Mammographic Parenchymal Patterns Research Articles

Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect.

In women with biopsy-proven breast cancer, histologically normal areas of the parenchyma have shown molecular similarity to the tumor, supporting a potential cancer field effect. The purpose of this work was to investigate relationships of human-engineered radiomic and deep learning features between regions across the breast in mammographic parenchymal patterns and specimen radiographs. This study included mammograms from 74 patients with at least 1 identified malignant tumor, of whom 32 also possessed intraoperative radiographs of mastectomy specimens. Mammograms were acquired with a Hologic system and specimen radiographs were acquired with a Fujifilm imaging system. All images were retrospectively collected under an Institutional Review Board-approved protocol. Regions of interest (ROI) of were selected from three regions: within the identified tumor, near to the tumor, and far from the tumor. Radiographic texture analysis was used to extract 45 radiomic features and transfer learning was used to extract 20 deep learning features in each region. Kendall's Tau-b and Pearson correlation tests were performed to assess relationships between features in each region. Statistically significant correlations in select subgroups of features with tumor, near to the tumor, and far from the tumor ROI regions were identified in both mammograms and specimen radiographs. Intensity-based features were found to show significant correlations with ROI regions across both modalities. Results support our hypothesis of a potential cancer field effect, accessible radiographically, across tumor and non-tumor regions, thus indicating the potential for computerized analysis of mammographic parenchymal patterns to predict breast cancer risk.

Correlation of the BI-RADS assessment categories of Papua New Guinean women with mammographic parenchymal patterns, age and diagnosis.

IntroductionWomen with increased breast density are at increased risk of breast cancer. The aim of this research is to evidence for the first time the mammographic breast findings of Papua New Guinean (PNG) women and the relationship between Breast Imaging‐Reporting and Data System (BI‐RADS) assessment, mammographic parenchymal patterns (MPPs) and age.MethodsA retrospective analysis of 1357 mammograms of women imaged at the Pacific International Hospital (PIH) from August 2006 to July 2010 was undertaken. Mammographic findings were categorised using the BI‐RADS Atlas® 5th Edition. MPPs were recorded for each woman using the Tabár Pattern I‐V classification system. Age was recorded in years. Statistical analysis was by descriptive analysis and Kruskal–Wallis with Dunn’s post‐test and Spearman’s rho correlation for inferential analysis.ResultsTrue pathological findings (benign and malignant); BI‐RADS 2–5 were noted in 111 women (8.2%); 1242 (91.5%) were negative. BI‐RADS categories for malignancy were reported in 16 (88.9%) of women aged 30 to 60 years. The lower risk Tabár Type I, II and III MPPs were associated with 94.4% (n = 17) of malignancies. Linear correlations between variables were weak and not statistically significant: age and Tabár pattern r = 0.031, P = 0.0261; age and BI‐RADS r = 0.018, P = 0.517; Tabár pattern and BI‐RADS r = 0.020, P = 0.459 (n = 1357).ConclusionThere was no correlation demonstrated between BI‐RADS category, age and MPP. Importantly, there was no correlation demonstrated between BI‐RADS categories 4 and 5 for breast malignancy and high‐risk Tabár Type IV and V MPPs. The results of this study again reflect that the incidence of breast cancer in PNG cannot be explained by breast density and suggest that any formalised screening program in PNG has a target age group aimed at women younger than that of Western screening programs.

Exploring correlations between the breast density of the women of Papua New Guinea and breast cancer risk factors

