scholarly journals Estrogen receptor positive breast cancers in BRCA1 mutation carriers: clinical risk factors and pathologic features

2010 ◽  
Vol 12 (1) ◽  
Author(s):  
Nadine Tung ◽  
Yihong Wang ◽  
Laura C Collins ◽  
Jennifer Kaplan ◽  
Hailun Li ◽  
...  
2012 ◽  
Vol 36 (10) ◽  
pp. 1483-1488 ◽  
Author(s):  
Jennifer S. Kaplan ◽  
Stuart J. Schnitt ◽  
Laura C. Collins ◽  
Yihong Wang ◽  
Judy E. Garber ◽  
...  

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 1507-1507
Author(s):  
R. T. Chlebowski ◽  
G. L. Anderson ◽  
D. S. Lane ◽  
A. Aragaki ◽  
T. Rohan ◽  
...  

1507 Background: Chemoprevention strategies for estrogen receptor positive (ER+) breast cancers are emerging, especially for postmenopausal women, but require methods of targeting appropriate populations. Our objective was to improve the Breast Cancer Risk Assessment Tool [Gail Model (GM)] for estimating ER+ breast cancer risk. Methods: A prospective cohort involving 161,809 postmenopausal women aged 50–79 years, (93,676 in the observational study (OS) and 68,132 in clinical trials (CT)) at Women’s Health Initiative (WHI) Clinical Centers had comprehensive assessment of lifestyle, medication use and breast cancer risk factors. Breast cancer risk from the GM and other models incorporating additional or fewer risk factors and five year incidence of ER + and ER negative (ER-) invasive breast cancers were determined. Main outcome measures were concordance statistics for models predicting breast cancer risk. Results: Of 148,266 women meeting eligibility criteria, (no prior breast cancer and/or mastectomy), 3,236 developed breast cancer. Chronological age and age at menopause, both GM components, were significantly associated with only ER+ but not ER- breast cancer risk (p<0.05 for heterogeneity test). The GM predicted population-based ER+ cancer risk with reasonable accuracy (concordance statistic 0.60, 95% confidence interval (CI) 0.58 to 0.62) but for ER- cancers, the results were equivalent to chance allocation (concordance statistic 0.49, 95% CI 0.45 to 0.54). For ER+ cancers, no additional risk factors improved the GM prediction. However, a simpler model, developed in the OS and tested in the CT population, including only age, family history, and benign breast biopsy was comparable to GM in ER+ breast cancer prediction (concordance statistics 0.58, 95% CI 0.56 to 0.60). Using this model, all women ≥ 55 years old (or ≥ 60 year old if African American) with either a prior breast biopsy or first degree breast cancer family history had five year breast cancer risk of ≥ 1.8%. Conclusions: In postmenopausal women with comprehensive mammography use, the GM identifies populations at increased risk for ER+ breast cancer but not for ER- cancer. A model with fewer variables provides a simpler alternative for identifying populations appropriate for breast cancer chemoprevention interventions. No significant financial relationships to disclose.


2021 ◽  
Vol 11 ◽  
Author(s):  
Zhongyi Wang ◽  
Fan Lin ◽  
Heng Ma ◽  
Yinghong Shi ◽  
Jianjun Dong ◽  
...  

PurposeWe developed and validated a contrast-enhanced spectral mammography (CESM)-based radiomics nomogram to predict neoadjuvant chemotherapy (NAC)-insensitive breast cancers prior to treatment.MethodsWe enrolled 117 patients with breast cancer who underwent CESM examination and NAC treatment from July 2017 to April 2019. The patients were grouped randomly into a training set (n = 97) and a validation set (n = 20) in a ratio of 8:2. 792 radiomics features were extracted from CESM images including low-energy and recombined images for each patient. Optimal radiomics features were selected by using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation, to develop a radiomics score in the training set. A radiomics nomogram incorporating the radiomics score and independent clinical risk factors was then developed using multivariate logistic regression analysis. With regard to discrimination and clinical usefulness, radiomics nomogram was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC) and decision curve analysis (DCA).ResultsThe radiomics nomogram that incorporates 11 radiomics features and 3 independent clinical risk factors, including Ki-67 index, background parenchymal enhancement (BPE) and human epidermal growth factor receptor-2 (HER-2) status, showed an encouraging discrimination power with AUCs of 0.877 [95% confidence interval (CI) 0.816 to 0.924] and 0.81 (95% CI 0.575 to 0.948) in the training and validation sets, respectively. DCA revealed the increased clinical usefulness of this nomogram.ConclusionThe proposed radiomics nomogram that integrates CESM-derived radiomics features and clinical parameters showed potential feasibility for predicting NAC-insensitive breast cancers.


Angiology ◽  
2021 ◽  
pp. 000331972110280
Author(s):  
Sukru Arslan ◽  
Ahmet Yildiz ◽  
Okay Abaci ◽  
Urfan Jafarov ◽  
Servet Batit ◽  
...  

The data with respect to stable coronary artery disease (SCAD) are mainly confined to main vessel disease. However, there is a lack of information and long-term outcomes regarding isolated side branch disease. This study aimed to evaluate long-term major adverse cardiac and cerebrovascular events (MACCEs) in patients with isolated side branch coronary artery disease (CAD). A total of 437 patients with isolated side branch SCAD were included. After a median follow-up of 38 months, the overall MACCE and all-cause mortality rates were 14.6% and 5.9%, respectively. Among angiographic features, 68.2% of patients had diagonal artery and 82.2% had ostial lesions. In 28.8% of patients, the vessel diameter was ≥2.75 mm. According to the American College of Cardiology lesion classification, 84.2% of patients had either class B or C lesions. Age, ostial lesions, glycated hemoglobin A1c, and neutrophil levels were independent predictors of MACCE. On the other hand, side branch location, vessel diameter, and lesion complexity did not affect outcomes. Clinical risk factors seem to have a greater impact on MACCE rather than lesion morphology. Therefore, the treatment of clinical risk factors is of paramount importance in these patients.


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