Screening the Implant-Augmented Breast with Digital Breast Tomosynthesis: Is Tomosynthesis Necessary for Non-implant-Displaced Views?

OBJECTIVE: To determine cancer visualization utility and radiation dose for non-implant-displaced (ID) views using standard protocol with digital breast tomosynthesis (DBT) vs alternative protocol with 2D only when screening women with implant augmentation. METHODS: This retrospective cohort study identified women with implants who underwent screening DBT examinations that had abnormal findings from July 28, 2014, to December 31, 2021. Three fellowship-trained breast radiologists independently reviewed examinations retrospectively to determine if the initially identified abnormalities could be visualized on standard protocol (DBT with synthesized 2D (S2D) for ID and non-ID views) and alternate protocol (DBT with S2D for ID and only the S2D images for non-ID views). Estimated exam average glandular dose (AGD) and associations between cancer visualization with patient and implant characteristics for both protocols were evaluated. RESULTS: The study included 195 patients (mean age 55 years ± 10) with 223 abnormal findings. Subsequent biopsy was performed for 86 abnormalities: 59 (69%) benign, 8 (9%) high risk, and 19 (22%) malignant. There was no significant difference in malignancy visualization rate between standard (19/223, 8.5%) and alternate (18/223, 8.1%) protocols (P = .92), but inclusion of the DBT for non-ID views found one additional malignancy. Total examination AGD using standard protocol (21.9 mGy ± 5.0) was significantly higher than it would be for estimated alternate protocol (12.6 mGy ± 5.0, P <.001). This remained true when stratified by breast thickness: 6.0-7.9 cm, 8.0-9.9 cm, >10.0 cm (all P <.001). CONCLUSION: The inclusion of DBT for non-ID views did not significantly increase the cancer visualization rate but did significantly increase overall examination AGD.

Feasibility of Prospective Assignment of Initial Method of Detection of Breast Cancer: A Multicenter Pilot Study.

OBJECTIVE: To determine the feasibility of standardized, prospective assignment of initial method of detection (MOD) of breast cancer by radiologists in diverse practice settings. METHODS: This multicenter, retrospective study analyzed the rate of assignment of MOD in four geographically varied health systems. A universal protocol for basic MOD assignment was agreed upon by the authors before start of the pilot study. Radiologists at each site were instructed how to assign MOD. Charts were then reviewed to determine the frequency and accuracy of MOD assignment for all cases subsequently diagnosed with breast cancer. When available, data regarding frequency of tumor registry abstraction were also reviewed for frequency and accuracy. RESULTS: A total of 2,328 patients with a new diagnosis of breast cancer were evaluated across the sites over the study period. Of these patients, initial MOD was prospectively assigned by the radiologist in 94% of cases. Of the cases in which MOD was assigned, retrospective review confirmed accurate assignment in 96% of cases. CONCLUSIONS: Prospective, standardized assignment of initial MOD of breast cancer is feasible across different practice sites and can be accurately captured in tumor registries. Standard collection of MOD would provide critical data about the impact of screening mammography in the United States.

Myeloid Sarcoma of the Breast: A Diagnostic Dilemma With Pathologic Correlation.

Myeloid sarcoma (MS) is a rare extramedullary solid tumor arising most often in patients with current or subsequent acute myeloid leukemia (AML). Patients of all ages may present with involvement of the skin, lymph nodes, intestinal tract, bone, and/or central nervous system. Isolated involvement of the breast is rare, and only a small number of cases have been described in the literature. Breast MS may present as a palpable mass on clinical evaluation. In this broad literature review from 2010 to 2022, the most common findings on mammography are either solitary or multiple masses, followed by architectural distortion and, less commonly, no discrete findings. Sonography may demonstrate hypoechoic or mixed echogenicity mass(es) with circumscribed or indistinct, not discrete margins. Myeloid sarcoma may present as an enhancing mass or nonmass enhancement on breast MRI and is typically moderately radiotracer avid on 18F-fluorodeoxyglucose-PET. At histopathology, MS is characterized by myeloid blasts in varying stages of granulocytic or neutrophilic maturation; diagnosis typically requires immunophenotyping. There is no consensus for treatment of MS, although systemic chemotherapy for AML is often used as MS is considered the tissue equivalent of AML. This article will discuss and illustrate imaging and pathology findings when the breast is involved by MS.

