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Objectives: To objectively evaluate automatic volumetric breast density assessment in Full-Field Digital Mammograms (FFDM) using measurements obtained from breast Magnetic Resonance Imaging (MRI). Material and Methods: A commercially available method for volumetric breast density estimation on FFDM is evaluated by comparing volume estimates obtained from 186 FFDM exams including mediolateral oblique (MLO) and cranial-caudal (CC) views to objective reference standard measurements obtained from MRI. Results: Volumetric measurements obtained from FFDM show high correlation with MRI data. Pearson’s correlation coefficients of 0.93, 0.97 and 0.85 were obtained for volumetric breast density, breast volume and fibroglandular tissue volume, respectively. Conclusions: Accurate volumetric breast density assessment is feasible in Full-Field Digital Mammograms and has potential to be used in objective breast cancer risk models and personalized screening The research leading to these results has received funding from the European Union's Seventh Framework Programme FP7 under grant agreement no 306088 and has been supported by the Spanish Science and Innovation grant nb. TIN2012-37171-C02-01. Albert Gubern-Merida holds a FPU grant AP2009-2835. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Contains fulltext : 173131.pdf (Publisher’s version ) (Open Access) PURPOSE: Fibroglandular tissue may mask breast cancers, thereby reducing the sensitivity of mammography. Here, we investigate methods for identification of women at high risk of a masked tumor, who could benefit from additional imaging. METHODS: The last negative screening mammograms of 111 women with interval cancer (IC) within 12 months after the examination and 1110 selected normal screening exams from women without cancer were used. From the mammograms, volumetric breast density maps were computed, which provide the dense tissue thickness for each pixel location. With these maps, three measurements were derived: (1) percent dense volume (PDV), (2) percent area where dense tissue thickness exceeds 1 cm (PDA), and (3) dense tissue masking model (DTMM). Breast density was scored by a breast radiologist using BI-RADS. Women with heterogeneously and extremely dense breasts were considered at high masking risk. For each masking measure, mammograms were divided into a high- and low-risk category such that the same proportion of the controls is at high masking risk as with BI-RADS. RESULTS: Of the women with IC, 66.1, 71.9, 69.2, and 63.0% were categorized to be at high masking risk with PDV, PDA, DTMM, and BI-RADS, respectively, against 38.5% of the controls. The proportion of IC at high masking risk is statistically significantly different between BI-RADS and PDA (p-value 0.022). Differences between BI-RADS and PDV, or BI-RADS and DTMM, are not statistically significant. CONCLUSION: Measures based on density maps, and in particular PDA, are promising tools to identify women at high risk for a masked cancer.

About 1 in 8 women will develop breast cancer. Histological tumor grade and biological markers such as oestrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2) are prognostic indicators for the clinical response to medical treatment and patient prognosis. Breast ultrasound (US) is mainly used to diagnose breast cancer or guide breast biopsies. However, quantitative analysis of US could also be of clinical value by giving additional information on tissue type. The automated breast volume scanner (ABUS) allows an automated assessment of a complete 3D US breast volume. By analyzing the texture of the B-mode images of the ABUS we have already shown that 3D quantitative breast ultrasound (3DQBUS) provides good discrimination between fibroadenomas (benign lesions) and invasive ductal carcinomas (IDC)(AUC=0.89). In this study we investigated whether 3DQBUS can be used to predict grade and receptor status.

Item does not contain fulltext OBJECTIVES: To compare the diagnostic performance of contrast-enhanced spectral mammography (CESM) to digital mammography (MG) and magnetic resonance imaging (MRI) in a prospective two-centre, multi-reader study. METHODS: One hundred seventy-eight women (mean age 53 years) with invasive breast cancer and/or DCIS were included after ethics board approval. MG, CESM and CESM + MG were evaluated by three blinded radiologists based on amended ACR BI-RADS criteria. MRI was assessed by another group of three readers. Receiver-operating characteristic (ROC) curves were compared. Size measurements for the 70 lesions detected by all readers in each modality were correlated with pathology. RESULTS: Reading results for 604 lesions were available (273 malignant, 4 high-risk, 327 benign). The area under the ROC curve was significantly larger for CESM alone (0.84) and CESM + MG (0.83) compared to MG (0.76) (largest advantage in dense breasts) while it was not significantly different from MRI (0.85). Pearson correlation coefficients for size comparison were 0.61 for MG, 0.69 for CESM, 0.70 for CESM + MG and 0.79 for MRI. CONCLUSIONS: This study showed that CESM, alone and in combination with MG, is as accurate as MRI but is superior to MG for lesion detection. Patients with dense breasts benefitted most from CESM with the smallest additional dose compared to MG. KEY POINTS: * CESM has comparable diagnostic performance (ROC-AUC) to MRI for breast cancer diagnostics. * CESM in combination with MG does not improve diagnostic performance. * CESM has lower sensitivity but higher specificity than MRI. * Sensitivity differences are more pronounced in dense and not significant in non-dense breasts. * CESM and MRI are significantly superior to MG, particularly in dense breasts.