IntroductionPapua New Guinea (PNG), has experienced an increase in breast cancer incidence correlating to the westernisation of the country. Increased breast density is known to increase breast cancer risk. This study investigates if there are any factors unique to the women of PNG that may impact breast density and breast cancer risk. MethodA survey was undertaken of 1,161 women who had undergone mammographic imaging at the Pacific International Hospital (PIH). Results were correlated with the five Tabár mammographic parenchymal patterns (TP), recorded for each woman and geographical location, parity, breast size, occupation, marital and menstrual status, smoking and alcohol consumption, hormone replacement therapy (HRT), and exercise. Statistical analysis was undertaken using chi-square test, Fisher's exact test and Odds Ratio (OR). ResultsRelationships were identified between TP and parity (p < 0.001), marital status (p < 0.001), smoking (p < 0.001), alcohol intake (p = 0.029) and HRT (p = 0.029). There was no evidence of a relationship between pattern type and geographical location (p = 0.290), breast size (p = 0.592), occupation (p = 0.724), menstruation (p = 0.866) or exercise (p = 0.290). Married women, OR = 0.4004, CI 95% (0.2873–0.5579) and those with higher parity, OR 0.5034, CI 95% (0.3693–0.6862) were half as likely to have increased breast density reducing risk. ConclusionThere was no clear relationship across almost all data. Factors associated with increased breast density in PNG included parity, marital status, smoking, alcohol, and HRT use were evidenced in this snapshot of PNG women. Breast cancer risk was shown to be reduced for married women and those with increased parity.

Mammographic parenchymal pattern and breast cancer risk profile of Papua New Guinean women – A baseline study of the screening population

IntroductionThe aim of this research is to evidence for the first time the breast density of Papua New Guinean (PNG) women as described by mammographic parenchymal patterns (MPPs) and profile breast cancer risk; to examine the relationship of age and MPPs. MethodsA retrospective analysis of 1161 screening mammograms of women who had undergone imaging at the Pacific International Hospital (PIH) was undertaken. Mammograms were classified into one of five Tabár MPPs; age was recorded in years. Descriptive analysis of the data for pattern distribution and a chi-square test, to test for relationships between age and pattern type were undertaken. ResultsThe majority (51.42%) of women had Pattern I breasts; Pattern II (30.58%), Pattern III (4.31%), Pattern IV (7.24%), and Pattern V (6.46%). The mean age was 38.8 with a range of 30–80 years; there were no obvious differences in mean age across the categories of patterns. A chi-square test reported no evidence of a relationship between age and pattern type (p-value = 0.504). Pattern V differed from other patterns, with proportionally more women aged over 50 and less aged in their 40s. ConclusionThis study sets a baseline for future studies of the MPPs of PNG women, and demonstrated that in this snapshot of PNG women, there is no unique distribution of MPPs and no increased risk of breast cancer based on breast density profile. This result does not help to explain the high incidence of breast cancer in PNG. A more comprehensive study of the PNG screening population is required to validate this study.

Mammographic Parenchymal Patterns in Asymptomatic Women.

Background:Breast density has been found to be an independent risk factor for breast cancer. Mammographic breast parenchymal pattern or percent density is mainly a reflection of the proportion of glandular tissue to fatty tissue, and studies have shown that it works synergistically with other risk factors such as nulliparity in predicting breast cancer risk. This study analyses the various mammographic breast patterns and correlates this with some demographic variables and final Breast Imaging Reporting and Data System outcomes of asymptomatic women in our center.Materials and Methods:This is a prospective descriptive study of mammographic breast pattern in 459 females who presented at the breast imaging suite of our institution. Mammography was performed after completion of an assisted administered questionnaire for demographic information. A GE Senographe DMR machine using two standard views (craniocaudal and mediolateral oblique) and additional views were used when necessary.Results:A total of 459 women ranging in age from 34 to 80 years were included in the study, of which 46.6% were in the age range of 41 to 50 years. The scattered fibroglandular pattern was the most common pattern found (44%), and the homogeneous dense pattern was the least common (0.4%). A significant association with age and menopause status was found, while no association was found with age at the time of the woman's first delivery and family history of breast cancer.Conclusion:This study demonstrates that there is a significant association between breast cancer and age and menopause status. However, no correlation was found with the age of women at their first delivery and family history of breast cancer.

Left-right analysis of mammary gland development in retinoid X receptor-α+/- mice.