The Benefits of Early Detection: Evidence From Modern International Mammography Service Screening Programs.

Research from randomized controlled trials initiated up to 60 years ago consistently confirms that regular screening with mammography significantly reduces breast cancer mortality. Despite this success, there is ongoing debate regarding the efficacy of screening, which is confounded by technologic advances and concerns about cost, overdiagnosis, overtreatment, and equitable care of diverse patient populations. More recent screening research, designed to quell the debates, derives data from variable study designs, each with unique strengths and weaknesses. This article reviews observational population-based screening research that has followed the early initial long-term randomized controlled trials that are no longer practical or ethical to perform. The advantages and disadvantages of observational data and study design are outlined, including the three subtypes of population-based observational studies: cohort/case-control, trend, and incidence-based mortality/staging. The most recent research, typically performed in countries that administer screening mammography to women through centralized health service programs and directly track patient-specific outcomes and detection data, is summarized. These data are essential to understand and inform construction of effective new databases that facilitate continuous assessment of optimal screening techniques in the current era of rapidly developing medical technology, combined with a focus on health care that is both personal and equitable.

A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images.

Importance: Breast cancer screening is among the most common radiological tasks, with more than 39 million examinations performed each year. While it has been among the most studied medical imaging applications of artificial intelligence, the development and evaluation of algorithms are hindered by the lack of well-annotated, large-scale publicly available data sets. Objectives: To curate, annotate, and make publicly available a large-scale data set of digital breast tomosynthesis (DBT) images to facilitate the development and evaluation of artificial intelligence algorithms for breast cancer screening; to develop a baseline deep learning model for breast cancer detection; and to test this model using the data set to serve as a baseline for future research. Design, Setting, and Participants: In this diagnostic study, 16 802 DBT examinations with at least 1 reconstruction view available, performed between August 26, 2014, and January 29, 2018, were obtained from Duke Health System and analyzed. From the initial cohort, examinations were divided into 4 groups and split into training and test sets for the development and evaluation of a deep learning model. Images with foreign objects or spot compression views were excluded. Data analysis was conducted from January 2018 to October 2020. Exposures: Screening DBT. Main Outcomes and Measures: The detection algorithm was evaluated with breast-based free-response receiver operating characteristic curve and sensitivity at 2 false positives per volume. Results: The curated data set contained 22 032 reconstructed DBT volumes that belonged to 5610 studies from 5060 patients with a mean (SD) age of 55 (11) years and 5059 (100.0%) women. This included 4 groups of studies: (1) 5129 (91.4%) normal studies; (2) 280 (5.0%) actionable studies, for which where additional imaging was needed but no biopsy was performed; (3) 112 (2.0%) benign biopsied studies; and (4) 89 studies (1.6%) with cancer. Our data set included masses and architectural distortions that were annotated by 2 experienced radiologists. Our deep learning model reached breast-based sensitivity of 65% (39 of 60; 95% CI, 56%-74%) at 2 false positives per DBT volume on a test set of 460 examinations from 418 patients. Conclusions and Relevance: The large, diverse, and curated data set presented in this study could facilitate the development and evaluation of artificial intelligence algorithms for breast cancer screening by providing data for training as well as a common set of cases for model validation. The performance of the model developed in this study showed that the task remains challenging; its performance could serve as a baseline for future model development.

Mixed-Methods Study to Predict Upstaging of DCIS to Invasive Disease on Mammography.

