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Rationale and Objectives Although screening mammography is essential to reducing breast cancer morbidity and mortality, barriers exist especially among underrepresented minority groups. There are few studies of mammogram screening among American Indian women, many of whom reside in rural areas where screening access is challenging. A mobile mammography unit served 24 Indian Health Service clinics during 2013-17. Screening mammography adherence was evaluated. Materials and Methods Among mobile unit women, ‘adherence to screening’ was determined by the date of the most recent prior mammogram. Those having a prior mammogram 9-27 months ago were classified as ‘adherent to screening’. Comparison screening data were obtained from the American College of Radiology National Mammography Database, consisting of screening cases occurring in year 2015. Additionally, among mobile unit women ‘continued adherence to screening’ was determined, defined as at least one repeat screening at the mobile unit within the subsequent 9-27 months after a screening there. Results Among 1,615 mobile unit women, 624 (38.6%) were adherent to screening. Among 2,509,826 National Mammography Database women, 1,481,021 (59.0%) were adherent to screening. (p 27-month interval between mammograms was 3.13 (95% CI 2.91-3.36) times greater among mobile unit women than National Mammography Database women. ‘Continued adherence to screening’ of mobile unit women was 428/1194 (35.9%). Conclusion Adherence to screening and continued adherence to screening were low among mobile unit women and time interval between screenings was longer than National Mammography Database women. Factors to improve screening adherence among these underserved women should be determined.

Most breast cancers in Oman are diagnosed at advanced stages and therefore early detection is important. The Oman Cancer Association (OCA) initiated a mobile mammography program in 2009, but no studies have evaluated its impact. This study aimed at estimating the proportion and predictors of OCA-screened women who had repeated mammography (adherence) and the sensitivity and specificity of the program. Demographic, screening, diagnosis, and treatment data of 13,079 women screened in the OCA mammography clinic from 2009 to 2016, and medical records of all breast cancer patients seen at Royal and Sultan Qaboos University hospitals during the same period were retrieved. Logistic regression analysis was conducted to identify predictors of adherence. A total of 8278 screened women over age 42 years (median age of 50 ± 8 years) were included in the study. Only 18% of initially negative screened women were compliant with recommended subsequent screening. Predictors of adherence included age (50–69 years), family history of cancer, family history of breast cancer, and breast self-examination. The overall cancer detection rate was 4.1/1000 screened women. Positive predictive value of screening was 4.7% with a sensitivity rate of 53% and specificity of 92%. This study showed a low mammography adherence among previously screened women. The study revealed low sensitivity, high specificity, and an acceptable cancer detection rate. Future programs should focus on improving data collection of screened women, maintaining the linkage of databases of screening and treatment clinics, and developing guidelines for breast cancer screening in Oman. The recommendations based on the study results should be incorporated into future professional, patient, and public cancer education programs.

Educating the public about breast cancer screening and diagnosis is important. Medical and regulatory agencies encourage shared decision making about undergoing breast cancer screening, and there are many places women can get information and misinformation. The Internet and other media sources present information that may not be correct or understandable. Breast radiologists are uniquely qualified to provide women with the accurate information necessary to enable informed choices. As a specialty, we have an obligation to our community to provide relevant and understandable information. We can accomplish that through community outreach forums. Presentations should be understandable with plain language, focusing on our key message and using pertinent images or icons. Slides should be simple and avoid medical jargon or complex statistics. As we engage with the community, we provide a vital service to the health of our community and foster respect of our specialty.

American Indians and Alaska Natives (AI/AN) are underserved populations who suffer from several health disparities, 1 of which is cancer. Malignancies, especially cancers of the breast, liver, and lung, are common causes of death in this population. Health care disparities in this population include more limited access to diagnostic radiology because of geographic and/or health system limitations. Early detection of these cancers may be enabled by improving patient and physician access to medical imaging. Awareness by the radiology community of the cancer disparities among this population is needed to support research targeted to this specific ethnic group and to support outreach efforts to provide more imaging opportunities. Providing greater access to imaging facilities will also improve patient compliance with screening recommendations, ultimately improving mortality in these populations.