Left-right (L-R) differences in mammographic parenchymal patterns are an early predictor of breast cancer risk; however, the basis for this asymmetry is unknown. Here, we use retinoid X receptor alpha heterozygous null (RXRα+/-) mice to propose a developmental origin: perturbation of coordinated anterior-posterior (A-P) and L-R axial body patterning. We hypothesized that by analogy to somitogenesis-in which retinoic acid (RA) attenuation causes anterior somite pairs to develop L-R asynchronously-that RA pathway perturbation would likewise result in asymmetric mammary development. To test this, mammary glands of RXRα+/- mice were quantitatively assessed to compare left- versus right-side ductal epithelial networks. Unlike wild-type controls, half of the RXRα+/- thoracic mammary gland (TMG) pairs exhibited significant L-R asymmetry, with left-side reduction in network size. In RXRα+/- TMGs in which symmetry was maintained, networks had bilaterally increased size, with left networks showing greater variability in area and pattern. Reminiscent of posterior somites, whose bilateral symmetry is refractory to RA attenuation, inguinal mammary glands (IMGs) also had bilaterally increased network size, but no loss of symmetry. Together, these results demonstrate that mammary glands exhibit differential A-P sensitivity to RXRα heterozygosity, with ductal network symmetry markedly compromised in anterior but not posterior glands. As TMGs more closely model human breast development than IMGs, these findings raise the possibility that for some women, breast cancer risk may initiate with subtle axial patterning defects that result in L-R asymmetric growth and pattern of the mammary ductal epithelium.This article is part of the themed issue 'Provocative questions in left-right asymmetry'.

Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment.

BackgroundThe assessment of a woman’s risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation.Main textThe analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation.ConclusionsThe objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.

Mammographic parenchymal patterns and breast cancer risk in New South Wales North Coast Aboriginal and Torres Strait Islander women.

IntroductionThe objective of the study was to document the distribution of mammographic parenchymal patterns (MPP) of Indigenous Australian women attending BreastScreen New South Wales (NSW) North Coast, to profile breast cancer risk as it relates to breast density and to explore the correlation between MPP, breast size as described by the posterior nipple line (PNL) and age.MethodsEthics was granted from CQUniversity Human Research Ethics Committee, NSW Population Health Services Research Ethics Committee and the Aboriginal Health and Medical Research Council Ethics Committee. A quantitative retrospective analysis reviewed 502 screening mammograms against the Tabár I–V MPP classification system. The PNL was measured in millimetres (mm) and the age of the patient documented.ResultsA statistically significant variation in the distribution of MPP (P < 0.0001) was demonstrated, with patterns of I (23.9%), II (45.6%), III (10.4%), IV (15.9%) and V (4.2%). Statistically significant differences were noted in the age of subjects between patterns (P = 0.0002). Patterns I and V demonstrated statistically significant lower ages than II, III and IV (all P < 0.05). Pattern V demonstrated a statistically significant lower age than pattern I (P = 0.0393). Pattern V demonstrated a statistically significant lower PNL value than all other patterns (all P < 0.001/P < 0.0002); pattern II was statistically significantly higher in PNL value than all other patterns (P < 0.002/P < 0.001). No significant relationship was noted between PNL and age.ConclusionThe study demonstrated that no identifiable or unique distribution of MPP was noted in this snapshot of Indigenous women. A larger study of Indigenous Australian women is required for validation.

SU‐E‐J‐248: Contributions of Tumor and Stroma Phenotyping in Computer‐Aided Diagnosis

Purpose:To gain insight into the role of parenchyma stroma in the characterization of breast tumors by incorporating computerized mammographic parenchyma assessment into breast CADx in the task of distinguishing between malignant and benign lesions.Methods:This study was performed on 182 biopsy‐proven breast mass lesions, including 76 benign and 106 malignant lesions. For each full‐field digital mammogram (FFDM) case, our quantitative imaging analysis was performed on both the tumor and a region‐of‐interest (ROI) from the normal contralateral breast. The lesion characterization includes automatic lesion segmentation and feature extraction. Radiographic texture analysis (RTA) was applied on the normal ROIs to assess the mammographic parenchymal patterns of these contralateral normal breasts. Classification performance of both individual computer extracted features and the output from a Bayesian artificial neural network (BANN) were evaluated with a leave‐one‐lesion‐out method using receiver operating characteristic (ROC) analysis with area under the curve (AUC) as the figure of merit.Results:Lesion characterization included computer‐extracted phenotypes of spiculation, size, shape, and margin. For parenchymal pattern characterization, five texture features were selected, including power law beta, contrast, and edge gradient. Merging of these computer‐selected features using BANN classifiers yielded AUC values of 0.79 (SE=0.03) and 0.67 (SE=0.04) in the task of distinguishing between malignant and benign lesions using only tumor phenotypes and texture features from the contralateral breasts, respectively. Incorporation of tumor phenotypes with parenchyma texture features into the BANN yielded improved classification performance with an AUC value of 0.83 (SE=0.03) in the task of differentiating malignant from benign lesions.Conclusion:Combining computerized tumor and parenchyma phenotyping was found to significantly improve breast cancer diagnostic accuracy highlighting the need to consider both tumor and stroma in decision making.Funding: University of Chicago Dean Bridge Fund, NCI U24‐CA143848‐05, P50‐CA58223 Breast SPORE program, and Breast Cancer Research Foundation. COI: MLG is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. MLG is a cofounder and stockholder in Quantitative Insights.

Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls.

We statistically compare the contributions of parenchymal phenotypes to mammographic density in distinguishing between high-risk cases and low-risk controls. The age-matched evaluation included computerized mammographic assessment of breast percent density (PD) and parenchymal patterns (phenotypes of coarseness and contrast) from radiographic texture analysis (RTA) of the full-field digital mammograms from 456 cases: 53 women with BRCA1/2 gene mutations, 75 with unilateral cancer, and 328 at low risk of developing breast cancer. Image-based phenotypes of parenchymal pattern coarseness and contrast were each found to significantly discriminate between the groups; however, PD did not. From ROC analysis, PD alone yielded area under the fitted ROC curve (AUC) values of 0.53 ([Formula: see text]) and 0.57 ([Formula: see text]) in the classification task between BRCA1/2 gene-mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. In a round-robin evaluation with Bayesian artificial neural network (BANN) analysis, RTA yielded AUC values of 0.81 (95% confidence interval [0.71, 0.89]) and 0.70 (95% confidence interval [0.63, 0.77]) between the BRCA1/2 gene-mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. These results show that high-risk and low-risk women have different mammographic parenchymal patterns with significantly higher discrimination resulting from characteristics of the parenchymal patterns than just the breast PD.

A method to determine the mammographic regions that show early changes due to the development of breast cancer

It is well understood nowadays that changes in the mammographic parenchymal pattern are an indicator of a risk of breast cancer and we have developed a statistical method that estimates the mammogram regions where the parenchymal changes, due to breast cancer, occur. This region of interest is computed from a score map by utilising the anatomical breast coordinate system developed in our previous work. The method also makes an automatic scale selection to avoid overfitting while the region estimates are computed by a nested cross-validation scheme. In this way, it is possible to recover those mammogram regions that show a significant difference in classification scores between the cancer and the control group. Our experiments suggested that the most significant mammogram region is the region behind the nipple and that can be justified by previous findings from other research groups. This result was conducted on the basis of the cross-validation experiments on independent training, validation and testing sets from the case-control study of 490 women, of which 245 women were diagnosed with breast cancer within a period of 2–4 years after the baseline mammograms. We additionally generalised the estimated region to another, mini-MIAS study and showed that the transferred region estimate gives at least a similar classification result when compared to the case where the whole breast region is used. In all, by following our method, one most likely improves both preclinical and follow-up breast cancer screening, but a larger study population will be required to test this hypothesis.

Pilot study demonstrating potential association between breast cancer image‐based risk phenotypes and genomic biomarkers

In this pilot study, the authors examined associations between image-based phenotypes and genomic biomarkers. The potential genetic contribution of UGT2B genes to interindividual variation in breast density and mammographic parenchymal patterns is demonstrated by performing an association study between image-based phenotypes and genomic biomarkers [single-nucleotide polymorphism (SNP) genotypes]. This candidate-gene approach study included 179 subjects for whom both mammograms and blood DNA samples had been obtained. The full-field digital mammograms were acquired using a GE Senographe 2000D FFDM system (12-bit; 0.1 mm-pixel size). Regions-of-interest, 256 × 256 pixels in size, selected from the central breast region behind the nipple underwent computerized image analysis to yield image-based phenotypes of mammographic density and parenchymal texture patterns. SNP genotyping was performed using a Sequenom MassArray System. One hundred twenty three SNPs with minor allele frequency above 5% were genotyped for the UGT2B gene clusters, and used in the study. The association between the image-based phenotypes and genomic biomarkers was assessed with the Pearson correlation coefficient via thePLINK software, and included permutation and correction for multiple SNP comparisons. From the phenotype-genotype association analysis, a parenchyma texture coarseness feature was found to be correlated with SNP rs451632 after multiple test correction for the multiple SNPs (p = 0.022). The power law β, which is used to characterize the frequency component of texture patterns, was found to be correlated with SNP rs4148298 (p = 0.035). The authors' results indicate that UGT2B gene variation may contribute to interindividual variation in mammographic parenchymal patterns and breast density. Understanding the relationship between image-based phenotypes and genomic biomarkers may help understand the biologic mechanism for image-based biomarkers and yield a future role in personalized medicine.