BACKGROUND. The incidence of ductal carcinoma in situ (DCIS) has steadily increased, as have concerns regarding overtreatment. Active surveillance is a novel treatment strategy that avoids surgical excision, but identifying patients with occult invasive disease who should be excluded from active surveillance is challenging. Radiologists are not typically expected to predict the upstaging of DCIS to invasive disease, though they might be trained to perform this task. OBJECTIVE. The purpose of this study was to determine whether a mixed-methods two-stage observer study can improve radiologists' ability to predict upstaging of DCIS to invasive disease on mammography. METHODS. All cases of DCIS calcifications that underwent stereotactic biopsy between 2010 and 2015 were identified. Two cohorts were randomly generated, each containing 150 cases (120 pure DCIS cases and 30 DCIS cases upstaged to invasive disease at surgery). Nine breast radiologists reviewed the mammograms in the first cohort in a blinded fashion and scored the probability of upstaging to invasive disease. The radiologists then reviewed the cases and results collectively in a focus group to develop consensus criteria that could improve their ability to predict upstaging. The radiologists reviewed the mammograms from the second cohort in a blinded fashion and again scored the probability of upstaging. Statistical analysis compared the performances between rounds 1 and 2. RESULTS. The mean AUC for reader performance in predicting upstaging in round 1 was 0.623 (range, 0.514-0.684). In the focus group, radiologists agreed that upstaging was better predicted when an associated mass, asymmetry, or architectural distortion was present; when densely packed calcifications extended over a larger area; and when the most suspicious features were focused on rather than the most common features. Additionally, radiologists agreed that BI-RADS descriptors do not adequately characterize risk of invasion, and that microinvasive disease and smaller areas of DCIS will have poor prediction estimates. Reader performance significantly improved in round 2 (mean AUC, 0.765; range, 0.617-0.852; p = .045). CONCLUSION. A mixed-methods two-stage observer study identified factors that helped radiologists significantly improve their ability to predict upstaging of DCIS to invasive disease. CLINICAL IMPACT. Breast radiologists can be trained to better predict upstaging of DCIS to invasive disease, which may facilitate discussions with patients and referring providers.

Mapping the Ultrasound Landscape to Define Point-of-Care Ultrasound and Diagnostic Ultrasound: A Proposal From the Society of Radiologists in Ultrasound and ACR Commission on Ultrasound.

Current descriptions of ultrasound evaluations, including use of the term "point-of-care ultrasound" (POCUS), are imprecise because they are predicated on distinctions based on the device used to obtain images, the location where the images were obtained, the provider who obtained the images, or the focus of the examination. This is confusing because it does not account for more meaningful distinctions based on the setting, comprehensiveness, and completeness of the evaluation. In this article, the Society of Radiologists in Ultrasound and the members of the American College of Radiology Ultrasound Commission articulate a map of the ultrasound landscape that divides sonographic evaluations into four distinct categories on the basis of setting, comprehensiveness, and completeness. Details of this classification scheme are elaborated, including important clarifications regarding what ensures comprehensiveness and completeness. Practical implications of this framework for future research and reimbursement paradigms are highlighted.

Abnormalities of the Fetal Central Nervous System: Prenatal US Diagnosis with Postnatal Correlation.

Fetal central nervous system (CNS) abnormalities are second only to cardiac malformations in their frequency of occurrence. Early and accurate diagnosis at prenatal US is therefore essential, allowing improved prenatal counseling and facilitating appropriate referral. Thorough knowledge of normal intracranial anatomy and adoption of a logical sonographic approach can improve depiction of abnormal findings, leading to a more accurate differential diagnosis earlier in pregnancy. Four standard recommended views-transventricular, falx, cavum, and posterior fossa or transcerebellar views-provide an overview of fetal intracranial anatomy during the second trimester anatomy scan. Essential elements surveyed in the head and neck include the lateral cerebral ventricles, choroid plexus, midline falx, cavum septi pellucidi, cerebellum, cisterna magna, upper lip, and nuchal fold. CNS abnormalities can be organized into six main categories at prenatal US. Developmental anomalies include neural tube defects and neuronal migration disorders. Posterior fossa disorders include Dandy-Walker malformation variants and Chiari II malformation. Ventricular anomalies include aqueductal stenosis. Midline disorders include those on the spectrum of holoprosencephaly, agenesis of the corpus callosum, and septo-optic dysplasia. Vascular anomalies include vein of Galen malformations. Miscellaneous disorders include hydranencephaly, porencephaly, tumors, and intracranial hemorrhage. Correlation with postnatal MRI is helpful for confirmation and clarification of suspected diagnoses after birth. The authors discuss a standard US imaging approach to the fetal CNS and review cases in all categories of CNS malformations, providing postnatal MRI correlation when available.©RSNA, 2020.

Tattoo pigment mimicking axillary lymph node calcifications on mammography.

Axillary lymph nodes can appear abnormal on mammography due to uptake of tattoo pigment and a malignant cause must be excluded through diagnostic workup. Furthermore, tattoo pigment can mimic malignant pathology at surgery or confound appropriate staging of breast cancer. We present the case of a 47-year-old female with left axillary lymph nodes demonstrating new coarse densities suspicious for malignant calcifications on screening mammogram. Stereotactic guided biopsy was performed which demonstrated pigment from a recent tattoo located on the patient's back and/or flank. Awareness of current or prior tattoos in a patient is helpful to properly manage such cases.