Most breast cancers in Oman are diagnosed at advanced stages and therefore early detection is important. The Oman Cancer Association (OCA) initiated a mobile mammography program in 2009, but no studies have evaluated its impact. This study aimed at estimating the proportion and predictors of OCA-screened women who had repeated mammography (adherence) and the sensitivity and specificity of the program. Demographic, screening, diagnosis, and treatment data of 13,079 women screened in the OCA mammography clinic from 2009 to 2016, and medical records of all breast cancer patients seen at Royal and Sultan Qaboos University hospitals during the same period were retrieved. Logistic regression analysis was conducted to identify predictors of adherence. A total of 8278 screened women over age 42 years (median age of 50 ± 8 years) were included in the study. Only 18% of initially negative screened women were compliant with recommended subsequent screening. Predictors of adherence included age (50-69 years), family history of cancer, family history of breast cancer, and breast self-examination. The overall cancer detection rate was 4.1/1000 screened women. Positive predictive value of screening was 4.7% with a sensitivity rate of 53% and specificity of 92%. This study showed a low mammography adherence among previously screened women. The study revealed low sensitivity, high specificity, and an acceptable cancer detection rate. Future programs should focus on improving data collection of screened women, maintaining the linkage of databases of screening and treatment clinics, and developing guidelines for breast cancer screening in Oman. The recommendations based on the study results should be incorporated into future professional, patient, and public cancer education programs.

To compare radiologists' sensitivity, confidence level, and reading efficiency of detecting microcalcifications in digital breast tomosynthesis (DBT) at two clinically relevant dose levels.Six 5-cm-thick heterogeneous breast phantoms embedded with a total of 144 simulated microcalcification clusters of four speck sizes were imaged at two dose modes by a clinical DBT system. The DBT volumes at the two dose levels were read independently by six MQSA radiologists and one fellow with 1-33 years (median 12 years) of experience in a fully-crossed counter-balanced manner. The radiologist located each potential cluster and rated its conspicuity and his/her confidence that the marked location contained a cluster. The differences in the results between the two dose modes were analyzed by two-tailed paired t-test.Compared to the lower-dose mode, the average glandular dose in the higher-dose mode for the 5-cm phantoms increased from 1.34 to 2.07 mGy. The detection sensitivity increased for all speck sizes and significantly for the two smaller sizes (p0.05). An average of 13.8% fewer false positive clusters was marked. The average conspicuity rating and the radiologists' confidence level were higher for all speck sizes and reached significance (p0.05) for the three larger sizes. The average reading time per detected cluster reduced significantly (p0.05) by an average of 13.2%.For a 5-cm-thick breast, an increase in average glandular dose from 1.34 to 2.07 mGy for DBT imaging increased the conspicuity of microcalcifications, improved the detection sensitivity by radiologists, increased their confidence levels, reduced false positive detections, and increased the reading efficiency.

We analyze the performance of a mammographically-configured, automated breast ultrasound (McABUS) scanner combined with a digital breast tomosynthesis (DBT) system. The GE Invenia ultrasound system was modified for integration with GE DBT systems. The ultrasound and DBT imaging were performed in the same mammographic compression. Our small preliminary study included thirteen cases, six of which contained invasive cancers. From analysis of these cases, current limitations and corresponding potential improvements of the system were determined. A registration analysis was performed to compare the ease of McABUS to DBT registration for this system with that of two systems designed previously. It was observed that, in comparison to data from an earlier study, the McABUS to DBT registration alignment errors for both this system and for a previously built combined system were smaller than for a previously built standalone McABUS system.

Rationale and Objectives This study aimed to compare Breast Imaging Reporting and Data System (BI-RADS) assessment of lesions in two-view digital mammogram (DM) to two-view wide-angle digital breast tomosynthesis (DBT) without DM. Materials and Methods With Institutional Review Board approval and written informed consent, two-view DBTs were acquired from 134 subjects and the corresponding DMs were collected retrospectively. The study included 125 subjects with 61 malignant (size: 3.9–36.9 mm, median: 13.4 mm) and 81 benign lesions (size: 4.8–43.8 mm, median: 12.0 mm), and 9 normal subjects. The cases in the two modalities were read independently by six experienced Mammography Quality Standards Act radiologists in a fully crossed counterbalanced manner. The readers were blinded to the prevalence of malignant, benign, or normal cases and were asked to assess the lesions based on the BI-RADS lexicon. The ratings were analyzed by the receiver operating characteristic methodology. Results Lesion conspicuity was significantly higher ( P P = .0001) from 0.783 (range: 0.723–0.886) for DM to 0.911 (range: 0.884–0.936) for DBT. Of the 366 ratings for malignant lesions, 343 on DBT and 278 on DM were rated as BI-RADS 4a and above. Of the 486 ratings for benign lesions, 220 on DBT and 206 on DM were rated as BI-RADS 4a and above. On average, 17.8% (65 of 366) more malignant lesions and 2.9% (14 of 486) more benign lesions would be recommended for biopsy using DBT. The inter-radiologist variability was reduced significantly. Conclusion With DBT alone, the BI-RADS assessment of breast lesions and inter-radiologist reliability were significantly improved compared to DM.

