Craniocaudal Mammograms Research Articles

Computer-extracted global radiomic features can predict the radiologists' first impression about the abnormality of a screening mammogram.

Radiologists can detect the gist of abnormal based on their rapid initial impression on a mammogram (ie, global gist signal [GGS]). This study explores (1) whether global radiomic (ie, computer-extracted) features can predict the GGS; and if so, (ii) what features are the most important drivers of the signals. The GGS of cases in two extreme conditions was considered: when observers detect a very strong gist (high-gist) and when the gist of abnormal was not/poorly perceived (low-gist). Gist signals/scores from 13 observers reading 4191 craniocaudal mammograms were collected. As gist is a noisy signal, the gist scores from all observers were averaged and assigned to each image. The high-gist and low-gist categories contained all images in the fourth and first quartiles, respectively. One hundred thirty handcrafted global radiomic features (GRFs) per mammogram were extracted and utilized to construct eight separate machine learning random forest classifiers (All, Normal, Cancer, Prior-1, Prior-2, Missed, Prior-Visible, and Prior-Invisible) for characterizing high-gist from low-gist images. The models were trained and validated using the 10-fold cross-validation approach. The models' performances were evaluated by the area under receiver operating characteristic curve (AUC). Important features for each model were identified through a scree test. The Prior-Visible model achieved the highest AUC of 0.84 followed by the Prior-Invisible (0.83), Normal (0.82), Prior-1 (0.81), All (0.79), Prior-2 (0.77), Missed (0.75), and Cancer model (0.69). Cluster shade, standard deviation, skewness, kurtosis, and range were identified to be the most important features. Our findings suggest that GRFs can accurately classify high- from low-gist images. Global mammographic radiomic features can accurately predict high- from low-gist images with five features identified to be valuable in describing high-gist images. These are critical in providing better understanding of the mammographic image characteristics that drive the strength of the GGSs which could be exploited to advance breast cancer (BC) screening and risk prediction, enabling early detection and treatment of BC thereby further reducing BC-related deaths.

Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning

Breast density, which is a measure of the relative amount of fibroglandular tissue within the breast area, is one of the most important breast cancer risk factors. Accurate segmentation of fibroglandular tissues and breast area is crucial for computing the breast density. Semiautomatic and fully automatic computer-aided design tools have been developed to estimate the percentage of breast density in mammograms. However, the available approaches are usually limited to specific mammogram views and are inadequate for complete delineation of the pectoral muscle. These tools also perform poorly in cases of data variability and often require an experienced radiologist to adjust the segmentation threshold for fibroglandular tissue within the breast area. This study proposes a new deep learning architecture that automatically estimates the area-based breast percentage density from mammograms using a weight-adaptive multitask learning approach. The proposed approach simultaneously segments the breast and dense tissues and further estimates the breast percentage density. We evaluate the performance of the proposed model in both segmentation and density estimation on an independent evaluation set of 7500 craniocaudal and mediolateral oblique-view mammograms from Kuopio University Hospital, Finland. The proposed multitask segmentation approach outperforms and achieves average relative improvements of 2.88% and 9.78% in terms of F-score compared to the multitask U-net and a fully convolutional neural network, respectively. The estimated breast density values using our approach strongly correlate with radiologists’ assessments with a Pearson’s correlation of r = 0.90 (95% confidence interval [0.89, 0.91]). We conclude that our approach greatly improves the segmentation accuracy of the breast area and dense tissues; thus, it can play a vital role in accurately computing the breast density. Our density estimation model considerably reduces the time and effort needed to estimate density values from mammograms by radiologists and therefore, decreases inter- and intra-reader variability.

Comparison of two-dimensional synthesized mammograms versus original digital mammograms: a quantitative assessment.

