Emily F. Conant

Breast Cancer Screening Using Tomosynthesis in Combination With Digital Mammography

IMPORTANCE Mammography plays a key role in early breast cancer detection. Single-institution studies have shown that adding tomosynthesis to mammography increases cancer detection and reduces false-positive results. OBJECTIVE To determine if mammography combined with tomosynthesis is associated with better performance of breast screening programs in the United States. DESIGN, SETTING, AND PARTICIPANTS Retrospective analysis of screening performance metrics from 13 academic and nonacademic breast centers using mixed models adjusting for site as a random effect. EXPOSURES Period 1: digital mammography screening examinations 1 year before tomosynthesis implementation (start dates ranged from March 2010 to October 2011 through the date of tomosynthesis implementation); period 2: digital mammography plus tomosynthesis examinations from initiation of tomosynthesis screening (March 2011 to October 2012) through December 31, 2012. MAIN OUTCOMES AND MEASURES Recall rate for additional imaging, cancer detection rate, and positive predictive values for recall and for biopsy. RESULTS A total of 454,850 examinations (n=281,187 digital mammography; n=173,663 digital mammography + tomosynthesis) were evaluated. With digital mammography, 29,726 patients were recalled and 5056 biopsies resulted in cancer diagnosis in 1207 patients (n=815 invasive; n=392 in situ). With digital mammography + tomosynthesis, 15,541 patients were recalled and 3285 biopsies resulted in cancer diagnosis in 950 patients (n=707 invasive; n=243 in situ). Model-adjusted rates per 1000 screens were as follows: for recall rate, 107 (95% CI, 89-124) with digital mammography vs 91 (95% CI, 73-108) with digital mammography + tomosynthesis; difference, -16 (95% CI, -18 to -14; P < .001); for biopsies, 18.1 (95% CI, 15.4-20.8) with digital mammography vs 19.3 (95% CI, 16.6-22.1) with digital mammography + tomosynthesis; difference, 1.3 (95% CI, 0.4-2.1; P = .004); for cancer detection, 4.2 (95% CI, 3.8-4.7) with digital mammography vs 5.4 (95% CI, 4.9-6.0) with digital mammography + tomosynthesis; difference, 1.2 (95% CI, 0.8-1.6; P < .001); and for invasive cancer detection, 2.9 (95% CI, 2.5-3.2) with digital mammography vs 4.1 (95% CI, 3.7-4.5) with digital mammography + tomosynthesis; difference, 1.2 (95% CI, 0.8-1.6; P < .001). The in situ cancer detection rate was 1.4 (95% CI, 1.2-1.6) per 1000 screens with both methods. Adding tomosynthesis was associated with an increase in the positive predictive value for recall from 4.3% to 6.4% (difference, 2.1%; 95% CI, 1.7%-2.5%; P < .001) and for biopsy from 24.2% to 29.2% (difference, 5.0%; 95% CI, 3.0%-7.0%; P < .001). CONCLUSIONS AND RELEVANCE Addition of tomosynthesis to digital mammography was associated with a decrease in recall rate and an increase in cancer detection rate. Further studies are needed to assess the relationship to clinical outcomes.

Diagnostic Accuracy of Digital versus Film Mammography: Exploratory Analysis of Selected Population Subgroups in DMIST

PURPOSE To retrospectively compare the accuracy of digital versus film mammography in population subgroups of the Digital Mammographic Imaging Screening Trial (DMIST) defined by combinations of age, menopausal status, and breast density, by using either biopsy results or follow-up information as the reference standard. MATERIALS AND METHODS DMIST included women who underwent both digital and film screening mammography. Institutional review board approval at all participating sites and informed consent from all participating women in compliance with HIPAA was obtained for DMIST and this retrospective analysis. Areas under the receiver operating characteristic curve (AUCs) for each modality were compared within each subgroup evaluated (age < 50 vs 50-64 vs >or= 65 years, dense vs nondense breasts at mammography, and pre- or perimenopausal vs postmenopausal status for the two younger age cohorts [10 new subgroups in toto]) while controlling for multiple comparisons (P < .002 indicated a significant difference). All DMIST cancers were evaluated with respect to mammographic detection method (digital vs film vs both vs neither), mammographic lesion type (mass, calcifications, or other), digital machine type, mammographic and pathologic size and diagnosis, existence of prior mammographic study at time of interpretation, months since prior mammographic study, and compressed breast thickness. RESULTS Thirty-three centers enrolled 49 528 women. Breast cancer status was determined for 42,760 women, the group included in this study. Pre- or perimenopausal women younger than 50 years who had dense breasts at film mammography comprised the only subgroup for which digital mammography was significantly better than film (AUCs, 0.79 vs 0.54; P = .0015). Breast Imaging Reporting and Data System-based sensitivity in this subgroup was 0.59 for digital and 0.27 for film mammography. AUCs were not significantly different in any of the other subgroups. For women aged 65 years or older with fatty breasts, the AUC showed a nonsignificant tendency toward film being better than digital mammography (AUCs, 0.88 vs 0.70; P = .0025). CONCLUSION Digital mammography performed significantly better than film for pre- and perimenopausal women younger than 50 years with dense breasts, but film tended nonsignificantly to perform better for women aged 65 years or older with fatty breasts.