The purpose of this study is to develop a new method for generating synthesized mammogram (SM) from digital breast tomosynthesis (DBT) and to assess its potential as an adjunct to DBT. We first applied multiscale bilateral filtering to the reconstructed DBT slices to enhance the high-frequency features and reduce noise. A maximum intensity projection (MIP) image was then obtained from the high-frequency components of the DBT slices. A multiscale image fusion method was designed to combine the MIP image and the central DBT projection view into an SM and further enhance the high-frequency features. We conducted a pilot reader study to visually assess the image quality of SM in comparison to full field digital mammograms (FFDM). For each DBT craniocaudal or mediolateral view, a clinical FFDM of the corresponding view was retrospectively collected. Three MQSA radiologists, blinded to the pathological and other clinical information, independently interpreted the SM and the corresponding FFDM side by side marked with the lesion locations. The differences in the BI-RADS assessments of both MCs and masses between SM and FFDM did not achieve statistical significance for all three readers. The conspicuity of MCs on SM was superior to that on FFDM and the BI-RADS assessments of MCs were comparable while the conspicuity of masses on SM was degraded and interpretation on SM was less accurate than that on FFDM. The SM may be useful for efficient prescreening of MCs in DBT but the DBT should be used for detection and characterization of masses.

The design and performance of a mammographically configured, dual-sided, automated breast ultrasound (ABUS) 3-D imaging system are described. Dual-sided imaging (superior and inferior) is compared with single-sided imaging to aid decisions on clinical implementation of the more complex, but potentially higher-quality dual-sided imaging. Marked improvement in image quality and coverage of the breast is obtained in dual-sided ultrasound over single-sided ultrasound. Among hypo-echoic masses imaged, there are increases in the mean contrast-to-noise ratio of 57% and 79%, respectively, for spliced dual-sided versus superior or inferior single-sided imaging. The fractional breast volume coverage, defined as the percentage volume in the transducer field of view that is imaged with clinically acceptable quality, is improved from 59% in both superior and inferior single-sided imaging to 89% in dual-sided imaging. Applying acoustic coupling to the breast requires more effort or sophisticated methods in dual-sided imaging than in single-sided imaging.

This work investigates the application of a deformable localization/mapping method to register lesions between the digital breast tomosynthesis (DBT) craniocaudal (CC) and mediolateral oblique (MLO) views and automated breast ultrasound (ABUS) images. This method was initially validated using compressible breast phantoms. This methodology was applied to 7 patient data sets containing 9 lesions. The automated deformable mapping algorithm uses finite element modeling and analysis to determine corresponding lesions based on the distance between their centers of mass (dCOM) in the deformed DBT model and the reference ABUS model. This technique shows that location information based on external fiducial markers is helpful in the improvement of registration results. However, use of external markers are not required for deformable registration results described by this methodology. For DBT (CC view) mapped to ABUS, the mean dCOM was 14.9 ± 6.8 mm based on 9 lesions using 6 markers in deformable analysis. For DBT (MLO view) mapped to ABUS, the mean dCOM was 13.7 ± 6.8 mm based on 8 lesions using 6 markers in analysis. Both DBT views registered to ABUS lesions showed statistically significant improvements (p ≤ 0.05) in registration using the deformable technique in comparison to a rigid registration. Application of this methodology could help improve a radiologist's characterization and accuracy in relating corresponding lesions between DBT and ABUS image datasets, especially for cases of high breast densities and multiple masses.