This study objectively evaluates the similarity between standard full-field digital mammograms and two-dimensional synthesized digital mammograms (2DSM) in a cohort of women undergoing mammography. Under an institutional review board-approved data collection protocol, we retrospectively analyzed 407 women with digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) examinations performed from September 1, 2014, through February 29, 2016. Both FFDM and 2DSM images were used for the analysis, and 3216 available craniocaudal (CC) and mediolateral oblique (MLO) view mammograms altogether were included in the dataset. We analyzed the mammograms using a fully automated algorithm that computes 152 structural similarity, texture, and mammographic density-based features. We trained and developed two different global mammographic image feature analysis-based breast cancer detection schemes for 2DSM and FFDM images, respectively. The highest structural similarity features were obtained on the coarse Weber Local Descriptor differential excitation texture feature component computed on the CC view images (0.8770) and MLO view images (0.8889). Although the coarse structures are similar, the global mammographic image feature-based cancer detection scheme trained on 2DSM images outperformed the corresponding scheme trained on FFDM images, with area under a receiver operating characteristic curve (AUC) = 0.878 ± 0.034 and 0.756 ± 0.052, respectively. Consequently, further investigation is required to examine whether DBT can replace FFDM as a standalone technique, especially for the development of automated objective-based methods.

Abstract P5-08-05: The interplay between stromal density, epithelial mesenchymal transition and chemoresistance in breast cancer

Abstract Epithelial Mesenchymal Transition (EMT) refers to the transition of cells from a more differentiated epithelial phenotype to a less differentiated mesenchymal phenotype, a process that may be triggered by a range of therapeutic interventions including cytotoxic treatment, and which we have previously linked to poor breast cancer (BrCa) outcome after neoadjuvant chemotherapy (NAC)1. Mammographic breast density (MBD)represents the white radiographic appearance of epithelial and stromal breast tissue on a mammogram. High MBD in patients being treated for BrCa also associates with chemoresistance, correlating with lower pathological complete response rates (pCR)2. Linking these two stimuli, EMT can also be induced by artificial high-density stroma, where it also leads to chemoresistance in vitro3. Here we set out to validate the link between poor outcome after NAC and EMT in a larger validatory patient cohort, and to ascertain the molecular drivers through which EMT is triggered in this setting. Further we look to confirm the association of high MBD with poor chemoresponse in the same cohort, and to assess whether this chemoresistance is mediated through EMT with the same drivers. In a pilot cohort of 50 NAC-treated locally advanced BrCas with a pCR rate of 20%, pre-NAC biopsies and post-NAC surgical specimens were analysed for expression changes in a panel of EMT-related markers across treatment using 230 Nanostring assays. This included the EMT-driving transcription factors TWIST 1 and 2, SNAIL 1, 2 and 3 and ZEB 1 and 2, which were correlated with risk of relapse. Snail-3 showed significantly greater induction in relapsers compared to non-relapsers (OR=1.8, p=0.04) with a borderline significantly greater induction of TWIST-1 (OR=2.4, p=0.08) in relapsers in addition. In a subsequent 240-patient validation cohort with a pCR rate of 18%, contralateral cranio-caudal view mammograms from the time of diagnosis have been collated and digitized with MBD assessment employing Cumulus software ongoing. Percent breast density will be assessed both as a continuous variable and by quartiles. Immunohistochemistry on pre- and post-operative tissue sections with pan-cytokeratin-vimentin co-staining to identify EMT and staining for SNAIL-3 and TWIST-1 is also in progress. Associations between MBD, EMT before and after chemotherapy, pCR and relapse-free survival will be presented. The role of Snail-3 and TWIST-1 in the interplay between MBD, EMT and outcome is being explored and will be reported. Citation Format: Agarwal V, Spalding LJ, Blick T, Dobrovic A, Thompson EW, Redfern A. The interplay between stromal density, epithelial mesenchymal transition and chemoresistance in breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-08-05.