Mammographic Parenchymal Patterns as an Imaging Marker of Endogenous Hormonal Exposure: A Preliminary Study in a High-Risk Population

Parenchymal texture patterns have been previously associated with breast cancer risk, yet their underlying biological determinants remain poorly understood. Here, we investigate the potential of mammographic parenchymal texture as a phenotypic imaging marker of endogenous hormonal exposure. A retrospective cohort study was performed. Digital mammography (DM) images in the craniocaudal (CC) view from 297 women, 154 without breast cancer and 143 with unilateral breast cancer, were analyzed. Menopause status was used as a surrogate of cumulative endogenous hormonal exposure. Parenchymal texture features were extracted and mammographic percent density (MD%) was computed using validated computerized methods. Univariate and multivariable logistic regression analysis was performed to assess the association between texture features and menopause status, after adjusting for MD% and hormonally related confounders. The receiver operating characteristic (ROC) area under the curve (AUC) of each model was estimated to evaluate the degree of association between the extracted mammographic features and menopause status. Coarseness, gray-level correlation, and fractal dimension texture features have a significant independent association with menopause status in the cancer-affected population; skewness and fractal dimension exhibit a similar association in the cancer-free population (P < .05). The ROC AUC of the logistic regression model including all texture features was 0.70 (P < .05) for cancer-affected and 0.63 (P < .05) for cancer-free women. Texture features retained significant association with menopause status (P < .05) after adjusting for MD%, age at menarche, ethnicity, contraception use, hormone replacement therapy, parity, and age at first birth. Mammographic texture patterns may reflect the effect of endogenous hormonal exposure on the breast tissue and may capture such effects beyond mammographic density. Differences in texture features between pre- and postmenopausal women are more pronounced in the cancer-affected population, which may be attributed to an increased association to breast cancer risk. Texture features could ultimately be incorporated in breast cancer risk assessment models as markers of hormonal exposure.

Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets

The purpose of this study was to demonstrate the robustness of our prior computerized texture analysis method for breast cancer risk assessment, which was developed initially on a limited dataset of screen-film mammograms. This current study investigated the robustness by (1) evaluating on a large clinical dataset, (2) using full-field digital mammograms (FFDM) as opposed to screen-film mammography, and (3) incorporating analyses over two types of high-risk patient sets, as well as patients at low risk for breast cancer. The evaluation included the analyses on the parenchymal patterns of women at high risk of developing of breast cancer, including both BRCA1/2 gene mutation carriers and unilateral cancer patients, and of women at low risk of developing breast cancer. A total of 456 cases, including 53 women with BRCA1/2 gene mutations, 75 women with unilateral cancer, and 328 low-risk women, were retrospectively collected under an institutional review board approved protocol. Regions-of-interest (ROIs), were manually selected from the central breast region immediately behind the nipple. These ROIs were subsequently used in computerized feature extraction to characterize the mammographic parenchymal patterns in the images. Receiver operating characteristic analysis was used to assess the performance of the computerized texture features in the task of distinguishing between high-risk and low-risk subjects. In a round robin evaluation on the FFDM dataset with Bayesian artificial neural network analysis, AUC values of 0.82 (95% confidence interval [0.75, 0.88]) and 0.73 (95% confidence interval [0.67, 0.78]) were obtained between BRCA1/2 gene mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. These results from computerized texture analysis on digital mammograms demonstrated that high-risk and low-risk women have different mammographic parenchymal patterns. On this large clinical dataset, we validated our methods for quantitative analyses of mammographic patterns on FFDM, statistically demonstrating again that women at high risk tend to have dense breasts with coarse and low-contrast texture patterns.