Mobile mammography can improve access to screening mammography in rural areas and underserved populations. We evaluated the frequency of incomplete reports in mobile mammography screening and the relationships between prior mammograms and recall rates. The frequency of incomplete mammogram reports, the subgroups of those needing prior comparison mammograms, recalls for additional imaging, and availability of prior mammograms of a mobile screening mammography unit were compared with fixed site mammography from January 1, 2007 through December 31, 2009. All mobile unit mammograms were full field digital mammography (FFDM). Differences between rates of recall, incomplete reports, and availability of prior mammograms were calculated using the Chi-Square statistic. Of 2640 mobile mammography cases, 21.9% (578) reports were incomplete, versus 15.2% (7653) (p ≤ 0.001) of 50325 fixed site reports. Of incomplete cases, recall for additional imaging occurred among 8.3% (218) of mobile mammography reports versus 11.3% (5708) (p ≤ 0.001) of fixed site reports. Prior mammograms were needed among 13.6% (360) of mobile mammography versus 3.9% (1945) (p ≤ 0.001) of fixed site reports. Mobile mammography recall rate varied with availability of prior mammograms: 16.0% (54) when no prior mammograms, 7.6% (127) when prior mammograms were elsewhere but unavailable and 5.9% (37) when prior FFDM were immediately available (p ≤ 0.001). Incomplete reports were more frequent in mobile mammography than the fixed site. The availability of prior comparison mammograms at time of interpretation decreased the rate of incomplete mammogram reports. Recall rates were higher without prior comparison mammograms and lowest when comparison FFDM mammograms were available.

Breast density is one of the most significant factors for cancer risk. In this study, we proposed a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammography (DM). The deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD). PD was calculated as the ratio of the dense area to the breast area based on the probability of each pixel belonging to dense region or fatty region at a decision threshold of 0.5. The DCNN estimate was compared to a feature-based statistical learning approach, in which gray level, texture and morphological features were extracted from each ROI and the least absolute shrinkage and selection operator (LASSO) was used to select and combine the useful features to generate the PMD. The reference PD of each image was provided by two experienced MQSA radiologists. With IRB approval, we retrospectively collected 347 DMs from patient files at our institution. The 10-fold cross-validation results showed a strong correlation r=0.96 between the DCNN estimation and interactive segmentation by radiologists while that of the feature-based statistical learning approach vs radiologists’ segmentation had a correlation r=0.78. The difference between the segmentation by DCNN and by radiologists was significantly smaller than that between the feature-based learning approach and radiologists (p < 0.0001) by two-tailed paired t-test. This study demonstrated that the DCNN approach has the potential to replace radiologists’ interactive thresholding in PD estimation on DMs.

In B-mode imaging of the dependent or compressed breast, wave incidence at steep angles can change propagation directions and induce areas of signal dropout. To evaluate the image anomalies in reasonable simulation times, we performed full-wave studies for center frequencies of 1 and 4 MHz. Speed of sound and density of skin, typical coupling gel, and adipose tissue were assigned to the test couplant. Compared with commercial gel, skin-like couplant reduced the dropout area at 1 and 4 MHz by 57.1% and 96.7%, respectively, consistent with a decreased average beam deflection in the breast. Conversely, the adipose-like couplant increased the dropout area from that of simulated commercial gel by 26.5% and 36.7% at 1 and 4 MHz, respectively. In addition, the skin-like couplant resulted in the greatest beam deflection inside the breast among all couplants. The findings could aid the use of three-dimensional simulations to design ultrasound couplants for beam passage through tissue boundaries at steep angles to improve corrections of signal dropout and defocusing and in compound imaging.

Screening mammography is the most effective and low-cost method to date for early cancer detection. Mammographic breast density has been shown to be highly correlated with breast cancer risk. We are developing a radiomic model for BI-RADS density categorization on digital mammography (FFDM) with a supervised machine learning approach. With IRB approval, we retrospectively collected 478 FFDMs from 478 women. As a gold standard, breast density was assessed by an MQSA radiologist based on BI-RADS categories. The raw FFDMs were used for computerized density assessment. The raw FFDM first underwent log-transform to approximate the x-ray sensitometric response, followed by multiscale processing to enhance the fibroglandular densities and parenchymal patterns. Three ROIs were automatically identified based on the keypoint distribution, where the keypoints were obtained as the extrema in the image Gaussian scale-space. A total of 73 features, including intensity and texture features that describe the density and the parenchymal pattern, were extracted from each breast. Our BI-RADS density estimator was constructed by using a random forest classifier. We used a 10-fold cross validation resampling approach to estimate the errors. With the random forest classifier, computerized density categories for 412 of the 478 cases agree with radiologist’s assessment (weighted kappa = 0.93). The machine learning method with radiomic features as predictors demonstrated a high accuracy in classifying FFDMs into BI-RADS density categories. Further work is underway to improve our system performance as well as to perform an independent testing using a large unseen FFDM set.

