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Background: Tumors in radiologically dense breast were overlooked on mammograms more often than tumors in low-density breasts. A fast reproducible and automated method of assessing percentage mammographic density (PMD) would be desirable to support decisions whether ultrasonography should be provided for women in addition to mammography in diagnostic mammography units. PMD assessment has still not been included in clinical routine work, as there are issues of interobserver variability and the procedure is quite time consuming. This study investigated whether fully automatically generated texture features of mammograms can replace time-consuming semi-automatic PMD assessment to predict a patient's risk of having an invasive breast tumor that is visible on ultrasound but masked on mammography (mammography failure)., Methods: This observational study included 1334 women with invasive breast cancer treated at a hospital-based diagnostic mammography unit. Ultrasound was available for the entire cohort as part of routine diagnosis. Computer-based threshold PMD assessments ("observed PMD") were carried out and 363 texture features were obtained from each mammogram. Several variable selection and regression techniques (univariate selection, lasso, boosting, random forest) were applied to predict PMD from the texture features. The predicted PMD values were each used as new predictor for masking in logistic regression models together with clinical predictors. These four logistic regression models with predicted PMD were compared among themselves and with a logistic regression model with observed PMD. The most accurate masking prediction was determined by cross-validation., Results: About 120 of the 363 texture features were selected for predicting PMD. Density predictions with boosting were the best substitute for observed PMD to predict masking. Overall, the corresponding logistic regression model performed better (cross-validated AUC, 0.747) than one without mammographic density (0.734), but less well than the one with the observed PMD (0.753). However, in patients with an assigned mammography failure risk >10%, covering about half of all masked tumors, the boosting-based model performed at least as accurately as the original PMD model., Conclusion: Automatically generated texture features can replace semi-automatically determined PMD in a prediction model for mammography failure, such that more than 50% of masked tumors could be discovered.

Although mammography screening programs do not include ultrasound examinations, some diagnostic units do provide women with both mammography and ultrasonography. This article is concerned with estimating the risk of a breast cancer patient diagnosed in a hospital-based mammography unit having a tumor that is visible on ultrasound but not on mammography. A total of 1,399 women with invasive breast cancer from a hospital-based diagnostic mammography unit were included in this retrospective study. For inclusion, mammograms from the time of the primary diagnosis had to be available for computer-assisted assessment of percentage mammographic density (PMD), as well as Breast Imaging Reporting and Data System (BIRADS) assessment of mammography. In addition, ultrasound findings were available for the complete cohort as part of routine diagnostic procedures, regardless of any patient or imaging characteristics. Logistic regression analyses were conducted to identify predictors of mammography failure, defined as BIRADS assessment 1 or 2. The probability that the visibility of a tumor might be masked at diagnosis was estimated using a regression model with the identified predictors. Tumors were only visible on ultrasound in 107 cases (7.6%). PMD was the strongest predictor for mammography failure, but age, body mass index and previous breast surgery also influenced the risk, independently of the PMD. Risk probabilities ranged from 1% for a defined low-risk group up to 40% for a high-risk group. These findings might help identify women who should be offered ultrasound examinations in addition to mammography., (© 2016 UICC.)

There is growing evidence that certain breast cancer (BC) risk factors specifically increase the risk for specific molecular tumor subtypes. Different molecular subtypes of BC can partly be described by analyzing proliferation in tumors. Very few data are available regarding the association of mammographic density (MD), as a BC risk factor, with proliferation. The aim of this study was to analyze the association between Ki-67 expression in BCs and MD. In this case-only study, data on BC risk factors, hormone receptor expression, and MD were available for 1,975 patients with incident BC. MD was assessed as percentage mammographic density (PMD) using a semiautomated method by two readers for every patient. The association of the Ki-67 proliferation index and PMD was studied using multifactorial analyses of covariance (ANCOVA), with PMD as the target variable and including well-known factors that are also associated with MD such as age, parity, use of hormone replacement therapy (HRT), and body mass index (BMI). There were no significant differences in PMD between women with BC who had low and high Ki-67 values (P = 0.31). However, there were relevant differences in women with low BMI (P = 0.07), and in women using postmenopausal HRT (P = 0.06) as well as in women with low PR values (P = 0.07). In these subgroups, the Ki-67 expression index increased with decreasing PMD. Likewise PMD is correlated with BMI, parity status, and menopausal status stronger in patients with low proliferating tumors, and with progesterone receptor expression in patients with high proliferating tumors. MD correlates inversely with Ki-67 proliferation in BC tumors only in some subgroups of BC patients, defined by commonly known BC risk factors that are usually associated with MD as well.

Introduction: Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design., Methods: A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model., Results: Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model., Conclusions: Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy.