Triple-negative and non-triple-negative breast cancer prediction by mammographic radiomics features

Objective To develop and validate a radiomics predictive model based on mammogram for preoperative predicting triple-negative breast cancer (TNBC) or non-triple-negative breast cancer (NTNBC). Methods We retrospectively analyzed 459 Chinese women who were diagnosed with invasive breast cancer (confirmed by pathology) during August 2015 to November 2015. Our cohort included 34 TNBC and random selected 102 NTNBC cases. Regions of interest (ROIs) were manually selected from craniocaudal and mediolateral oblique mammograms by radiologists through manual lesion segmentation, and 43 radiomics features were evaluated. Craniocaudal (CC) single-view, mediolateral oblique (MLO) single-view and CC and MLO double-view classification model were constructed respectively. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Kruskal-Walls U test and t test were used to compare the radiomics features between TNBC and UTNBC. Results The model that used the combination of both the CC and MLO view images achieved the overall best performance than using either of the two views alone, yielding an AUC of 0.791, accuracy of 0.798, sensitivity of 0.776 and specificity of 0.806 for TNBC comparing with NTNBC. Three features were selected by the model (gray scale span and inverse different moment for CC, roundness for MLO) showed a statistical significance (P 0.6 in the subtype classification. Conclusion This research constructed model based on mammograms classification model can effectively distinguish between TNBC and NTNBC. This model has potential value for breast cancer molecular subtype classification and clinical treatment. Key words: Breast neoplasms; Triple-negative breast cancer; Molecular subtypes; Radiomics; Machine learning; Mammogram

Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset

We propose a novel convolutional neural network derived pixel-wise breast cancer risk model using mammographic dataset. An institutional review board approved retrospective case-control study of 1474 mammographic images was performed in average risk women. First, 210 patients with new incidence of breast cancer were identified. Mammograms from these patients prior to developing breast cancer were identified and made up the case group [420 bilateral craniocaudal mammograms]. The control group consisted of 527 patients without breast cancer from the same time period. Prior mammograms from these patients made up the control group [1054 bilateral craniocaudal mammograms]. A convolutional neural network (CNN) architecture was designed for pixel-wise breast cancer risk prediction. Briefly, each mammogram was normalized as a map of z-scores and resized to an input image size of 256 × 256. Then a contracting and expanding fully convolutional CNN architecture was composed entirely of 3 × 3 convolutions, a total of four strided convolutions instead of pooling layers, and symmetric residual connections. L2 regularization and augmentation methods were implemented to prevent overfitting. Cases were separated into training (80%) and test sets (20%). A 5-fold cross validation was performed. Software code was written in Python using the TensorFlow module on a Linux workstation with NVIDIA GTX 1070 Pascal GPU. The average age of patients between the case and the control groups was not statistically different [case: 57.4years (SD, 10.4) and control: 58.2years (SD, 10.9), p = 0.33]. Breast Density (BD) was significantly higher in the case group [2.39 (SD, 0.7)] than the control group [1.98 (SD, 0.75), p < 0.0001]. On multivariate logistic regression analysis, both CNN pixel-wise mammographic risk model and BD were significant independent predictors of breast cancer risk (p < 0.0001). The CNN risk model showed greater predictive potential [OR = 4.42 (95% CI, 3.4-5.7] compared to BD [OR = 1.67 (95% CI, 1.4-1.9). The CNN risk model achieved an overall accuracy of 72% (95%CI, 69.8-74.4) in predicting patients in the case group. Novel pixel-wise mammographic breast evaluation using a CNN architecture can stratify breast cancer risk, independent of the BD. Larger dataset will likely improve our model.

Abstract 3260: Utilizing digital pathology to understand breast epithelial characteristics of benign breast disease among women undergoing diagnostic image-guided breast biopsy