With imaging conditions that did not include x-ray source motion or patient motion during acquisition of the projection views, narrow-angle digital breast tomography (DBT) provided higher detection sensitivity and conspicuity than wide-angle DBT for subtle microcalcification clusters.

Breast cancer is a burden for American Indian (AI) women who have younger age at diagnosis and higher stage of disease. Rural areas also have had less access to screening mammography. An Indian Health Service Mobile Women’s Health Unit (MWHU) was implemented to improve mammogram screening of AI women in the Northern Plains. Our purpose was to determine the past adherence to screening mammography at a woman’s first presentation to the MWHU for mammogram screening. Date of the most recent prior non-MWHU mammogram was obtained from mammography records. Adherence to screening guidelines was defined as the prior mammogram occurring 1–2 years before the first MWHU visit among women >41 years, and was the main outcome, whereas, age and clinic site were predictors. Adherence was compared with national data of the Breast Cancer Surveillance Consortium (BCSC). Among 1,771 women >41 years, adherence to screening mammography guidelines was 48.01 % among >65 years, 42.05 % among 50–64 years, 33.43 % among 41–49 years, and varied with clinic site (25.23–65.93 %). Age (p < 0.0001) and clinic site (p < 0.0001) were associated with adherence. Overall, adherence to screening mammography guidelines was found in 39.86 % (706/1771) of MWHU women versus 74.34 % (747,095/1,004,943) of BCSC women. The majority (60.14 %) of women at first presentation to the MWHU had not had mammograms in the previous 2 years, lower screening adherence than nationally (25.66 %). Adherence was lowest among women ages 41–49, and varied with clinic site. Findings suggest disparities in mammography screening among these women.

This study aimed to compare Breast Imaging Reporting and Data System (BI-RADS) assessment of lesions in two-view digital mammogram (DM) to two-view wide-angle digital breast tomosynthesis (DBT) without DM.With Institutional Review Board approval and written informed consent, two-view DBTs were acquired from 134 subjects and the corresponding DMs were collected retrospectively. The study included 125 subjects with 61 malignant (size: 3.9-36.9 mm, median: 13.4 mm) and 81 benign lesions (size: 4.8-43.8 mm, median: 12.0 mm), and 9 normal subjects. The cases in the two modalities were read independently by six experienced Mammography Quality Standards Act radiologists in a fully crossed counterbalanced manner. The readers were blinded to the prevalence of malignant, benign, or normal cases and were asked to assess the lesions based on the BI-RADS lexicon. The ratings were analyzed by the receiver operating characteristic methodology.Lesion conspicuity was significantly higher (P .0001) and fewer lesion margins were considered obscured in DBT. The mean area under the receiver operating characteristic curve for the six readers increased significantly (P = .0001) from 0.783 (range: 0.723-0.886) for DM to 0.911 (range: 0.884-0.936) for DBT. Of the 366 ratings for malignant lesions, 343 on DBT and 278 on DM were rated as BI-RADS 4a and above. Of the 486 ratings for benign lesions, 220 on DBT and 206 on DM were rated as BI-RADS 4a and above. On average, 17.8% (65 of 366) more malignant lesions and 2.9% (14 of 486) more benign lesions would be recommended for biopsy using DBT. The inter-radiologist variability was reduced significantly.With DBT alone, the BI-RADS assessment of breast lesions and inter-radiologist reliability were significantly improved compared to DM.

Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

OBJECTIVE. The objective of our study was to retrospectively evaluate the imaging findings of patients with breast cancer negative for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)—so-called “triple receptor–negative cancer”—and to compare the mammographic findings and clinical characteristics of triple receptor–negative cancer with non–triple receptor–negative cancers (i.e., ER-positive, PR-positive, or HER2-positive or two of the three markers positive). CONCLUSION. Triple receptor–negative cancer was most commonly an irregular noncalcified mass with ill-defined or spiculated margins on mammography and a hypoechoic or complex mass with an irregular shape and noncircumscribed margins on ultrasound. Most triple receptor–negative cancers were discovered on physical examination. Compared with non–triple receptor–negative cancers, triple receptor–negative cancers were found in younger women and were a higher pathologic grade.

