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To evaluate conspicuity of screen-detected cancers on two-dimensional synthetic mammography (SM) reconstructed from digital breast tomosynthesis (DBT) compared to two-dimensional full field digital mammography (FFDM).IRB-approved retrospective review of consecutive screen-detected cancers from October 1, 2015 to June 30, 2017 was performed. All examinations were reviewed by three radiologists in consensus (n = 224); a score of 1-3 was given to each screen-detected cancer on SM vs. FFDM [1 = FFDM more conspicuous than SM, 2 = FFDM equivalent to SM, and 3 = SM more conspicuous than FFDM]. Findings considered only visible on tomosynthesis (n = 40), without medical history (n = 2), and with skin thickening only (n = 1) were excluded, leaving 181 cases as the study population. The longitudinal medical record was reviewed to determine patient demographics and outcomes of imaging surveillance and biopsy.Mammographic features on SM (n = 181) were calcifications (n = 68, 37.8%), masses (n = 51, 27.8%), asymmetries (n = 50, 27.6% [11 focal asymmetries]), and distortion (n = 12, 6.8%). The majority (76%, 137/181) of findings were equal or more conspicuous on SM vs. FFDM. However, calcifications and distortion greater than 2 cm were more conspicuous on SM and asymmetries were less conspicuous on SM vs. FFDM, controlling for menopausal status, family or personal history of breast cancer, BRCA status, and breast density.Although the majority of screen-detected cancers are equal to more conspicuous on SM when compared to FFDM, calcifications and asymmetries2cm were less conspicuous on SM than FFDM. When SM + DBT is used as an alternative to FFDM + DBT in breast cancer screening, caution should be taken when assessing one-view asymmetries and findings2cm on SM.

Rationale and Objectives Synthesized mammography with digital breast tomosynthesis (SM+DBT) and full-field digital mammography with DBT were prospectively evaluated for recall rate (RR), cancer detection rate (CDR), positive predictive value 1 (PPV1), lesion recall differences, and disagreements in recall for additional imaging. Materials and Methods From December 15, 2015 to January 15, 2017, after informed consent was obtained for this Health Insurance Portability and Accountability Act compliant study, each enrolled patient's SM+DBT and FFDM+DBT were interpreted sequentially by one of eight radiologists. RR, CDR, PPV1, and imaging findings (asymmetry, focal asymmetry, mass, architectural distortion, and calcifications) recalled were reviewed. Results For SM+DBT and FFDM+DBT in 1022 patients, RR was 7.3% and 7.9% (SM+DBT vs. FFDM+DBT: diff= −0.6%; 90% CI= −1.4%, 0.1%); CDR was 6.8 and 7.8 per 1000 (SM+DBT vs. FFDM+DBT: diff= −1.0, 95% CI= −5.5, 2.8, p = 0.317); PPV1 was 9.3% and 9.9% (relative positive predictive value for SM+DBT vs. FFDM+DBT: 0.95, 95% CI: 0.73–1.22, p = 0.669). FFDM+DBT detected eight cancers; SM+DBT detected seven (missed 1 cancer with calcifications). SM+DBT and FFDM+DBT disagreed on patient recall for additional imaging in 19 patients, with majority (68%, 13/19 patients) in the recall of patients for calcifications. For calcifications, SM+DBT recalled six patients that FFDM+DBT did not recall, and FFDM+DBT recalled seven patients that SM+DBT did not recall, even though the total number of calcifications finding recalled was similar overall for both SM+DBT and FFDM+DBT. Conclusion Disagreement in recall of patients for calcifications may impact cancer detection by SM+DBT, warranting further investigation.

