Search

Abstract Background High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments. Methods We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON® 730 Expert system (GE Medical Systems Kretztechnik GmbH & Co OHG, Austria). Gray level bins were calculated from ultrasound images to characterize mammographic density. Percentage mammographic density (PMD) was predicted by gray level bins using various regression models. Results Gray level bins and PMD correlated to a certain extent. Spearman’s ρ ranged from − 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R 2, 0.255). Overall, ultrasound images from the VOLUSON® 730 Expert device in this study showed limited predictive power for PMD when correlated with the corresponding mammograms. Conclusions In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).

Abstract Background Growing demand for risk-reducing surgery in individuals with inherited susceptibility to cancer leads to the question whether these procedures are cost effective for the executing hospitals. This study compared the clinical costs for bilateral risk-reducing mastectomy (BRRM) with and without different types of reconstruction, risk-reducing salpingo-oophorectomy (RRSO), and their combinations with corresponding reimbursements in the statutory health-care system in Germany. Patients and methods Real total costs of care for BRRM with and without reconstruction, RRSO, and their combinations were calculated as the sum of all personnel and technical costs. These costs calculated in a German University hospital were compared with the sum of all reimbursements in the German DRG-based health-care system. Results While sole RRSO, BRRM without reconstruction, and BRRM with secondary DIEP (deep inferior epigastric perforator)—reconstruction still result in a small benefit, we even found shortfalls for the hospital with all other prophylactic operations under consideration. The calculated deficits were especially high for BRRM with implant-based breast reconstruction and for combined operations when the risk reduction is achieved with a minimum of separate operations. Conclusions Risk-reducing surgery in BRCA-mutation carriers is frequently not cost-covering for the executing hospitals in the German health-care system. Thus, appropriate concepts are required to ensure a nationwide care.

Abstract 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.