Data relative to breast cancer among American Indian and Alaska native (AI/AN) women are limited and vary by regions. Despite national decreases in breast cancer incidence and mortality rates, declines in these measures have not yet appeared among AI/AN women. Health disparities in breast cancer persist, manifest by higher stage at diagnosis, and lower screening rates compared to other racial and ethnic groups. Disproportionately more AI/AN are younger at diagnosis. Screening beginning at age 40, improving access, annual rescreening, community education and outreach, and mobile mammography for rural areas are ways to improve these disparities in breast cancer.

To determine if digital breast tomosynthesis (DBT) performs comparably to mammographic spot views (MSVs) in characterizing breast masses as benign or malignant.This IRB-approved, HIPAA-compliant reader study obtained informed consent from all subjects. Four blinded Mammography Quality Standards Act-certified academic radiologists individually evaluated DBT images and MSVs of 67 masses (30 malignant, 37 benign) in 67 women (age range, 34-88 years). Images were viewed in random order at separate counterbalanced sessions and were rated for visibility (10-point scale), likelihood of malignancy (12-point scale), and Breast Imaging Reporting and Data System (BI-RADS) classification. Differences in mass visibility were analyzed by using the Wilcoxon matched-pairs signed-ranks test. Reader performance was measured by calculating the area under the receiver operating characteristic curve (A(z)) and partial area index above a sensitivity threshold of 0.90 (A(z)(0.90)) by using likelihood of malignancy ratings. Masses categorized as BI-RADS 4 or 5 were compared with histopathologic analysis to determine true-positive results for each modality.Mean mass visibility ratings were slightly better with DBT (range, 3.2-4.4) than with MSV (range, 3.8-4.8) for all four readers, with one reader's improvement achieving statistical significance (P = .001). The A(z) ranged 0.89-0.93 for DBT and 0.88-0.93 for MSV (P ≥ .23). The A(z)((0.90)) ranged 0.36-0.52 for DBT and 0.25-0.40 for MSV (P ≥ .20). The readers characterized seven additional malignant masses as BI-RADS 4 or 5 with DBT than with MSV, at a cost of five false-positive biopsy recommendations, with a mean of 1.8 true-positive (range, 0-3) and 1.3 false-positive (range, -1 to 4) assessments per reader.In this small study, mass characterization in terms of visibility ratings, reader performance, and BI-RADS assessment with DBT was similar to that with MSVs. Preliminary findings suggest that MSV might not be necessary for mass characterization when performing DBT.

The purpose of this article is to determine the accuracy of mammography and sonography in evaluating pregnant, lactating, and postpartum women.We retrospectively reviewed diagnostic breast imaging examinations of 155 pregnant, lactating, and postpartum women with 164 lesions presenting to our breast imaging department from 2004 to 2005. Records were reviewed for clinical presentation, reported sonographic or mammographic findings with BI-RADS assessment, histologic results, and clinical outcomes. Examinations rated as BI-RADS categories 4 and 5 were considered positive. One hundred thirty-four (82%) of 164 lesions had pathology results available or longer than 12 months follow-up in our study group. Of these lesions, 12 (9%) were evaluated by mammography alone, 49 (37%) were evaluated by ultrasound alone, and 73 (54%) were evaluated by both techniques.Of 134 lesions, 87 (65%) were in patients who presented during lactation, 34 (25%) who presented during pregnancy, and 13 (10%) who presented postpartum. The presenting symptom for 86 lesions (64%) was a palpable mass. Biopsies were performed for 40 lesions. Of these lesions, four were malignant and 36 were benign. Mammograms were dense or heterogeneously dense in 88% of patients. All four malignancies were BI-RADS category 4 or 5 according to both mammography and ultrasound. For the 85 lesions evaluated with mammography, there was 100% sensitivity, 93% specificity, 40% positive predictive value, and 100% negative predictive value. For the 122 lesions evaluated with sonography, there was 100% sensitivity, 86% specificity, 19% positive predictive value, and 100% negative predictive value.Among lactating and pregnant women, both mammography and sonography had a negative predictive value of 100% and accurately revealed the few cancers that were present in our study group.