Predicting Upstaging of DCIS to Invasive Disease: Radiologists's Predictive Performance.

RATIONALE AND OBJECTIVES: The purpose of this study is to quantify breast radiologists' performance at predicting occult invasive disease when ductal carcinoma in situ (DCIS) presents as calcifications on mammography and to identify imaging and histopathological features that are associated with radiologists' performance. MATERIALS AND METHODS: Mammographically detected calcifications that were initially diagnosed as DCIS on core biopsy and underwent definitive surgical excision between 2010 and 2015 were identified. Thirty cases of suspicious calcifications upstaged to invasive ductal carcinoma and 120 cases of DCIS confirmed at the time of definitive surgery were randomly selected. Nuclear grade, estrogen and progesterone receptor status, patient age, calcification long axis length, and breast density were collected. Ten breast radiologists who were blinded to all clinical and pathology data independently reviewed all cases and estimated the likelihood that the DCIS would be upstaged to invasive disease at surgical excision. Subgroup analysis was performed based on nuclear grade, long axis length, breast density and after exclusion of microinvasive disease. RESULTS: Reader performance to predict upstaging ranged from an area under the receiver operating characteristic curve (AUC) of 0.541-0.684 with a mean AUC of 0.620 (95%CI: 0.489-0.751). Performances improved for lesions smaller than 2 cm (AUC: 0.676 vs 0.500; p = 0.002). The exclusion of microinvasive cases also improved performance (AUC: 0.651 vs 0.620; p = 0.005). There was no difference in performance based on breast density (p = 0.850) or nuclear grade (p = 0.270) CONCLUSION: Radiologists were able to predict invasive disease better than chance, particularly for smaller DCIS lesions (<2 cm) and after the exclusion of microinvasive disease.

MRI predictors of tumor-positive margins after breast-conserving surgery.

PURPOSE: The purpose of this study is to identify predictors of tumor-positive surgical margins after breast-conserving surgery on dynamic contrast-enhanced (DCE) MRI. MATERIALS AND METHODS: We conducted a retrospective study of consecutive women who underwent DCE MRI before breast-conserving surgery from 2005 to 2014. Patient demographics, indication for surgery, MRI findings, biopsy pathology results, and surgical outcomes were reviewed. The unpaired t-test and chi-square test were used to compare the positive and negative margins groups. RESULTS: 554 women (mean age, 56; range, 26-90) underwent DCE MRI before 575 breast-conserving surgeries for invasive carcinoma (n = 473) or ductal carcinoma in situ (DCIS) (n = 102). Positive margins requiring re-excision occurred in 19.7% (93/473) of surgeries for invasive carcinoma and 31.4% (32/102) of surgeries for DCIS. For invasive carcinoma surgeries, positive margins were more common when MRI demonstrated the finding of non-mass enhancement (NME) rather than the finding of enhancing mass (33.8% [22/65] versus 16.9% [61/360], p < 0.01). Tumor size on MRI was significantly larger in the positive margins group (2.5 cm versus 1.9 cm, p < 0.001). Positive margins were more common with invasive lobular rather than invasive ductal histology at core biopsy (38.3% [18/47] versus 16.0% [56/350], p < 0.001). For DCIS surgeries, there were no significant differences in positive margin rates related to MRI features. CONCLUSION: For invasive carcinoma surgeries, positive margins are associated with NME on MRI, larger tumor size on MRI, and lobular histology at core biopsy. These findings may be used to predict which patients are at risk for positive margins after breast-conserving surgery.

Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.