Abstract Introduction: Defining the histologic correlates of mammographic breast density (MD) may provide insights into why elevated MD is related to increased breast cancer risk. Studies suggest that reduced terminal ductal lobular (TDLU) involution is associated with elevated MD, and both are independent breast cancer risk factors among women who have undergone a biopsy for benign breast disease (BBD). Prior digital histologic analyses of normal breast tissues revealed epithelial nuclear density (END) and TDLU involution are correlated. Accordingly, we examined associations of END with TDLU involution and MD in clinical biopsies. Methods: We analyzed 262 image-guided breast biopsies diagnosed as BBD from 224 women. TDLU involution was visually assessed as TDLU count/mm2 and TDLU span (inversely related to level of involution) in background normal tissue, evaluated using digitized images. The Genie Classifier (Aperio) was applied to images to estimate nuclei count per unit epithelial area, (END). Single X-ray Absorptiometry of pre-biopsy craniocaudal digital mammograms was applied to measure global MD (percent fibroglandular volume (%FGV)). Analysis of covariance, adjusted for age and body mass index, examined mean END differences across tertiles of TDLU/MD measures. Analyses were conducted at the biopsy level using SAS PROC GENMOD to account for within-woman correlations. All tests were two-tailed. Results: Overall, 67% of BBD biopsies were proliferative. Higher END was observed among proliferative than non-proliferative BBD (median END: 10,187 vs. 9,953 respectively; p=0.04). Among all women, END significantly increased with increasing tertiles of TDLU measures (p-trends: TDLU count/100mm2=0.0001, TDLU span=0.046). Whereas TDLU metrics were positively associated with %FGV, no relationship was observed between END and %FGV. In analyses stratified by BBD severity, however, END and %FGV were positively associated among women with non-proliferative disease (p-trend=0.04), findings not observed with proliferative disease. Conclusions: Automated END and visually assessed TDLU involution metrics were positively associated with each other and with MD. However, associations were diluted for proliferative lesions, suggesting that applying automated digital pathology tools to unsegmented digital images of whole sections of BBD biopsies does not demonstrate the same associations with MD as visual assessment of TDLU involution. Citation Format: Maeve Mullooly, Samantha Puvanesarajah, Shaoqi Fan, Ruth M. Pfeiffer, Linnea Olsson, Manila Hada, Erin L. Kirk, Pamela M. Vacek, Donald L. Weaver, John A. Shepherd, Amir P. Mahmoudzadeh, Jeff Wang, Stephen M. Hewitt, Sally D. Herschorn, Mark E. Sherman, Melissa A. Troester, Gretchen L. Gierach. Utilizing digital pathology to understand breast epithelial characteristics of benign breast disease among women undergoing diagnostic image-guided breast biopsy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3260.

Occupation and mammographic density: A population-based study (DDM-Occup)

IntroductionHigh mammographic density is one of the main risk factors for breast cancer. Although several occupations have been associated with breast cancer, there are no previous occupational studies exploring the association with mammographic density. Our objective was to identify occupations associated with high mammographic density in Spanish female workers. MethodsWe conducted a population-based cross-sectional study of occupational determinants of high mammographic density in Spain, based on 1476 women, aged 45–68 years, recruited from seven screening centers within the Spanish Breast Cancer Screening Program network. Reproductive, family, personal, and occupational history data were collected. The latest occupation of each woman was collected and coded according to the 1994 National Classification of Occupations. Mammographic density was assessed from the cranio-caudal mammogram of the left breast using a semi-automated computer-assisted tool. Association between mammographic density and occupation was evaluated by using mixed linear regression models, using log-transformed percentage of mammographic density as dependent variable. Models were adjusted for age, body mass index, menopausal status, parity, smoking, alcohol intake, educational level, type of mammography, first-degree relative with breast cancer, and hormonal replacement therapy use. Screening center and professional reader were included as random effects terms. ResultsMammographic density was higher, although non-statistically significant, among secondary school teachers (eβ = 1.41; 95%CI = 0.98–2.03) and nurses (eβ = 1.23; 95%CI = 0.96–1.59), whereas workers engaged in the care of people (eβ = 0.81; 95%CI = 0.66–1.00) and housewives (eβ = 0.87; 95%CI = 0.79–0.95) showed an inverse association with mammographic density. A positive trend for every 5 years working as secondary school teachers was also detected (p-value = 0.035). ConclusionsNurses and secondary school teachers were the occupations with the highest mammographic density in our study, showing the latter a positive trend with duration of employment. Future studies are necessary to confirm if these results are due to chance or are the result of a true association whose causal hypothesis is, for the moment, unknown.

An Anthropometric-Based Subject-Specific Finite Element Model of the Human Breast for Predicting Large Deformations.