The purpose of this study is to develop a computer-aided detection (CAD) system that combined a dual system approach with a two-view fusion method to improve the accuracy of mass detection on mammograms.The authors previously developed a dual CAD system that merged the decision from two mass detection systems in parallel, one trained with average masses and another trained with subtle masses, to improve sensitivity without excessively increasing false positives (FPs). In this study, they further designed a two-view fusion method to combine the information from different mammographic views. Mass candidates detected independently by the dual system on the two-view mammograms were first identified as potential pairs based on a regional registration technique. A similarity measure was designed to differentiate TP-TP pairs from other pairs (TP-FP and FP-FP pairs) using paired morphological features, Hessian feature, and texture features. A two-view fusion score for each object was generated by weighting the similarity measure with the cross correlation measure of the object pair. Finally, a linear discriminant analysis classifier was trained to combine the mass likelihood score of the object from the single-view dual system and the two-view fusion score for classification of masses and FPs. A total of 2332 mammograms from 735 subjects including 800 normal mammograms from 200 normal subjects was collected with Institutional Review Board (IRB) approval.When the single-view CAD system that was trained with average masses only were applied to the test sets, the average case-based sensitivities were 50.6% and 63.6% for average masses on current mammograms and 22.6% and 36.2% for subtle masses on prior mammograms at 0.5 and 1 FPs/image, respectively. With the new two-view dual system approach, the average case-based sensitivities were improved to 67.4% and 83.7% for average masses and 44.8% and 57.0% for subtle masses at the same FP rates.The improvement with the proposed method was found to be statistically significant (p0.0001) by JAFROC analysis.

The purpose of this study was to evaluate the potential for use of image volume based registration (IVBaR) to aid in measurement of changes in the tumor during chemotherapy of breast cancer. Successful IVBaR could aid in the detection of such changes in response to neoadjuvant chemotherapy and potentially be useful for routine breast cancer screening and diagnosis. IVBaR was employed in a new method of automated estimation of tumor volume in studies following the radiologist identification of the tumor region in the prechemotherapy scan. The authors have also introduced a new semiautomated method for validation of registration based on Doppler ultrasound (U.S.) signals that are independent of the grayscale signals used for registration. This Institutional Review Board approved study was conducted on 10 patients undergoing chemotherapy and 14 patients with a suspicious/unknown mass scheduled to undergo biopsy. Reasonably reproducible mammographic positioning and nearly whole breast U.S. imaging were achieved. The image volume was registered offline with a mutual information cost function and global interpolation based on a thin-plate spline using MIAMI FUSE software developed at the University of Michigan. The success and accuracy of registration of the three dimensional (3D) U.S. image volume were measured by means of mean registration error (MRE). IVBaR was successful with MRE of 4.3 +/- 1.7 mm in 9 out of 10 reproducibility automated breast ultrasound (ABU) studies and in 12 out of 17 ABU image pairs collected before, during, or after 115 +/- 14 days of chemotherapy. Semiautomated tumor volume estimation was performed on registered image volumes giving 86 +/- 8% mean accuracy compared to the radiologist hand-segmented tumor volume on seven cases. Doppler studies yielded fractional volume of color pixels in the region surrounding the lesion and its change with changing breast compression. The Doppler study of patients with detectable blood flow included five patients with suspicious masses and three undergoing chemotherapy. Spatial alignment of the 3D blood vessel data from the Doppler studies provided independent measures for the validation of registration. In 15 Doppler image volume pairs scanned with differing breast compression, the mean centerline separation value was 1.5 +/- 0.6 mm, while MRE based on a few identifiable structural points common to the two grayscale image volumes was 1.1 +/- 0.6 mm. Another measure, the overlap ratio of blood vessels, was shown to increase from 0.32 to 0.59 (+84%) with IVBaR for pairs at various compression levels. These results show that successful registration of ABU scans may be accomplished for comparison and integration of information.