Immunohistochemistry with pancytokeratins improves the sensitivity of sentinel lymph node biopsy in patients with breast carcinoma

Sentinel lymph node (SLN) biopsy is being investigated as a staging procedure for breast carcinoma. The authors evaluated whether immunohistochemical (IHC) analysis improves the sensitivity of this procedure.

How experience and training influence mammography expertise

RATIONALE AND OBJECTIVES The authors evaluated the influence of perceptual and cognitive skills in mammography detection and interpretation by testing three groups representing different levels of mammography expertise in terms of experience, training, and talent with a mammography screening-diagnostic task. MATERIALS AND METHODS One hundred fifty mammograms, composed of unilateral cranial-caudal and mediolateral oblique views, were displayed in pairs on a digital workstation to 19 radiology residents, three experienced mammographers, and nine mammography technologists. One-third of the mammograms showed malignant lesions; two-thirds were malignancy-free. Observers interacted with the display to indicate whether each image contained no malignant lesions or suspicious lesions indicating malignancy. Decision time was measured as the lesions were localized, classified, and rated for decision confidence. RESULTS Compared with performance of experts, alternative free response operating characteristic performance for residents was significantly lower and equivalent to that of technologists. Analysis of overall performance showed that, as level of expertise decreased, false-positive results exerted a greater effect on overall decision accuracy over the time course of image perception. This defines the decision speed-accuracy relationship that characterizes mammography expertise. CONCLUSION Differences in resident performance resulted primarily from lack of perceptual-learning experience during mammography training, which limited object recognition skills and made it difficult to determine differences between malignant lesions, benign lesions, and normal image perturbations. A proposed solution is systematic mentor-guided training that links image perception to feedback about the reasons underlying decision making.

Screening Outcomes Following Implementation of Digital Breast Tomosynthesis in a General-Population Screening Program

BACKGROUND Early data on breast cancer screening utilizing digital breast tomosynthesis (DBT) combined with digital mammography (DM) have shown improvements in false-positive and false-negative screening rates compared with DM alone. However, these trials were performed at sites where conventional mammographic screening was concurrently performed, possibly leading to selection biases or with complex, multireader algorithms not reflecting general clinical practice. Our study reports the impact on screening outcomes for DBT screening implemented in an entire clinic population. METHODS Recall rates, cancer detection, and positive predictive values of screening were compared for 15571 women screened with DBT and 10728 screened with DM alone prior to DBT implementation at a single breast imaging center. Generalized linear mixed-effects models were used to estimate the odds ratio (OR) for recall rate adjusted for age, race, presence of prior mammograms, breast density and reader. All statistical tests were two-sided. RESULTS DBT screening showed a statistically significant reduction in recalls compared to DM alone. For the entire population, there were 16 fewer recalls (8.8% vs 10.4%, P <.001, adjusted OR = 0.80, 95% confidence interval [CI] = 0.74 to 0.88, P < .001) and 0.9 additional cancers detected per 1000 screened with DBT compared to DM alone. There was a statistically significant increase in PPV1 (6.2% vs 4.4%, P = .047). In women younger than age 50 years screened with DBT, there were 17 fewer recalls (12.3% vs 14.0%, P = .02) and 3.6 additional cancer detected per 1000 screened (5.7 vs 2.2 per 1000, P = .02). CONCLUSIONS Our data support the clinical implementation of DBT in breast cancer screening; however, larger prospective trials are needed to validate our findings in specific patient subgroups.

Breast tissue density quantification via digitized mammograms

Studies reported in the literature indicate that breast cancer risk is associated with mammographic densities. An objective, repeatable, and a quantitative measure of risk derived from mammographic densities will be of considerable use in recommending alternative screening paradigms and/or preventive measures. However, image processing efforts toward this goal seem to be sparse in the literature, and automatic and efficient methods do not seem to exist. Here, the authors describe and validate an automatic and reproducible method to segment dense tissue regions from fat within breasts from digitized mammograms using scale-based fuzzy connectivity methods. Different measures for characterizing mammographic density are computed from the segmented regions and their robustness in terms of their linear correlation across two different projections-cranio-caudal and medio-lateral-oblique-are studied. The accuracy of the method is studied by computing the area of mismatch of segmented dense regions using the proposed method and using manual outlining. A comparison between the mammographic density parameter taking into account the original intensities and that just considering the segmented area indicates that the former may have some advantages over the latter.

