scholarly journals Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Maeve Mullooly ◽  
Babak Ehteshami Bejnordi ◽  
Ruth M. Pfeiffer ◽  
Shaoqi Fan ◽  
Maya Palakal ◽  
...  
Keyword(s):  
Random Forest ◽  
Cancer Risk ◽  
Breast Density ◽  
Morphological Structure ◽  
Breast Cancer Risk Factor ◽  
Mammographic Breast Density ◽  
Low Density ◽  
Learning Approaches ◽  
Operating Characteristics ◽  
Fibroglandular Volume

AbstractBreast density, a breast cancer risk factor, is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume. Its histology is incompletely characterized. Here we use deep learning approaches to identify histologic correlates in radiologically-guided biopsies that may underlie breast density and distinguish cancer among women with elevated and low density. We evaluated hematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n = 852 patients). Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. In total 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume (n = 588). Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A second random forest classifier was trained to predict diagnosis (invasive vs. benign); performance was assessed using area under receiver-operating characteristics curves (AUC). Using extracted features, regression models predicted global (r = 0.94) and localized (r = 0.93) fibroglandular volume, with fat and non-fatty stromal content representing the strongest correlates, followed by epithelial organization rather than quantity. For predicting cancer among high and low fibroglandular volume, the classifier achieved AUCs of 0.92 and 0.84, respectively, with epithelial organizational features ranking most important. These results suggest non-fatty stroma, fat tissue quantities and epithelial region organization predict fibroglandular volume. The model holds promise for identifying histological correlates of cancer risk in patients with high and low density and warrants further evaluation.

Mammographic breast density, its changes, and breast cancer risk in premenopausal and postmenopausal women

Cancer ◽  
2020 ◽  
Vol 126 (21) ◽  
pp. 4687-4696
Author(s):  
Eun Young Kim ◽  
Yoosoo Chang ◽  
Jiin Ahn ◽  
Ji‐Sup Yun ◽  
Yong Lai Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Postmenopausal Women ◽  
Breast Density ◽  
Mammographic Breast Density

scholarly journals Review of: Are breast density and bone mineral density independent risk factors for breast cancer?

2005 ◽  
Vol 8 (11) ◽  
Author(s):  
J. L. Hopper
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Bone Mineral Density ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Correlation Coefficient ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Mineral Density ◽  
Increased Risk

Citation of original article:K. Kerlikowske, J. Shepherd, J. Creasman, J. A. Tice, E. Ziv, S. R. Cummings. Are breast density and bone mineral density independent risk factors for breast cancer. Journal of the National Cancer Institute 2005; 97(7): 368–74.Abstract of the original articleBackground: Mammographic breast density and bone mineral density (BMD) are markers of cumulative exposure to estrogen. Previous studies have suggested that women with high mammographic breast density or high BMD are at increased risk of breast cancer. We determined whether mammographic breast density and BMD of the hip and spine are correlated and independently associated with breast cancer risk. Methods: We conducted a cross-sectional study (N = 15 254) and a nested case-control study (of 208 women with breast cancer and 436 control subjects) among women aged 28 years or older who had a screening mammography examination and hip BMD measurement within 2 years. Breast density for 3105 of the women was classified using the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) categories, and percentage mammographic breast density among the case patients and control subjects was quantified with a computer-based threshold method. Spearman rank partial correlation coefficient and Pearson's correlation coefficient were used to examine correlations between BI-RADS breast density and BMD and between percentage mammographic breast density and BMD, respectively, in women without breast cancer. Logistic regression was used to examine the association of breast cancer with percentage mammographic breast density and BMD. All statistical tests were two-sided. Results: Neither BI-RADS breast density nor percentage breast density was correlated with hip or spine BMD (correlation coefficient = −.02 and −.01 for BI-RADS, respectively, and −2.06 and .01 for percentage breast density, respectively). Neither hip BMD nor spine BMD had a statistically significant relationship with breast cancer risk. Women with breast density in the highest sextile had an approximately threefold increased risk of breast cancer compared with women in the lowest sextile (odds ratio: 2.7; 95% confidence interval: 1.4–5.4); adjusting for hip or spine BMD did not change the association between breast density and breast cancer risk. Conclusion: Breast density is strongly associated with increased risk of breast cancer, even after taking into account reproductive and hormonal risk factors, whereas BMD, although a possible marker of lifetime exposure to estrogen, is not. Thus, a component of breast density that is independent of estrogen-mediated effects may contribute to breast cancer risk.

Determination of breast density by bioimpedance.

2012 ◽  
Vol 30 (27_suppl) ◽  
pp. 56-56
Author(s):  
Karina Bukhanov ◽  
Joel S. Ironstone ◽  
Cindy Basso ◽  
Tina Bilodeau
Keyword(s):  
Breast Cancer ◽  
Primary Care ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Correlation Coefficient ◽  
Breast Density ◽  
Pearson Correlation ◽  
Mammographic Breast Density ◽  
Pearson Correlation Coefficient ◽  
Dense Breasts

56 Background: Mammographic breast density is a significant risk factor for breast cancer. Women with extremely dense breasts are at 4-to-6 times the risk of developing breast cancer than women with primarily fatty breast tissue. Electrical Breast Densitometry (EBD) is a new technique that assesses breast density. EBD is non-ionizing, fast, has low cost per test ($20-$30) and may help in breast cancer risk assessment in the primary care setting. Methods: This study evaluated the feasibility of the EBD in an IRB-approved pilot study of 20 patients. The study used a custom-made self-adhesive electrode (SenoSENSE Medical Systems, Toronto, Canada) interfaced to an off-the-shelf impedance meter (Bodystat 1500, Bodystat, Isle of Man, UK) with a customized cable. On the same day as the subject’s scheduled mammogram, impedance measurements were acquired for each breast. Mammogram densities were scored by a trained radiologist using standard BiRADS breast density categories 1 to 4. Results: A high correlation coefficient was observed (Pearson correlation coefficient >0.80) between breast density determined by the EBD and the BiRADS breast density score. In addition a statistically significant difference was observed between dense categories (BiRADS 3,4) and fatty categories (BiRADS 1,2) (p<0.01), as well as between extremely dense breasts (BiRADS 4) and all other categories (p<0.01). Very high correlation (Pearson correlation coefficient >0.95) was observed between EBD measurements on the left and right breasts. Previous studies have reported a left/right correlation of 0.89 for blinded mammography readers. Conclusions: These results suggests that the EBD measure may be less variable than mammographic estimates of density. The results of the study suggest that Electrical Breast Densitometry is a promising technique for the assessment of breast density and the ability to aid in evaluation of breast cancer risk. It can be reasonably deployed at primary care facilities.

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