Background: Re-operations after breast conserving surgery (BCS) are necessary, when specimen margins are not free of breast cancer cells. This study explored the accuracy of preoperative tumour size assessment and its influence on the rate of re-excisions and mastectomies., Methods: The study included 1591 patients with invasive breast cancer, who were planned for BCS. Patient, staging and tumor characteristics were evaluated concerning their influence on re-excision and mastectomy rates. Patient and tumor characteristics comprised histopathological tumour size, HER2 status, multifocality, in situ component, grading (G), nodal status and hormone receptor (HR) status. Staging characteristics included deviation from pathological tumour size as measured by clinical examination, sonography and mammography., Results: In 1316 patients (83%) sufficient treatment was possible with one operation. 275 patients (17%) had to undergo at least one further surgery as a result of positive specimen margins. In 138 patients (9%) mastectomy was ultimately necessary. In patients with a positive HER2 status, a larger tumour size, underestimation by ultrasound, an in situ component and multifocality, the risk for a re-operation was about doubled. Tumour size deviation in the mammogram or the clinical tumour size assessment did not have significant influence to the re-excision rates., Conclusion: Tumour size and accurate presurgical assessment of the tumour size itself are independent predictors for the need of a second surgery or even a mastectomy in patients for whom a primary BCS was planned., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Objectives: Tumour size estimates using mammography (MG), conventional ultrasound (US), compound imaging (CI) and real-time elastography (RTE) were compared with histopathological specimen sizes., Methods: The largest diameters of 97 malignant breast lesions were measured. Two US and CI measurements were made: US1/CI1 (hypoechoic nucleus only) and US2/CI2 (hypoechoic nucleus plus hyperechoic halo). Measurements were compared with histopathological tumour sizes using linear regression and Bland-Altman plots., Results: Size prediction was best with ultrasound (US/CI/RTE: R (2) 0.31-0.36); mammography was poorer (R(2) = 0.19). The most accurate method was US2, while US1 and CI1 were poorest. Bland-Altman plots showed better size estimation with US2, CI2 and RTE, with low variation, while mammography showed greatest variability. Smaller tumours were better assessed than larger ones. CI2 and US2 performed best for ductal tumours and RTE for lobular cancers. Tumour size prediction accuracy did not correlate significantly with breast density, but on MG tumours were more difficult to detect in high-density tissue., Conclusions: The size of ductal tumours is best predicted with US2 and CI2, while for lobular cancers RTE is best. Hyperechoic tumour surroundings should be included in US and CI measurements and RTE used as an additional technique in the clinical staging process.

Mammographic percent density (MD) is recognized as one of the strongest risk factors associated with breast cancer. This matched case-control study investigated whether MD represents an independent risk factor. Mammograms were obtained from 1025 breast cancer patients and from 520 healthy controls. MD was measured using a quantitative computer-based threshold method (0-100%). Breast cancer patients had a higher MD than healthy controls (38 vs. 32%, P<0.01). MD was significantly higher in association with factors such as age over 60 years, body mass index (BMI) of 25-30 kg/m², nulliparity or low parity (one to two births). Average MD was inversely associated with age, BMI, parity and positively associated with age at first full-term pregnancy. MD was higher in women with at least one first-degree relative affected, but only among patients and not in the group of healthy controls (P<0.01/P=0.61). In women with an MD of 25% or more, the risk of breast cancer was doubled compared with women with an MD of less than 10% (odds ratio: 2.1; 95% confidence interval: 1.3-3.4; P<0.01); in the postmenopausal subgroup, the risk was nearly tripled (odds ratio: 2.7; 95% confidence interval: 1.6-4.7; P<0.001). This study provides further evidence that MD is an important risk factor for breast cancer. These results indicate strong associations between MD and the risk of breast cancer in a matched case-control study in Germany.

Mammographic density (MD) has consistently been found as one of the strongest breast cancer risk factors. In our study, both qualitative and quantitative density measurements were performed in a hospital-based group of premenopausal women before and after first full-term pregnancy providing an opportunity for direct evaluation of the effects of one pregnancy on MD. Mammograms were obtained from 23 women before and after first full-term pregnancy and from 28 nulliparous controls. MD was determined by a standard qualitative assessment method using the Breast Imaging Reporting and Data System, and a quantitative computer-based threshold method (0-100%). The mean age at mammography before and after pregnancy was 31 and 34 years, respectively, with a mean difference of 40 months between mammographies. The quantitative density assessment showed a significant reduction in relative MD after pregnancy of 12 percentage points (8.6-15.4), compared with 3.1 (0.0-6.2) in the nulliparous control group (P<0.001). A reduction in MD of more than 10% was seen in 52% of the patients, compared with 18% of the controls. The qualitative density assessment confirmed a reduction in MD after pregnancy by one Breast Imaging Reporting and Data System category (P=0.02). This longitudinal study showed that MD can be influenced by one full-term pregnancy. This effect was seen with both quantitative and qualitative assessment methods. It may be hypothesized that breast cancer risk reduction associated with pregnancy is mediated through a direct reduction of MD, and MD assessment might be incorporated in individualizing risk assessment and prevention.

It has been shown in several studies that antihormonal compounds can offer effective prophylactic treatment to prevent breast cancer. In view of the low participation rates in chemoprevention trials, the purpose of this study was to identify the characteristics of women taking part in a population-based mammography screening program who wished to obtain information about the risk of breast cancer and then participate in the the International Breast Cancer Intervention Study II (IBIS-II) trial, a randomized double-blind controlled chemoprevention trial comparing anastrozole with placebo. A paper-based survey was conducted in a population-based mammography screening program in Germany between 2007 and 2009. All women who met the criteria for the mammography screening program were invited to complete a questionnaire. A total of 2,524 women completed the questionnaire, and 17.7% (n = 446) met the eligibility criteria for the IBIS-II trial after risk assessment. The women who wished to receive further information about chemoprevention were significantly younger (P < 0.01) and had significantly more children (P = 0.03) and significantly more relatives with breast cancer (P < 0.001). There were no significant differences between the participants with regard to body mass index or hormone replacement therapy. Normal mammographic findings at screening were the main reason (42%) for declining to participate in the IBIS-II trial or attend risk counseling. The ultimate rate of recruitment to the IBIS-II trial was very low (three women). Offering chemoprevention to women within a mammography screening unit as part of a paper-based survey resulted in low participation rates for both, the survey and the final participation in the IBIS-II trial. More individualized approaches and communication of breast cancer risk at the time of the risk assessment might be helpful to increase the participation and the understanding of chemopreventive approaches.