scholarly journals Recommendation and Acceptance of Counselling for Familial Cancer Risk in Newly Diagnosed Breast Cancer Cases

Breast Care ◽  
2021 ◽  
pp. 1-6
Author(s):  
Karin Kast ◽  
Julia Häfner ◽  
Evelin Schröck ◽  
Arne Jahn ◽  
Carmen Werner ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Risk ◽  
Genetic Counselling ◽  
Primary Breast Cancer ◽  
Ductal Carcinoma ◽  
Familial Cancer ◽  
University Hospital ◽  
Tumor Board ◽  
Newly Diagnosed ◽  
Familial Cancer Risk

<b><i>Background:</i></b> In clinical routine, not every patient who is offered genetic counselling and diagnostics in order to investigate a familial cancer risk predisposition opts for it. Little is known about acceptance of counselling and testing in newly diagnosed breast cancer cases in Germany. <b><i>Methods:</i></b> All primary breast cancer cases and patients with DCIS (ductal carcinoma in situ) treated at the University Hospital of Dresden between 2016 and 2019 were included. The number of tumor board recommendations for genetic counselling on the basis of the GC-HBOC risk criteria was recorded. Acceptance was analyzed by number of cases with counselling in the GC-HBOC-Center Dresden. <b><i>Results:</i></b> Of 996 primary breast cancer and DCIS cases, 262 (26.3%) were eligible for genetic counselling. Recommendation for genetic counselling was accepted by 64.1% (168/262). Of these 90.5% (152/168) opted for molecular genetic analysis. The acceptance rate for counselling increased between 2016 and 2019 from 58.3 to 72.6%. Altogether, 20.4% (31/152) patients were found to carry a pathogenic variant in the breast cancer genes <i>BRCA1</i> or <i>BRCA2</i>. <b><i>Conclusion:</i></b> Acceptance of recommendation is increasing as clinical consequences augment. Optimization in providing information about hereditary cancer risk and in accessibility of counselling and testing is required to further improve acceptance of recommendation.

Screening for familial cancer risk: Focus on breast cancer

Maturitas ◽  
2017 ◽  
Vol 105 ◽  
pp. 69-77 ◽  
Author(s):  
Christine Rousset-Jablonski ◽  
Anne Gompel
Keyword(s):  
Breast Cancer ◽  
Cancer Risk ◽  
Familial Cancer ◽  
Familial Cancer Risk

scholarly journals Cumulative Risk Distribution for Interval Invasive Second Breast Cancers After Negative Surveillance Mammography

2018 ◽  
Vol 36 (20) ◽  
pp. 2070-2077 ◽  
Author(s):  
Janie M. Lee ◽  
Linn Abraham ◽  
Diana L. Lam ◽  
Diana S.M. Buist ◽  
Karla Kerlikowske ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Risk ◽  
Primary Breast Cancer ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Interval Cancer ◽  
Invasive Cancer ◽  
Breast Cancers ◽  
Surveillance Mammography

Purpose The aim of the current study was to characterize the risk of interval invasive second breast cancers within 5 years of primary breast cancer treatment. Methods We examined 65,084 surveillance mammograms from 18,366 women with a primary breast cancer diagnosis of unilateral ductal carcinoma in situ or stage I to III invasive breast carcinoma performed from 1996 to 2012 in the Breast Cancer Surveillance Consortium. Interval invasive breast cancer was defined as ipsilateral or contralateral cancer diagnosed within 1 year after a negative surveillance mammogram. Discrete-time survival models—adjusted for all covariates—were used to estimate the probability of interval invasive cancer, given the risk factors for each surveillance round, and aggregated across rounds to estimate the 5-year cumulative probability of interval invasive cancer. Results We observed 474 surveillance-detected cancers—334 invasive and 140 ductal carcinoma in situ—and 186 interval invasive cancers which yielded a cancer detection rate of 7.3 per 1,000 examinations (95% CI, 6.6 to 8.0) and an interval invasive cancer rate of 2.9 per 1,000 examinations (95% CI, 2.5 to 3.3). Median cumulative 5-year interval cancer risk was 1.4% (interquartile range, 0.8% to 2.3%; 10th to 90th percentile range, 0.5% to 3.7%), and 15% of women had ≥ 3% 5-year interval invasive cancer risk. Cumulative 5-year interval cancer risk was highest for women with estrogen receptor– and progesterone receptor–negative primary breast cancer (2.6%; 95% CI, 1.7% to 3.5%), interval cancer presentation at primary diagnosis (2.2%; 95% CI, 1.5% to 2.9%), and breast conservation without radiation (1.8%; 95% CI, 1.1% to 2.4%). Conclusion Risk of interval invasive second breast cancer varies across women and is influenced by characteristics that can be measured at initial diagnosis, treatment, and imaging. Risk prediction models that evaluate the risk of cancers not detected by surveillance mammography should be developed to inform discussions of tailored surveillance.

scholarly journals Prospective comparison of the diagnostic accuracy of 18F-FDG PET/MRI, MRI, CT, and bone scintigraphy for the detection of bone metastases in the initial staging of primary breast cancer patients