Rationale and Objectives To investigate the effect of a computer-aided diagnosis (CADx) system on radiologists' performance in discriminating malignant and benign masses on mammograms and three-dimensional (3D) ultrasound (US) images. Materials and Methods Our dataset contained mammograms and 3D US volumes from 67 women (median age, 51; range: 27–86) with 67 biopsy-proven breast masses (32 benign and 35 malignant). A CADx system was designed to automatically delineate the mass boundaries on mammograms and the US volumes, extract features, and merge the extracted features into a multi-modality malignancy score. Ten experienced readers (subspecialty academic breast imaging radiologists) first viewed the mammograms alone, and provided likelihood of malignancy (LM) ratings and Breast Imaging and Reporting System assessments. Subsequently, the reader viewed the US images with the mammograms, and provided LM and action category ratings. Finally, the CADx score was shown and the reader had the opportunity to revise the ratings. The LM ratings were analyzed using receiver-operating characteristic (ROC) methodology, and the action category ratings were used to determine the sensitivity and specificity of cancer diagnosis. Results Without CADx, readers' average area under the ROC curve, A z , was 0.93 (range, 0.86–0.96) for combined assessment of the mass on both the US volume and mammograms. With CADx, their average A z increased to 0.95 (range, 0.91–0.98), which was borderline significant ( P = .05). The average sensitivity of the readers increased from 98% to 99% with CADx, while the average specificity increased from 27% to 29%. The change in sensitivity with CADx did not achieve statistical significance for the individual radiologists, and the change in specificity was statistically significant for one of the radiologists. Conclusions A well-trained CADx system that combines features extracted from mammograms and US images may have the potential to improve radiologists' performance in distinguishing malignant from benign breast masses and making decisions about biopsies.

Background Racial misclassification of American Indian and Alaska Native (AI/AN) individuals as non-AI/AN in cancer registries presents problems for cancer surveillance, research, and public health practice. The aim of this study was to investigate the efficiency of tribal linkages in enhancing the quality of racial information in state cancer registries. Methods Registry Plus™ Link Plus 2.0 probabilistic record linkage software was used to link the Michigan state cancer registry data (1985–2004; 1,031,168 cancer cases) to the tribal membership roster (40,340 individuals) in July of 2007. A data set was created containing AI/AN cancer cases identified by the state registry, Indian Health Service (IHS) linkages, and tribal linkage. The differences between these three groups of individuals were compared by distribution of demographic, diagnostic, and county-level characteristics using multilevel analysis (conducted in 2007–2008). Results From 1995 to 2004, the tribal enrollment file showed linkages to 670 cancer cases (583 individuals) and the tribal linkage led to the identification of 190 AI/AN cancer cases (168 individuals) that were classified as non-AI/AN in the registry. More than 80% of tribal members were reported as non-AI/AN to the registry. Individuals identified by IHS or tribal linkages were different from those reported to be AI/AN in terms of stage at diagnosis, tumor confirmation, and characteristics of the county of diagnosis, including contract health services availability, tribal health services availability, and proportion of AI/AN residents. Conclusions The data linkage between tribal and state cancer registry data sets improved racial classification validity of AI/AN Michigan cancer cases. Assessing tribal linkages is a simple, noninvasive way to improve the accuracy of state cancer data for AI/AN populations and to generate tribe-specific cancer information.

OBJECTIVE. The purpose of this study was to achieve 3D registration of digital tomosynthesis mammographic volumes using mutual information.CONCLUSION. Registration of digital breast tomosynthesis mammographic volumes was achieved with an average error of 1.8 ± 1.4 mm.

Objectives: Although rates for first-time and recent mammography screening have increased for women in the US in the past decade, rates for repeat mammography remain low. This study aimed to conduct an analysis of women's mammography experience, to examine the rates of repeat mammography and to identify the significant predictors of repeat mammography within 12 and 18 months of the index mammogram. Methods: Participants were 397 women obtaining a screening mammogram (i.e. index) at three university-affiliated radiology clinics. Following the index mammogram, women completed the measures assessing demographic background, health history, breast cancer knowledge, risk, and screening history, and aspects of the mammography experience. Eighteen months following the index mammogram, 296 women were contacted via telephone to assess repeat mammography behavior. Results: Factor analysis of a mammography experience survey yielded four major components including satisfaction with clinic services, physical experience, psychological experience, and communication with clinic staff. Twelve-month and 18-month repeat mammography rates were 37 and 68%, respectively. Logistic regression models found lifetime number of mammograms to predict repeat mammography at 12 and 18 months. In addition, the number of clinical breast exams obtained in the past 5 years predicted repeat mammography at 12 months, while having scheduled a mammography appointment predicted repeat mammography at 18 months. Conclusions: Based on these findings, strategies to increase mammography adherence include implementing a formal reminder system that prompts patients (e.g. postcard, automated telephone call) to schedule an annual mammogram or training clinic staff to automatically schedule an annual mammogram at the time of the current screening appointment. Copyright © 2008 John Wiley & Sons, Ltd.