Mammographic image segmentation and risk classification based on mammographic parenchymal patterns and geometric moments

Mammographic risk assessment is becoming increasingly important in decision making in screening mammography and computer aided diagnosis systems. Strong evidence shows that characteristic patterns of breast tissue as seen in mammography, referred to as mammographic parenchymal patterns, provide crucial information about breast cancer risk. Quantitative evaluation of the characteristic mixture of breast tissue can be used both for the estimation of mammographic risk assessment, as well as for quantifying the change of the relative proportion of different breast tissue patterns. This paper investigates mammographic image segmentation based on geometric moments, and prior information of the mammographic building blocks as described by Tabár tissue modelling. The segmentation methodology presented here consists of five distinct steps: (1) feature extraction using mammographic patches, (2) deriving local image properties, (3) feature transformation, (4) mammographic building block based model generation by clustering, and (5) model driven segmentation. The Mammographic Image Analysis Society database was used to facilitate the quantitative and qualitative evaluation, with respect to mammographic risk assessment, based on both Tabár and Breast Imaging Reporting And Data System schemes. Classification accuracies of 71% and 79% were achieved in the corresponding low and high risk categories for Tabár and Breast Imaging Reporting And Data System schemes, respectively. Visual assessment indicates that the proposed segmentation approach can produce consistent and realistic segmentation results, with respect to breast anatomy and Tabár tissue modelling. For screening mammography and computer aided diagnosis, the proposed mammographic segmentation approach is useful in aiding radiologists’ estimation of breast cancer risk and treatment planning prior to biopsies.

Do the majority of Malaysian women have dense breasts on mammogram?

Purpose:To determine: (i) the mammographic parenchymal patterns in Malaysian women and whether the breasts are dense on mammogram; (ii) the effect of age on breast density; (iii) the effect of parity on breast density; (iv) the difference in breast parenchymal patterns among the major races of women in Malaysia.Methods:This was a descriptive cross-sectional study of 1,784 patients (981 Malays, 571 Chinese, 214 Indians and 18 others) who had undergone mammography during the 1-year study period. Majority of women (41.7%) were aged between 51 and 60 years and majority (43%) had 3–4 children. The Tabar classification (Pattern I - V) was used to evaluate breast parenchymal patterns on mammogram. Tabar Pattern I was further divided into 3 sub-groups (Pattern IA, IB, and IC). The different patterns were then grouped into dense (IB, IC, IV, V) and not dense (IA, II, III) breasts. The SPSS package was used for statistical analysis.Results:Majority (59%) of Malaysian women had dense breasts (Pattern IB 29%, IC 20%, IV 5%, and V 5%) and 41% did not have dense breasts (Pattern IA 28%, II 6%, and III 7%). Age and parity were inversely related to breast density (p < 0.0001). Chinese women (65.7%) had the highest percentage of dense breasts (p = 0.69, odds ratio = 1.22), followed by the Indians (57.2%) and the Malays (50.5%).Conclusion:Majority of women had dense breasts but Pattern IV, which has been associated with increased risk of breast cancer, was seen in only 5% of the women. The breast density reduced steadily with increasing age and parity. There was no statistically significant difference in breast density in the three main races.

Association of Computerized Mammographic Parenchymal Pattern Measure with Breast Cancer Risk: A Pilot Case-Control Study