A Review of Breast Ultrasound

Frequent advances in transducer design, electronics, computers, and signal processing have improved the quality of ultrasound images to the extent that sonography is now a major mode of imaging for the clinical diagnosis of breast cancer. Breast ultrasound is routinely used for differentiating cysts and solid nodules with high specificity. In combination with mammography, ultrasound is used to characterize solid masses as benign or malignant. There is growing interest in using Doppler ultrasound and contrast agents for measuring tumor blood flow and for imaging tumor vascularity. Ease of use and real-time imaging capability make breast ultrasound a method of choice for guiding breast biopsies and other interventional procedures. Breast ultrasound is used in many forms. B-mode is the most common form of imaging for the breast, although compound imaging and harmonic imaging are being increasingly applied to better visualize breast lesions and to reduce image artifacts. These developments, together with the formulation of a standardized lexicon of solid mass features, have improved the diagnostic performance of breast ultrasound. Several approaches that are currently being investigated to further improve performance include: (1) computer-aided-diagnosis; (2) the assessment of tumor vascularity and tumor blood flow with Doppler ultrasound and contrast agents; and (3) tissue elasticity imaging. In the future, ultrasound will play a greater role in differentiating benign from malignant masses and in the diagnosis of breast cancer.

Quantitative Vascularity of Breast Masses by Doppler Imaging: Regional Variations and Diagnostic Implications

Seventy‐four biopsy proven breast masses were imaged by color and power Doppler imaging to evaluate vascular pattern of malignant and benign breast masses. The images were analyzed for vascularity. The measurements were made over the entire mass as well as regionally at its core, at its periphery, and in the tissue surrounding it. The surgical specimens were analyzed for microvessel density. The diagnostic performance of Doppler sonographic vascularity indices was evaluated by receiver operating characteristic analysis. The malignant masses were 14 to 54% more vascular than the benign masses. Both types of masses were more vascular by ultrasonography than the tissue surrounding them. Whereas benign masses were 2.2 times more vascular than the surrounding tissue, the malignant masses were 5.0 times more vascular. In a subset of patients the regional vascularity at the core, periphery, and surrounding tissue by Doppler imaging exhibited a strong correlation (R2 > 0.9) with the corresponding histologic microvessel density measurements. Although the malignant masses exhibited a strong gradient in vascularity, core > periphery > surrounding tissue, the benign masses had relatively uniform distribution of vascularity. The area under the receiver operating characteristic curve (A(Z)) for the Doppler indices ranged from 0.56 +/‐ 0.07 to 0.65 +/‐ 0.07. A nonlinear analysis including age‐specific values of Doppler indices improved the diagnostic performance to A(Z) = 0.85 +/‐ 0.06. In conclusion, quantitative Doppler imaging when used in combination with a nonlinear rule‐based approach has the potential for differentiating between malignant and benign masses.

Autologous fat grafting to the reconstructed breast: the management of acquired contour deformities.

Background: Autologous fat grafting has become a workhorse for soft-tissue augmentation throughout the body. In the reconstructed breast, autologous fat grafting is a useful tool for managing secondary contour deformities. The authors have categorized these deformities into three types: type 1 deformities are step-off deformities between the chest wall/reconstructed breast interface, type 2 deformities result from intrinsic deficiencies within a flap such as fat necrosis, and type 3 deformities are the result of extrinsic factors such as postoperative irradiation. Methods: The authors conducted a detailed retrospective review of 110 patients who have received fat grafting to the reconstructed breast for the management of contour deformities. In addition, the authors reviewed the recent literature describing the use of autologous fat grafting to the breast. Particular attention has been placed on the concerns of oncologic surveillance in reconstructed breasts that have undergone fat grafting. Results: The authors have had relative success in the treatment of patients who will require postoperative irradiation and even those who have rippling surrounding an implant. Conclusions: Autologous fat grafting represents an important tool for the management of secondary contour deformities of the reconstructed breast. Fat grafting is a simple, safe, and effective treatment option, with low morbidity.

Multimodality Screening of High-Risk Women: A Prospective Cohort Study

PURPOSE Mammography has been established as the primary imaging screening method for breast cancer; however, the sensitivity of mammography is limited, especially in women with dense breast tissue. Given the limitations of mammography, interest has developed in alternative screening techniques. This interest has led to numerous studies reporting mammographically occult breast cancers detected on magnetic resonance imaging (MRI) or ultrasound. In addition, digital mammography was shown to be more sensitive than film mammography in selected populations. Our goal was to prospectively compare cancer detection of digital mammography (DM), whole-breast ultrasound (WBUS), and contrast-enhanced MRI in a high-risk screening population previously screened negative by film screen mammogram (FSM). METHODS During a 2-year period, 609 asymptomatic high-risk women with nonactionable FSM examinations presented for a prospective multimodality screening consisting of DM, WBUS, and MRI. The FSM examinations were reinterpreted by study radiologists. Patients had benign or no suspicious findings on clinical examination. The cancer yield by modality was evaluated. RESULTS Twenty cancers were diagnosed in 18 patients (nine ductal carcinomas in situ and 11 invasive breast cancers). The overall cancer yield on a per-patient basis was 3.0% (18 of 609 patients). The cancer yield by modality was 1.0% for FSM (six of 597 women), 1.2% for DM (seven of 569 women), 0.53% for WBUS (three of 567 women), and 2.1% for MRI (12 of 571 women). Of the 20 cancers detected, some were only detected on one imaging modality (FSM, n = 1; DM, n = 3; WBUS, n = 1; and MRI, n = 8). CONCLUSION The addition of MRI to mammography in the high-risk group has the greatest potential to detect additional mammographically occult cancers. The incremental cancer yield of WBUS and DM is much less.