goal was to evaluate the influence of quality control (QC) decisions using two genotype calling algorithms, CRLMM and Birdseed, designed for the Affymetrix SNP Array 6.0. 〈 i 〉 Methods: 〈 /i 〉 Various QC options were tried using the two algorithms and comparisons were made on subject and call rate and on association results using two data sets. 〈 i 〉 Results: 〈 /i 〉 For Birdseed, we recommend using the contrast QC instead of QC call rate for sample QC. For CRLMM, we recommend using the signal-to-noise rate ≧4 for sample QC and a posterior probability of 90% for genotype accuracy. For both algorithms, we recommend calling the genotype separately for each plate, and dropping SNPs with a lower call rate ( 〈 95%) before evaluating samples with lower call rates. To investigate whether the genotype calls from the two algorithms impacted the genome-wide association results, we performed association analysis using data from the GENOA cohort; we observed that the number of significant SNPs were similar using either CRLMM or Birdseed. 〈 i 〉 Conclusions: 〈 /i 〉 Using our suggested workflow both algorithms performed similarly; however, fewer samples were removed and CRLMM took half the time to run our 854 study samples (4.2 h) compared to Birdseed (8.4 h).
    Type of Medium: Online Resource
    ISSN: 0001-5652 , 1423-0062
    URL: Article
    DOI: 10.1159/000328843
    RVK:
    WA 15000
    Language: English
    Publisher: S. Karger AG
    Publication Date: 2011
    detail.hit.zdb_id: 1482710-4
    SSG: 12
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