BACKGROUND: Preliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a more precise and reproducible means of measuring BPE than human readers and thus might improve the predictive performance of future cancer development. PURPOSE: To determine if algorithmically extracted imaging features of BPE on screening breast MRI in high-risk women are associated with subsequent development of cancer. STUDY TYPE: Case-control study. POPULATION: In all, 133 women at high risk for developing breast cancer; 46 of these patients developed breast cancer subsequently over a follow-up period of 2 years. FIELD STRENGTH/SEQUENCE: 5 T or 3.0 T T1 -weighted precontrast fat-saturated and nonfat-saturated sequences and postcontrast nonfat-saturated sequences. ASSESSMENT: Automatic features of BPE were extracted with a computer algorithm. Subjective BPE scores from five breast radiologists (blinded to clinical outcomes) were also available. STATISTICAL TESTS: Leave-one-out crossvalidation for a multivariate logistic regression model developed using the automatic features and receiver operating characteristic (ROC) analysis were performed to calculate the area under the curve (AUC). Comparison of automatic features and subjective features was performed using a generalized regression model and the P-value was obtained. Odds ratios for automatic and subjective features were compared. RESULTS: The multivariate model discriminated patients who developed cancer from the patients who did not, with an AUC of 0.70 (95% confidence interval: 0.60-0.79, P < 0.001). The imaging features remained independently predictive of subsequent development of cancer (P < 0.003) when compared with the subjective BPE assessment of the readers. DATA CONCLUSION: Automatically extracted BPE measurements may potentially be used to further stratify risk in patients undergoing high-risk screening MRI. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:456-464.

Relationship between Background Parenchymal Enhancement on High-risk Screening MRI and Future Breast Cancer Risk.

RATIONALE AND OBJECTIVES: To determine if background parenchymal enhancement (BPE) on screening breast magnetic resonance imaging (MRI) in high-risk women correlates with future cancer. MATERIALS AND METHODS: All screening breast MRIs (n = 1039) in high-risk women at our institution from August 1, 2004, to July 30, 2013, were identified. Sixty-one patients who subsequently developed breast cancer were matched 1:2 by age and high-risk indication with patients who did not develop breast cancer (n = 122). Five fellowship-trained breast radiologists independently recorded the BPE. The median reader BPE for each case was calculated and compared between the cancer and control cohorts. RESULTS: Cancer cohort patients were high-risk because of a history of radiation therapy (10%, 6 of 61), high-risk lesion (18%, 11 of 61), or breast cancer (30%, 18 of 61); BRCA mutation (18%, 11 of 61); or family history (25%, 15 of 61). Subsequent malignancies were invasive ductal carcinoma (64%, 39 of 61), ductal carcinoma in situ (30%, 18 of 61) and invasive lobular carcinoma (7%, 4of 61). BPE was significantly higher in the cancer cohort than in the control cohort (P = 0.01). Women with mild, moderate, or marked BPE were 2.5 times more likely to develop breast cancer than women with minimal BPE (odds ratio = 2.5, 95% confidence interval: 1.3-4.8, P = .005). There was fair interreader agreement (κ = 0.39). CONCLUSIONS: High-risk women with greater than minimal BPE at screening MRI have increased risk of future breast cancer.

Solitary, Well-Circumscribed, T2 Hyperintense Masses on MRI Have Very Low Malignancy Rates.

OBJECTIVE: The purpose of this study was to determine the malignancy rate of solitary MRI masses with benign BI-RADS descriptors. METHODS: A retrospective review was conducted of all breast MRI reports that described a mass with a final BI-RADS assessment of 3, 4, or 5, from February 1, 2005, through February 28, 2014 (n = 1510). Studies were excluded if the mass was not solitary, did not meet formal criteria for a mass, or had classically suspicious BI-RADS features (e.g., washout kinetics, and spiculated margin). The masses were reviewed by 2 fellowship-trained breast radiologists who reported consensus BI-RADS mass margin, shape, internal-enhancement, and kinetics descriptors. The T2 signal was reported as hyperintense if equal to or greater than the signal intensity of the axillary lymph nodes. Pathology results or 2 years of imaging follow-up were recorded. Comparisons were made between mass descriptors and clinical outcomes. RESULTS: There were 127 women with 127 masses available for analysis. There were 76 (60%) masses that underwent biopsy for an overall malignancy rate of 4% (5/127): 2 ductal carcinoma in situ (DCIS) and 3 invasive ductal carcinoma. The malignancy rate was 2% (1/59) for T2 hyperintense solitary masses. The malignancy rate was greater than 2% for all of the following BI-RADS descriptors: oval (3%, 3/88), round (5%, 2/39), circumscribed (4%, 5/127), homogeneous (4%, 3/74), and dark internal septations (4%, 2/44). CONCLUSION: T2 hyperintense solitary masses without associated suspicious features have a low malignancy rate, and they could be considered for a BI-RADS 3 final assessment.