2021 ◽  
Author(s):  
Nils Martin Bruckmann ◽  
Julian Kirchner ◽  
Lale Umutlu ◽  
Wolfgang Peter Fendler ◽  
Robert Seifert ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bone Metastases ◽  
Cancer Patients ◽  
Bone Scintigraphy ◽  
Fdg Pet ◽  
Primary Breast Cancer ◽  
Whole Body ◽  
Newly Diagnosed ◽  
Breast Cancer Patients ◽  
Initial Staging

Abstract Objectives To compare the diagnostic performance of [18F]FDG PET/MRI, MRI, CT, and bone scintigraphy for the detection of bone metastases in the initial staging of primary breast cancer patients. Material and methods A cohort of 154 therapy-naive patients with newly diagnosed, histopathologically proven breast cancer was enrolled in this study prospectively. All patients underwent a whole-body [18F]FDG PET/MRI, computed tomography (CT) scan, and a bone scintigraphy prior to therapy. All datasets were evaluated regarding the presence of bone metastases. McNemar χ2 test was performed to compare sensitivity and specificity between the modalities. Results Forty-one bone metastases were present in 7/154 patients (4.5%). Both [18F]FDG PET/MRI and MRI alone were able to detect all of the patients with histopathologically proven bone metastases (sensitivity 100%; specificity 100%) and did not miss any of the 41 malignant lesions (sensitivity 100%). CT detected 5/7 patients (sensitivity 71.4%; specificity 98.6%) and 23/41 lesions (sensitivity 56.1%). Bone scintigraphy detected only 2/7 patients (sensitivity 28.6%) and 15/41 lesions (sensitivity 36.6%). Furthermore, CT and scintigraphy led to false-positive findings of bone metastases in 2 patients and in 1 patient, respectively. The sensitivity of PET/MRI and MRI alone was significantly better compared with CT (p < 0.01, difference 43.9%) and bone scintigraphy (p < 0.01, difference 63.4%). Conclusion [18F]FDG PET/MRI and MRI are significantly better than CT or bone scintigraphy for the detection of bone metastases in patients with newly diagnosed breast cancer. Both CT and bone scintigraphy show a substantially limited sensitivity in detection of bone metastases. Key Points • [18F]FDG PET/MRI and MRI alone are significantly superior to CT and bone scintigraphy for the detection of bone metastases in patients with newly diagnosed breast cancer. • Radiation-free whole-body MRI might serve as modality of choice in detection of bone metastases in breast cancer patients.

scholarly journals Biomarker Modulation following Short-Term Vorinostat in Women with Newly Diagnosed Primary Breast Cancer

2013 ◽  
Vol 19 (14) ◽  
pp. 4008-4016 ◽  
Author(s):  
Vered Stearns ◽  
Lisa K. Jacobs ◽  
MaryJo Fackler ◽  
Theodore N. Tsangaris ◽  
Michelle A. Rudek ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Primary Breast Cancer ◽  
Newly Diagnosed ◽  
Short Term

scholarly journals EPID-11. THE CONTRIBUTION OF THE rs55705857 G ALLELE TO FAMILIAL CANCER RISK USING THE UTAH POPULATION DATABASE

2016 ◽  
Vol 18 (suppl_6) ◽  
pp. vi57-vi57
Author(s):  
Sarah Hummel ◽  
Wendy Kohlmann ◽  
Thomas Kollmeyer ◽  
Robert Jenkins ◽  
Joshua Sonnen ◽  
...  
Keyword(s):  
Cancer Risk ◽  
Familial Cancer ◽  
Population Database ◽  
Familial Cancer Risk

Toward robust mammography-based models for breast cancer risk

2021 ◽  
Vol 13 (578) ◽  
pp. eaba4373 ◽  
Author(s):  
Adam Yala ◽  
Peter G. Mikhael ◽  
Fredrik Strand ◽  
Gigin Lin ◽  
Kevin Smith ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Massachusetts General Hospital ◽  
The United States ◽  
University Hospital ◽  
Chang Gung Memorial Hospital ◽  
Risk Models

Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines. To bring deep learning risk models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. We developed Mirai, a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and tested on held-out test sets from MGH, Karolinska University Hospital in Sweden, and Chang Gung Memorial Hospital (CGMH) in Taiwan, obtaining C-indices of 0.76 (95% confidence interval, 0.74 to 0.80), 0.81 (0.79 to 0.82), and 0.79 (0.79 to 0.83), respectively. Mirai obtained significantly higher 5-year ROC AUCs than the Tyrer-Cuzick model (P < 0.001) and prior deep learning models Hybrid DL (P < 0.001) and Image-Only DL (P < 0.001), trained on the same dataset. Mirai more accurately identified high-risk patients than prior methods across all datasets. On the MGH test set, 41.5% (34.4 to 48.5) of patients who would develop cancer within 5 years were identified as high risk, compared with 36.1% (29.1 to 42.9) by Hybrid DL (P = 0.02) and 22.9% (15.9 to 29.6) by the Tyrer-Cuzick model (P < 0.001).

scholarly journals Bisphosphonates and primary breast cancer risk: an updated systematic review and meta-analysis involving 963,995 women

2019 ◽  
Vol Volume 11 ◽  
pp. 593-603 ◽  
Author(s):  
Yupeng Liu ◽  
Xiaosan Zhang ◽  
Hongru Sun ◽  
Shu Zhao ◽  
Yuxue Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Primary Breast Cancer ◽  
Meta Analysis
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