To develop a computerized mammographic parenchymal pattern (MPP) measure and investigate its association with breast cancer risk. A pilot case-control study was conducted by collecting mammograms from 382 subjects retrospectively. The study was institutional review board approved and HIPAA compliant. Informed consent was waived. The cases included the contralateral mammograms of cancer patients (n = 136) obtained at least 1 year before diagnosis. The controls included mammograms of healthy subjects (n = 246) who had cancer-free follow-up for at least 5 years. The data set was historically divided into a training set and an independent test set. An MPP measure was designed to analyze the texture patterns of fibroglandular tissue in the retroareolar region. Odds ratios (ORs) were used to assess the association between breast cancer risk and MPP. To test the trend in ORs, we divided the MPP measure into three categories (C1, C2, and C3) on the basis of its values from low to high, with C1 as the baseline. The confounding factors in this study included patient age, body mass index, first-degree relatives with history of breast cancer, number of previous breast biopsies, and percentage density (PD). Among all of the subjects from the training and test data sets, the Pearson product-moment correlation coefficient between MPP and PD was 0.13. With logistic regression to adjust the confounding, the adjusted ORs for C2 and C3 relative to C1 in the test set were 2.82 (P = .041) and 13.89 (P < .001), respectively. The proposed MPP measure demonstrated a strong association with breast cancer risk and has the potential to serve as an independent factor for risk prediction.

Physical activity and mammographic parenchymal patterns among Greek postmenopausal women

ObjectiveTo examine whether physical activity during the last five years is related to later breast mammographic density in postmenopausal Greek women. MethodsWe designed a cross-sectional study in 724 women, of ages 45–67 years. An interview-administered questionnaire was used to obtain information on duration and intensity of recreational physical activity during five years preceding study recruitment. Mammograms were evaluated according to BIRADS classification and BIRADS score was also estimated. Multivariate ordinal logistic regression analysis was used to assess associations between physical activity index and breast density according to the BIRADS classification methods. ResultsWe observed a statistically significant inverse association of mammographic breast density measured by the BIRADS classification method and recreational exercise (OR=−0.10; 95% CI −0.018, −0.001; p=0.022). For one unit increase in physical activity as expressed by the MET-h/week score, the odds of lower versus higher breast density categories are 1.105 greater, given that all of the other variables in the model are held constant. A modifying effect by age at recruitment was evident among participants, with a stronger inverse association between recreational activity and mammographic breast density among older women (OR=−0.036; 95% CI −0.063, −0.009; p=0.009). An inverse association between physical activity and BIRADS score was evident, not reaching statistical significance (OR=0.00; 95% CI −0.009, 0.008; p=0.887). ConclusionsMammographic breast area was lower in postmenopausal women who participated in sports/recreational physical activity compared to inactive controls. Increasing physical activity levels among postmenopausal women might be a reasonable approach to reduce mammographic density. However, until more physical activity and mammographic breast density studies are conducted that confirm our findings, they have to be interpreted with caution, due to the retrospective nature of our data and the possibility of memory bias.

Mammographic parenchymal patterns of 1,666 Thai women :A mammographic screening evaluation

Mammographic parenchymal patterns of 1,666 Thai women :A mammographic screening evaluation

Mammographic density and breast cancer risk in Chinese women.

Abstract Abstract #4081 Introduction: Mammographic density has been widely accepted as an important breast cancer risk factor. Women with high mammographic density are believed to have an elevated risk of breast cancer. However, no such association has been shown in native Chinese women so far. We carried out a matched case-control study to evaluate the relationship between mammographic density and the risk of breast cancer among Chinese women in a province of Northern China.&amp;#x2028; Method: Cases included 220 women histologically confirmed with breast cancer at a cancer hospital in Northern China between 2005 and 2007. Each case was matched to a control subject with the similar age (within two years old) who attended the same hospital in the same year and diagnosed as benign breast disease or normal breast. Mammograms were the films used for diagnosis. Breast density was evaluated according to Wolf parenchyma patterns through digitalized normal Medio-Lateral Oblique (MLO) mammography view without knowledge of diagnosis. Risk factor information was obtained from archival database of patient-provided information.&amp;#x2028; Results: Compared to women with low-risk (N1 and P1) mammographic parenchymal patterns, those with high-risk patterns (P2 and DY) had an increased risk of breast cancer (odds ratio = 1.9; 95% confidence interval: 1.1 - 3.2) after adjustment for body mass index, age at menarche, and number of full-term pregnancies. There was no statistically significant difference in this association between premenopausal and postmenopausal women.&amp;#x2028; Conclusion: These results indicate that high mammographic density is associated with the risk of breast cancer in native Chinese women. A larger scale study with the validated computer-assisted mammographic density measurement method is being carried out to further confirm this association. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 4081.