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Abstract Objectives We aimed to compare the recall rate (RR) and the cancer detection rate (CDR) of combined full field digital mammography and digital breast tomosynthesis (FFDM + DBT) to those of full field digital mammography (FFDM) alone in breast cancer survivors. Methods We enrolled 146 female breast cancer survivors schedule. All patients underwent FFDM and DBT in the same setting. Results of FFDM alone were compared to those of FFDM + DBT regarding patients' RR and CDR. Sensitivity, specificity, accuracy, positive and negative predictive values were also calculated for FFDM alone and for FFDM + DBT in detecting breast cancer lesions. Results Our results showed that FFDM + DBT decreased patients' RR by 3.4% and increased the CDR by 4.1%. Reduction in RR was evident in higher breast densities. FFDM mammography had 18 false negative lesions and 29 false positives. Sensitivity, specificity, accuracy, NPV and PPV in detecting breast lesions were: 84.2%, 53.1%, 64.0%, 86.7% and 48.9% for FFDM compared to 100%, 92.1%, 95.3%, 100% and 89.7% for FFDM + DBT. Conclusion Combined FFDM + DBT in the post breast cancer surveillance regimen has shown to reduce the patients' RR and to increase the CDR. FFDM + DBT had higher diagnostic accuracy than FFDM alone. FFDM + DBT ought to be a standard combination in the breast cancer surveillance in treated patients. [ABSTRACT FROM AUTHOR]

Objective To assess the efficiency of dual energy contrast enhanced mammography in the assessment of the indeterminate breast lesions (BIRADS 3 and BIRADS 4). Materials and methods 34 female having 39 indeterminate breast lesions (BIRADS 3 and BIRADS 4) by digital mammography were further examined by dual energy contrast enhanced mammography. Two images were acquired at low and high energy in MLO view after 2 min and in CC view at 4 min post iodinated contrast injection (1.5 ml/kg with flow of 4 ml/s). Images were processed to obtain subtracted images to enhance the areas of the contrast uptake. Results Results from pathology were detected for all cases. Contrast enhanced digital mammography showed specificity, sensitivity, PPV, NPV, FDR, FPR and accuracy of 93.75%, 91.3%, 88.2%, 95.4%, 11.7%, 8.6% and 92.3% respectively compared to full field digital mammography which were 68.75%, 69.5%, 61.1%, 76.1%, 38.8%, 30% and 69.2% respectively. Conclusion Contrast-enhanced digital mammography is a useful tool to be used for breast cancer detection especially in indeterminate lesions (BIRADS 3 and 4). [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Increased mammographic breast density is a significant risk factor for breast cancer. A reproducible, accurate, and automated breast density measurement is required for full-field digital mammography (FFDM) to support clinical applications. We evaluated a novel automated percentage of breast density measure (PDa) and made comparisons with the standard operator-assisted measure (PD) using FFDM data. Methods: We used a nested breast cancer case-control study matched on age, year of mammogram and diagnosis with images acquired from a specific direct x-ray conversion FFDM technology. PDa was applied to the raw and clinical display (or processed) representation images. We evaluated the transformation (pixel mapping) of the raw image, giving a third representation (raw-transformed), to improve the PDa performance using differential evolution optimization. We applied PD to the raw and clinical display images as a standard for measurement comparison. Conditional logistic regression was used to estimate the odd ratios (ORs) for breast cancer with 95% confidence intervals (CI) for all measurements; analyses were adjusted for body mass index. PDa operates by evaluating signal-dependent noise (SDN), captured as local signal variation. Therefore, we characterized the SDN relationship to understand the PDa performance as a function of data representation and investigated a variation analysis of the transformation. Results: The associations of the quartiles of operator-assisted PD with breast cancer were similar for the raw (OR: 1.00 [ref.]; 1.59 [95% CI, 0.93-2.70]; 1.70 [95% CI, 0.95-3.04]; 2.04 [95% CI, 1.13-3.67]) and clinical display (OR: 1.00 [ref.]; 1.31 [95% CI, 0.79-2.18]; 1.14 [95% CI, 0.65-1.98]; 1.95 [95% CI, 1.09-3.47]) images. PDa could not be assessed on the raw images without preprocessing. However, PDa had similar associations with breast cancer when assessed on 1) raw-transformed (OR: 1.00 [ref.]; 1.27 [95% CI, 0.74-2.19]; 1.86 [95% CI, 1.05-3.28]; 3.00 [95% CI, 1.67-5.38]) and 2) clinical display (OR: 1.00 [ref.]; 1.79 [95% CI, 1.04-3.11]; 1.61 [95% CI, 0.90-2.88]; 2.94 [95% CI, 1.66-5.19]) images. The SDN analysis showed that a nonlinear relationship between the mammographic signal and its variation (ie, the biomarker for the breast density) is required for PDa. Although variability in the transform influenced the respective PDa distribution, it did not affect the measurement's association with breast cancer. Conclusions: PDa assessed on either raw-transformed or clinical display images is a valid automated breast density measurement for a specific FFDM technology and compares well against PD. Further work is required for measurement generalization. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Breast density is a significant breast cancer risk factor measured from mammograms. Evidence suggests that the spatial variation in mammograms may also be associated with risk. We investigated the variation in calibrated mammograms as a breast cancer risk factor and explored its relationship with other measures of breast density using full field digital mammography (FFDM). Materials and Methods: A matched case-control analysis was used to assess a spatial variation breast density measure in calibrated FFDM images, normalized for the image acquisition technique variation. Three measures of breast density were compared between cases and controls: (a) the calibrated average measure, (b) the calibrated variation measure, and (c) the standard percentage of breast density (PD) measure derived from operator-assisted labeling. Linear correlation and statistical relationships between these three breast density measures were also investigated. Results: Risk estimates associated with the lowest to highest quartiles for the calibrated variation measure were greater in magnitude (odds ratios: 1.0 [ref.], 3.5, 6.3, and 11.3) than the corresponding risk estimates for quartiles of the standard PD measure (odds ratios: 1.0 [ref.], 2.3, 5.6, and 6.5) and the calibrated average measure (odds ratios: 1.0 [ref.], 2.4, 2.3, and 4.4). The three breast density measures were highly correlated, showed an inverse relationship with breast area, and related by a mixed distribution relationship. Conclusion: The three measures of breast density capture different attributes of the same data field. These preliminary findings indicate the variation measure is a viable automated method for assessing breast density. Insights gained by this work may be used to develop a standard for measuring breast density. [ABSTRACT FROM AUTHOR]

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