To compare mass size measurements between synthesized mammography (SM) and full-field digital mammography (FFDM), and to assess interobserver agreement for those measurements.One hundred and forty-three patients who underwent FFDM and digital breast tomosynthesis acquisitions during the same compression session were included in the study. Two observers with four-and 1-year experience with digital breast tomosynthesis and SM images, respectively, measured mass sizes independently in two different sessions that were 2 weeks apart, and were blinded to each other. The first session included only FFDM images, and the second session included only SM images. Largest dimension of masses was measured in millimeters. Paired t test was used to compare differences in size measurements between FFDM and SM images. Intraclass correlation coefficient test was used to analyze interobserver agreement. Bland-Altman analyses were performed to evaluate agreements between the imaging techniques and between the observers.The mean mass sizes on FFDM and SM images were 20.27 ± 14.10 and 18.50 ± 13.05 mm, respectively, for the first observer and 21.56 ± 14.84 and 19.89 ± 13.68 mm, respectively, for the second observer. The mass size measurements were significantly different between FFDM and SM for both observers (p0.001). Range of measurement errors, defined as 95% limits of agreements between two imaging techniques for observers 1 and 2 were ±1.96*1.36 mm, and ±1.96*1.53 mm, respectively. Range of measurement errors, defined as 95% limits of agreements between two observers for SM and FFDM were ±1.96*3.68 mm, and ±1.96*3.35 mm, respectively.The measured mass sizes were significantly smaller on SM than FFDM images, and the interobserver differences for both of the imaging techniques were greater than the differences measured between FFDM and SM images.

Rationale and Objectives Contrast-enhanced spectral mammography (CESM) combines the benefits of full field digital mammography with the concept of tumor angiogenesis. Technique and practical applications of CESM are discussed. Materials and Methods An overview of the technique is followed by a demonstration of practical applications of CESM in our practice. Results We have successfully implemented CESM into our practice as a screening, diagnostic, staging, and treatment response tool. Conclusion It is important to understand the technique of CESM and how to incorporate it into practice.

Rationale and Objectives Contrast-enhanced spectral mammography (CESM) uses full field digital mammography with the added benefit of intravenous contrast administration to significantly reduce false-positive and false-negative results and improve specificity while maintaining high sensitivity. For CESM to fulfill its purpose, one should be aware of possible artifacts and other factors which may interfere with image quality, and attention should be taken to minimize these factors. Materials and Methods This pictorial demonstration will depict types of artifacts detected and other factors that interfere with image acquisition in our practice since CESM implementation. Results Many of the artifacts and other factors we have encountered while using CESM have simple solutions to resolve them. Conclusion The illustrated artifacts and other factors interfering with image quality will serve as a useful reference to anyone using CESM.

To evaluate whether a computer-aided diagnosis (CADx) technique can accurately classify breast calcifications in full-field digital mammograms (FFDMs) as malignant or benign. The computer technique was developed previously on screen-film mammograms (SFMs) in which individual calcifications were identified manually. The present study evaluated the computer technique independently on a new database of FFDM images with automatic detection of the individual calcifications.We analyzed 49 consecutive FFDM cases (19 cancers) that showed suspicious calcifications. Four mammography radiologists read soft-copy mammograms retrospectively and electronically indicated the region of calcifications in each image. The computer then automatically detected the individual calcifications within the indicated region and analyzed eight features of calcification morphology and distribution to arrive at an estimated likelihood of malignancy. The radiologists entered Breast Imaging Report and Data System assessments before and after seeing the computer results. Performance was analyzed using receiver operating characteristic analysis.Despite variability in radiologist-indicated regions of calcifications, the computer achieved consistently high performance taking input from the four radiologists (receiver operating characteristic curve area, A(z): 0.80, 0.80, 0.78, and 0.77; differences not statistically significant). Previous results showed that the computer technique achieved an A(z) value of 0.80 on SFMs, which improved radiologists' performance significantly.The computer technique appears to maintain consistently high performance in classifying calcifications in FFDMs as malignant or benign without requiring substantial modification from its initial development on SFMs. The computer performance appears to be robust with respect to variations in radiologists' input.