The large deformation of the human breast threatens proper nodules tracking when the subject mammograms are used as pre-planning data for biopsy. However, techniques capable of accurately supporting the surgeons during biopsy are missing. Finite element (FE) models are at the basis of currently investigated methodologies to track nodules displacement. Nonetheless, the impact of breast material modeling on the mechanical response of its tissues (e.g., tumors) is not clear. This study proposes a subject-specific FE model of the breast, obtained by anthropometric measurements, to predict breast large deformation. A healthy breast subject-specific FE parametric model was developed and validated by Cranio-caudal (CC) and Medio-Lateral Oblique (MLO) mammograms. The model was successively modified, including nodules, and utilized to investigate the effect of nodules size, typology, and material modeling on nodules shift under the effect of CC, MLO, and gravity loads. Results show that a Mooney–Rivlin material model can estimate healthy breast large deformation. For a pathological breast, under CC compression, the nodules displacement is very close to zero when a linear elastic material model is used. Finally, when nodules are modeled, including tumor material properties, under CC, or MLO or gravity loads, nodules shift shows ~15% average relative difference.

Feasibility study of a breast density measurement within a direct photon-counting mammography scanner system.

To evaluate the clinical feasibility of breast density measurements by a new application within a direct photon-counting mammography scanner system. A retrospective study of consecutive women who underwent mammography using a direct photon-counting mammography scanner system (MicroDose mammography SI; Philips Digital Mammography Sweden AB) was performed at the authors' institution between September and December 2013. Quantitative volumetric glandularity measurements were performed automatically for each acquired mammographic image using an application (Breast Density Measurement; Philips Digital Mammography Sweden AB). The quantitative volumetric glandularity of each breast was defined as the average values for the mediolateral oblique (MLO) and craniocaudal (CC) mammogram views. Of the 44 women who underwent bilateral mammogram acquisitions, the breast density measurements were performed successfully in 40 patients (90.9%). A very good to excellent correlation in the quantitative breast density measurements acquired from the MLO and CC images was obtained in the 40 evaluable patients (R = 0.99). The calculated volumetric glandularity using this new application should correspond well with the true volumetric density of each breast.

Lobular intraepithelial neoplasia arising within breast fibroadenoma

BackgroundFibroadenomas are the second most common breast pathology occurring in young women under the age of 35 years old. Fibroadenomas can be classified as simple or complex according to histological features. Complex fibroadenomas differ from simple fibroadenomas because of the presence of cysts (3 mm), sclerosing adenosis, epithelial calcifications, or papillary apocrine changes. Most fibroadenomas are clinically identifiable. In 25% of cases, fibroadenomas are non-palpable and are diagnosed with mammography and ultrasound. Differential diagnosis with well differentiated breast cancer is often necessary, particularly with medullary or mucinous tumors. Calcification findings within fibroadenomas by mammogram have to be investigated. The age of a lump is usually reflected by calcifications. Microcalcification can hide foci of carcinoma in situ when they are small, branching type, and heterogeneous. However, many morphological possibilities may not be reliable for deciding whether a certain calcification is the product of a malignant or a benign process. From a radiological point of view, fibroadenomas containing foci of carcinoma in situ can be indistinguishable from benign lesions, even if the incidence of carcinoma within fibroadenomas is estimated as 0.1–0.3%, and it could be a long-term risk factor for invasive breast cancer.Case presentationA 44-year-old woman presented with a 1.5-cm palpable, smooth, mobile lump in the lower-inner quadrant of her right breast. Standard mediolateral oblique and craniocaudal mammograms showed a cluster of eccentric popcorn-like calcifications within the fibroadenoma. After lumpectomy, a definitive histological examination confirmed the intra-operative diagnosis of a benign mass. However, lobular intraepithelial neoplasia foci were found, surrounded by atypical lobular hyperplasia.ConclusionsThe possibility of an old benign breast lump might be supported by fine needle aspiration biopsy or core biopsy before initiating follow-up. According to our experience, when patients are older than 40 years and have a familial history of breast cancer, we prefer to carry out lumpectomy with follow up to avoid the risk of underestimation in situ foci within the lump.

