Electrical Impedance Scanning for Breast Cancer Risk Stratification in Young Women

2007 ◽  
pp. 675-678
Author(s):  
Alexander Stojadinovic ◽  
M.D., Aviram Nissan ◽  
M.D., Craig D. Shriver ◽  
M.D. Sarah Lenington ◽  
Ph.D., David Gur
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Young Women ◽  
Electrical Impedance ◽  
Electrical Impedance Scanning

Electrical impedance scanning as a new breast cancer risk stratification tool for young women

2008 ◽  
Vol 97 (2) ◽  
pp. 112-120 ◽  
Author(s):  
Alexander Stojadinovic ◽  
Aviram Nissan ◽  
Craig D. Shriver ◽  
Elizabeth A. Mittendorf ◽  
Mark D. Akin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Young Women ◽  
Electrical Impedance ◽  
Risk Stratification Tool ◽  
Electrical Impedance Scanning

scholarly journals A GMM-based breast cancer risk stratification using a resonance-frequency electrical impedance spectroscopy

2011 ◽  
Vol 38 (3) ◽  
pp. 1649-1659 ◽  
Author(s):  
Dror Lederman ◽  
Bin Zheng ◽  
Xingwei Wang ◽  
Jules H. Sumkin ◽  
David Gur
Keyword(s):  
Breast Cancer ◽  
Impedance Spectroscopy ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Resonance Frequency ◽  
Electrical Impedance ◽  
Electrical Impedance Spectroscopy

scholarly journals Comparisons Among Multiple Machine Learning Based Classifiers for Breast Cancer Risk Stratification Using Electrical Impedance Spectroscopy

2020 ◽  
Vol 4 (4) ◽  
Author(s):  
Md. Toukir Ahmed ◽  
Md. Rayhanul Masud ◽  
Abdullah Al Mamun
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Impedance Spectroscopy ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Electrical Impedance ◽  
Performance Metrics ◽  
Electrical Impedance Spectroscopy

Nowadays, women worldwide are affected greatly by Breast cancer. The consequences of the disease and the number of affected are so alarming that it requires immediate attention. Prediction of the disease is the primary and most significant route to prevention of it. This study aims to have a comparison among multiple machine learning based classifiers for breast cancer risk stratification using resonance-frequency electrical impedance spectroscopy. Five machine learning based classifiers namely- Naïve Bayes, Multilayer perceptron, J48, Bagging and Random Forest were applied to the dataset and a comparison was made based on different performance metrics. The study demonstrated that Random Forest classifier performed slightly better than the others in both splitting and folding of the dataset.

scholarly journals Health professionals’ perspectives on breast cancer risk stratification: understanding evaluation of risk versus screening for disease

2019 ◽  
Vol 40 (1) ◽  
Author(s):  
Svetlana Puzhko ◽  
Justin Gagnon ◽  
Jacques Simard ◽  
Bartha Maria Knoppers ◽  
Sophia Siedlikowski ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Health Professionals

scholarly journals Environmental Exposures during Puberty: Window of Breast Cancer Risk and Epigenetic Damage

2020 ◽  
Vol 17 (2) ◽  
pp. 493 ◽  
Author(s):  
Rama Natarajan ◽  
Dana Aljaber ◽  
Dawn Au ◽  
Christine Thai ◽  
Angelica Sanchez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Young Women ◽  
Food Additives ◽  
Environmental Exposures ◽  
Cost Effective ◽  
Environmental Damage ◽  
Environmental Carcinogens ◽  
Molecular Damage

During puberty, a woman’s breasts are vulnerable to environmental damage (“window of vulnerability”). Early exposure to environmental carcinogens, endocrine disruptors, and unhealthy foods (refined sugar, processed fats, food additives) are hypothesized to promote molecular damage that increases breast cancer risk. However, prospective human studies are difficult to perform and effective interventions to prevent these early exposures are lacking. It is difficult to prevent environmental exposures during puberty. Specifically, young women are repeatedly exposed to media messaging that promotes unhealthy foods. Young women living in disadvantaged neighborhoods experience additional challenges including a lack of access to healthy food and exposure to contaminated air, water, and soil. The purpose of this review is to gather information on potential exposures during puberty. In future directions, this information will be used to help elementary/middle-school girls to identify and quantitate environmental exposures and develop cost-effective strategies to reduce exposures.

scholarly journals Pubertal timing and breast density in young women: a prospective cohort study

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Lauren C. Houghton ◽  
Seungyoun Jung ◽  
Rebecca Troisi ◽  
Erin S. LeBlanc ◽  
Linda G. Snetselaar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Breast Density ◽  
Young Women ◽  
Geometric Mean ◽  
Age At Onset ◽  
Breast Development ◽  
Increased Risk ◽  
Pubertal Tempo

Abstract Background Earlier age at onset of pubertal events and longer intervals between them (tempo) have been associated with increased breast cancer risk. It is unknown whether the timing and tempo of puberty are associated with adult breast density, which could mediate the increased risk. Methods From 1988 to 1997, girls participating in the Dietary Intervention Study in Children (DISC) were clinically assessed annually between ages 8 and 17 years for Tanner stages of breast development (thelarche) and pubic hair (pubarche), and onset of menses (menarche) was self-reported. In 2006–2008, 182 participants then aged 25–29 years had their percent dense breast volume (%DBV) measured by magnetic resonance imaging. Multivariable, linear mixed-effects regression models adjusted for reproductive factors, demographics, and body size were used to evaluate associations of age and tempo of puberty events with %DBV. Results The mean (standard deviation) and range of %DBV were 27.6 (20.5) and 0.2–86.1. Age at thelarche was negatively associated with %DBV (p trend = 0.04), while pubertal tempo between thelarche and menarche was positively associated with %DBV (p trend = 0.007). %DBV was 40% higher in women whose thelarche-to-menarche tempo was 2.9 years or longer (geometric mean (95%CI) = 21.8% (18.2–26.2%)) compared to women whose thelarche-to-menarche tempo was less than 1.6 years (geometric mean (95%CI) = 15.6% (13.9–17.5%)). Conclusions Our results suggest that a slower pubertal tempo, i.e., greater number of months between thelarche and menarche, is associated with higher percent breast density in young women. Future research should examine whether breast density mediates the association between slower tempo and increased breast cancer risk.

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