Rationale and Objectives The authors developed and evaluated a method of computer-aided diagnosis (CAD) for mass detection with full-field digital mammography (FFDM). Materials and Methods The new CAD method for FFDM employs adaptive, nonlinear multiscale processing and hybrid classification methods. The major strategies are ( a ) to “standardize” the mammographic image before it is input to the analysis modules, ( b ) to adapt the segmentation of suspicious regions adapt to accommodate different characteristics of masses and mammograms, and ( c ) to use combined “hard” and “soft” decision making in discriminating between mass and normal tissue regions. Two data sets of diagnostic FFDM mammograms were used. The training data set includes 36 normal and 24 abnormal mammograms (34 masses), and the testing data set includes 24 normal and 10 abnormal mammograms (10 masses). The tumors in this diagnostic database were more subtle and difficult to detect than those in screening databases the authors have used before. Results With the limited database and a partial optimization, a sensitivity of 91% was obtained in training, with a false-positive rate of 3.21 per image. At this trained operating point of the CAD system, six of 10 subtle masses were detected in testing. Conclusion The CAD algorithms developed in screen-film mammography can be modified for FFDM. More data analysis and system optimization and evaluation will be needed before CAD can be integrated efficiently into the performance of FFDM.

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 (PD a ) 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. PD a 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 PD a 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. PD a operates by evaluating signal-dependent noise (SDN), captured as local signal variation. Therefore, we characterized the SDN relationship to understand the PD a 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. PD a could not be assessed on the raw images without preprocessing. However, PD a 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 PD a . Although variability in the transform influenced the respective PD a distribution, it did not affect the measurement's association with breast cancer. Conclusions PD a 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.

The purpose of this study was to assess the performance of a MicroDose photon-counting full-field digital mammography (PCM) system in comparison to full-field digital mammography (FFDM) for area under the receiver-operating characteristic (ROC) curve (AUC), sensitivity, specificity, and feature analysis of standard-view mammography for women presenting for screening mammography, diagnostic mammography, or breast biopsy.A total of 133 women were enrolled in this study at two European medical centers, with 67 women who had a pre-existing 10-36 months FFDM enrolled prospectively into the study and 66 women who underwent breast biopsy and had screening PCM and diagnostic FFDM, including standard craniocaudal and mediolateral oblique views of the breast with the lesion, enrolled retrospectively. The case mix consisted of 49 cancers, 17 biopsy-benign cases, and 67 normal cases. Sixteen radiologists participated in the reader study and interpreted all 133 cases in both conditions, separated by washout period of ≥4 weeks. ROC curve and free-response ROC curve analyses were performed for noninferiority of PCM compared to FFDM using a noninferiority margin Δ value of 0.10. Feature analysis of the 66 cases with lesions was conducted with all 16 readers at the conclusion of the blinded reads. Mean glandular dose was recorded for all cases.The AUC for PCM was 0.947 (95% confidence interval [CI], 0.920-0.974) and for FFDM was 0.931 (95% CI, 0.898-0.964). Sensitivity per case for PCM was 0.936 (95% CI, 0.897-0.976) and for FFDM was 0.908 (95% CI, 0.856-0.960). Specificity per case for PCM was 0.764 (95% CI, 0.688-0.841) and for FFDM was 0.749 (95% CI, 0.668-0.830). Free-response ROC curve figures of merit were 0.920 (95% CI, 0.881-0.959) and 0.903 (95% CI, 0.858-0.948) for PCM and FFDM, respectively. Sensitivity per lesion was 0.903 (95% CI, 0.846-0.960) and 0.883 (95% CI, 0.823-0.944) for PCM and FFDM, respectively. The average false-positive marks per image of noncancer cases were 0.265 (95% CI, 0.171-0.359) and 0.281 (95% CI, 0.188-0.374) for PCM and FFDM, respectively. Noninferiority P values for AUC, sensitivity (per case and per lesion), specificity, and average false-positive marks per image were all statistically significant (P.001). The noninferiority P value for free-response ROC was.025, from the 95% CI for the difference. Feature analysis resulted in PCM being preferred to FFDM by the readers for ≥70% of the cases. The average mean glandular dose for PCM was 0.74 mGy (95% CI, 0.722-0.759 mGy) and for FFDM was 1.23 mGy (95% CI, 1.199-1.262 mGy).In this study, radiologist performance with PCM was not inferior to that with conventional FFDM at an average 40% lower mean glandular dose.