THE MUSCULUS STERNALIS: ULTRASONOGRAPHIC VERIFICATION OF A RARE BUT BENIGN MAMMOGRAPHIC FINDING

Screening mammography requires high-quality images and experienced radiologists. An improved technique with a systematic approach to patient positioning enables visualization of the deep parts of the breast. The sternalis muscle is occasionally observed in mammograms as a variant of the deep normal structures and may mimic a mass. The sternalis muscle is an uncommon anatomic variant of the chest wall musculature. It is located subcutaneously over the pectoralis muscle and has a parallel course to the sternum. It runs from the infraclavicular area to the caudal aspect of the breast. This inconsistent muscle has varying shapes, contours and sizes and may mimic a mass lesion on the craniocaudal (CC) mammogram. Familiarity with its mammographic appearance and ultrasonographic correlation is essential to avoid confusion with a malignant lesion. In this case report, we describe mammographic and ultrasonographic imaging findings of the sternalis muscle and review the literature.

Hematoma Formation During Breast Core Needle Biopsy in Women Taking Antithrombotic Therapy

The purpose of this study was to compare hematoma formation after breast core needle biopsy performed on patients undergoing and those not undergoing concurrent antithrombotic therapy. A prospective assessment of core needle biopsies (stereotactic, ultrasound guided, or MRI guided) performed on patients enrolled between September 2011 and July 2012 formed the basis of this study. Postprocedure mediolateral and craniocaudal mammograms were evaluated for the presence and size of hematomas. Patients were clinically evaluated for complications 24-48 hours after the procedure through telephone call or face-to-face consultation. Needle size, type of biopsy, and presence of hematoma and documented complications were correlated with use of antithrombotic agents (including aspirin, warfarin, clopidogrel, and daily nonsteroidal antiinflammatory medications). No clinically significant hematomas or bleeding complications were found. Eighty-nine of 617 (14.4%) non-clinically significant hematomas were detected on postprocedure mammograms. The probability of development of a non-clinically significant hematoma was 21.6% for patients taking antithrombotics and 13.0% for those not taking antithrombotics. Concurrent antithrombotic therapy and larger needle gauge were significant factors contributing to the probability of hematoma formation. The volume of the hematoma was not related to needle gauge or presence of antithrombotic therapy. No clinically significant hematomas were found. Because there are potential life-threatening risks to stopping antithrombotic therapy before breast biopsy, withholding antithrombotic therapy for core needle breast biopsy is not recommended because the incidence of non-clinically significant hematoma is low.

Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms

Image processing algorithms can be used in computer-aided diagnosis systems to extract features directly from digitized mammograms. Typically, two classes of features are extracted from mammograms with these algorithms, namely morphological and non-morphological features. Image texture analysis is an important technique that represents gray level properties of images used to describe non-morphological features. This technique has shown to be a promising technique in analyzing mammographic lesions caused by masses. In this paper, we evaluate texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images. In particular, we propose a false positive reduction in computer-aided detection of masses. The data set consisted of 120 cranio-caudal mammograms, half containing a mass, rated as abnormal images, and half with no lesions. The following texture descriptors were then calculated to analyze the regions of interest (ROIs) texture patterns: entropy, energy, sum average, sum variance, and cluster tendency. To select the best set of features for each method, we applied a genetic algorithm (GA). In the ROIs classification stage, we used the Random Forest algorithm, a data mining technique that separates the data into non-overlapping segments. Experimental results showed that the best classification rates were obtained with the wavelet-based feature extraction using GA for selection of the most relevant features, giving an AUC=0.90.

Women’s features and inter-/intra-rater agreement on mammographic density assessment in full-field digital mammograms (DDM-SPAIN)

Measurement of mammographic density (MD), one of the leading risk factors for breast cancer, still relies on subjective assessment. However, the consistency of MD measurement in full-digital mammograms has yet to be evaluated. We studied inter- and intra-rater agreement with respect to estimation of breast density in full-digital mammograms, and tested whether any of the women's characteristics might have some influence on them. After an initial training period, three experienced radiologists estimated MD using Boyd scale in a left breast cranio-caudal mammogram of 1,431 women, recruited at three Spanish screening centres. A subgroup of 50 randomly selected images was read twice to estimate short-term intra-rater agreement. In addition, a reading of 1,428 of the images, performed 2 years before by one rater, was used to estimate long-term intra-rater agreement. Pair-wise weighted kappas with 95% bootstrap confidence intervals were calculated. Dichotomous variables were defined to identify mammograms in which any rater disagreed with other raters or with his/her own assessment, respectively. The association between disagreement and women's characteristics was tested using multivariate mixed logistic models, including centre as a random-effects term, and taking into account repeated measures when required. All quadratic-weighted kappa values for inter- and intra-rater agreement were excellent (higher than 0.80). None of the studied women's features, i.e. body mass index, brassiere size, menopause, nulliparity, lactation or current hormonal therapy, was associated with higher risk of inter- or intra-rater disagreement. However, raters differed significantly more in images that were classified in the higher-density MD categories, and disagreement in intra-rater assessment was also lower in low-density mammograms. The reliability of MD assessment in full-field digital mammograms is comparable to that for original or digitised images. The reassuring lack of association between subjects' MD-related characteristics and agreement suggests that bias from this source is unlikely.

Childhood factors associated with mammographic density in adult women

Growth and development factors could contribute to the development of breast cancer associated with an increase in mammographic density. This study examines the influence of certain childhood-related, socio-demographic and anthropometric variables on mammographic density in adult woman. The study covered 3574 women aged 45-68 years, participating in breast cancer-screening programmes in seven Spanish cities. Based on a craniocaudal mammogram, blind, anonymous measurement of mammographic density was made by a single radiologist, using Boyd's semiquantitative scale. Data associated with the early stages of life were obtained from a direct survey. Ordinal logistic regression and generalised linear models were employed to estimate the association between mammographic density and the variables covered by the questionnaire. Screening programme was introduced as a random effects term. Age, number of children, body mass index (BMI) and other childhood-related variables were used as adjustment variables, and stratified by menopausal status. A total of 811 women (23%) presented mammographic density of over 50%, and 5% of densities exceeded 75%. Our results show a greater prevalence of high mammographic density in women with low prepubertal weight (OR: 1.18; 95% CI: 1.02-1.36); marked prepubertal height (OR: 1.25; 95% CI: 0.97-1.60) and advanced age of their mothers at their birth (>39 years: OR: 1.28; 95% CI: 1.03-1.60); and a lower prevalence of high mammographic density in women with higher prepubertal weight, low birth weight and earlier menarche. The influence of these early-life factors may be explained by greater exposure to hormones and growth factors during the development of the breast gland, when breast tissue would be particularly susceptible to proliferative and carcinogenic stimulus.

Reproductive and menstrual factors and mammographic density in African American, Caribbean, and white women

We investigated the associations between reproductive and menstrual risk factors for breast cancer and mammographic density, a strong risk factor for breast cancer, in a predominantly ethnic minority and immigrant sample. We interviewed women (42% African American, 22% African Caribbean, 22% White, 9% Hispanic Caribbean, 5% other) without a history of breast cancer during their mammography appointment (n = 191, mean age = 50). We used a computer-assisted method to measure the area and percentage of dense breast tissue from cranio-caudal mammograms. We used multivariable linear regression analyses to estimate the associations between reproductive and menstrual risk factors and mammographic density. Age was inversely associated with percent density and dense area, and body mass index (BMI) was inversely associated with percent density. Adjusting for age, BMI, ethnicity and menopausal status, later age at menarche (e.g., β = -7.37, 95% CI: -12.29, -2.46 for age ≥ 13 years vs. ≤ 11 years), and any use of hormonal birth control (HBC) methods (β = -5.10, 95% CI: -9.37, -0.84) were associated with reduced dense area. Ethnicity and nativity (foreign- vs. US-born) were not directly associated with density despite variations in the distribution of several risk factors across ethnic and nativity groups. The mean level of mammographic density did not differ across ethnic and nativity groups, but several risk factors for breast cancer were associated with density in ethnic minority and immigrant women.

Die diagnostische Bildqualität von Mammografien in der vertragsärztlichen Versorgung Deutschlands

PURPOSE: A total of 79 115 mammograms from statutory health insurance (SHI) physicians within German outpatient care were evaluated with respect to the diagnostic image quality. MATERIALS AND METHODS: Mammograms were randomly selected between 2006 and 2008 by the regional Associations of Statutory Health Insurance Physicians and submitted to regional boards of experts for external evaluation. The mammogram quality was evaluated using a 3-point scale (adequate, borderline, failure) and documented using a nationally standardized protocol. RESULTS: 87.6 % of the mammograms were classified as adequate, 11.0 % as borderline and 1.4 % as failure. Mediolateral oblique mammograms (mlo) had worse ratings than craniocaudal mammograms (cc). Main reasons for classifying the mammograms as borderline or failure were “inframammary fold not adequately visualized” (mlo), “pectoral muscle not in the correct angle or not to the level with the nipple” (mlo), “the nipple not in profile” (mlo, cc) and “breast not completely or not adequately visualized” (cc). CONCLUSION: The results show a good overall quality of mammograms in German outpatient medical care. Failures can be associated predominantly with incorrect positioning of the breast. More precisely defined quality criteria using objective measures are recommended, especially for craniocaudal mammograms (cc).

Die diagnostische Bildqualität von Mammografien in der vertragsärztlichen Versorgung Deutschlands

A total of 79115 mammograms from statutory health insurance (SHI) physicians within German outpatient care were evaluated with respect to the diagnostic image quality. Mammograms were randomly selected between 2006 and 2008 by the regional Associations of Statutory Health Insurance Physicians and submitted to regional boards of experts for external evaluation. The mammogram quality was evaluated using a 3-point scale (adequate, borderline, failure) and documented using a nationally standardized protocol. 87.6% of the mammograms were classified as adequate, 11.0% as borderline and 1.4% as failure. Mediolateral oblique mammograms (mlo) had worse ratings than craniocaudal mammograms (cc). Main reasons for classifying the mammograms as borderline or failure were "inframammary fold not adequately visualized" (mlo), "pectoral muscle not in the correct angle or not to the level with the nipple" (mlo), "the nipple not in profile" (mlo, cc) and "breast not completely or not adequately visualized" (cc). The results show a good overall quality of mammograms in German outpatient medical care. Failures can be associated predominantly with incorrect positioning of the breast. More precisely defined quality criteria using objective measures are recommended, especially for craniocaudal mammograms (cc).

Predicting breast cancer risk using mammographic density measurements from both mammogram sides and views

Mammographic density is a strong risk factor for breast cancer. Which and how many x-rays are used for research, and how mammographic density is measured varies across studies. In this article, we compared three different measurements (absolute dense area, percent dense area and percent dense volume) from each of four mammograms [left, right, medio-lateral oblique (MLO) and cranio-caudal (CC) views] using three different methods of measurement [computer-assisted thresholding, visual assessment and standard mammogram form (SMF)] to investigate whether additional measurements and/or different methods of measurement provide more information in the prediction of breast cancer risk. Mammographic density was measured in all four mammograms from 318 cases and 899 age-matched controls combined from the Cambridge and Norwich Breast Screening Programmes. Measurements were averaged across various combinations of mammogram type and/or view. Conditional logistic regression was used to estimate odds ratios associated with increasing quintiles of each mammographic measure. Overall, there appeared to be no difference in the fit of the models using two or four mammograms compared to the models using just the contralateral MLO or CC mammogram (all P > 0.07) for all methods of measurement. Common practice of measuring just the contralateral MLO or CC mammogram for analysis in case-control studies investigating the association between mammographic density and breast cancer risk appears to be sufficient.