A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.

BACKGROUND: Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship. METHODS: We analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as well as a tumour proliferation marker (Ki-67). Trained models were evaluated on the set of the remaining 461 patients. RESULTS: Multivariate models were predictive of Luminal A subtype with AUC = 0.697 (95% CI: 0.647-0.746, p < .0001), triple negative breast cancer with AUC = 0.654 (95% CI: 0.589-0.727, p < .0001), ER status with AUC = 0.649 (95% CI: 0.591-0.705, p < .001), and PR status with AUC = 0.622 (95% CI: 0.569-0.674, p < .0001). Associations between individual features and subtypes we also found. CONCLUSIONS: There is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features.

Screening mammography for average-risk women.

This Practice Pearl describes an approach to screening mammography for average-risk women that encourages the use of shared decision-making that addresses benefits (early diagnosis and decreased mortality) and potential harms (false positives and overdiagnosis/overtreatment) in determining screening mammography initiation, frequency, and duration for women at average risk of breast cancer.

Imaging Features of Patients Undergoing Active Surveillance for Ductal Carcinoma in Situ.

RATIONALE AND OBJECTIVES: The aim of this study was to describe the imaging appearance of patients undergoing active surveillance for ductal carcinoma in situ (DCIS). MATERIALS AND METHODS: We retrospectively identified 29 patients undergoing active surveillance for DCIS from 2009 to 2014. Twenty-two patients (group 1) refused surgery or were not surgical candidates. Seven patients (group 2) enrolled in a trial of letrozole and deferred surgical excision for 6-12 months. Pathology and imaging results at the initial biopsy and follow-up were recorded. RESULTS: In group 1, the median follow-up was 2.7 years (range: 0.6-13.9 years). Fifteen patients (68%) remained stable. Seven patients (32%) underwent additional biopsies with invasive ductal carcinoma diagnosed in two patients after 3.9 and 3.6 years who developed increasing calcifications and new masses. In group 2, one patient (14%) was upstaged to microinvasive ductal carcinoma at surgery. Among the patients in both groups with calcifications (n = 26), there was no progression to invasive disease among those with stable (50%, 13/26) or decreased (19%, 5/26) calcifications. CONCLUSIONS: Among a DCIS active surveillance cohort, invasive disease progression presented as increasing calcifications and a new mass following more than 3.5 years of stable imaging. In contrast, there was no progression to invasive disease among cases of DCIS with stable or decreasing calcifications. Close imaging is a key follow-up component in active surveillance.

Suspicious breast calcifications undergoing stereotactic biopsy in women ages 70 and over: Breast cancer incidence by BI-RADS descriptors.

OBJECTIVES: To determine the malignancy rate overall and for specific BI-RADS descriptors in women ≥70 years who undergo stereotactic biopsy for calcifications. METHODS: We retrospectively reviewed 14,577 consecutive mammogram reports in 6839 women ≥70 years to collect 231 stereotactic biopsies of calcifications in 215 women. Cases with missing images or histopathology and calcifications associated with masses, distortion, or asymmetries were excluded. Three breast radiologists determined BI-RADS descriptors by majority. Histology, hormone receptor status, and lymph node status were correlated with BI-RADS descriptors. RESULTS: There were 131 (57 %) benign, 22 (10 %) atypia/lobular carcinomas in situ, 55 (24 %) ductal carcinomas in situ (DCIS), and 23 (10 %) invasive diagnoses. Twenty-seven (51 %) DCIS cases were high-grade. Five (22 %) invasive cases were high-grade, two (9 %) were triple-negative, and three (12 %) were node-positive. Malignancy was found in 49 % (50/103) of fine pleomorphic, 50 % (14/28) of fine linear, 25 % (10/40) of amorphous, 20 % (3/15) of round, 3 % (1/36) of coarse heterogeneous, and 0 % (0/9) of dystrophic calcifications. CONCLUSIONS: Among women ≥70 years that underwent stereotactic biopsy for calcifications only, we observed a high rate of malignancy. Additionally, coarse heterogeneous calcifications may warrant a probable benign designation. KEY POINTS: • Cancer rates of biopsied calcifications in women ≥70 years are high • Radiologists should not dismiss suspicious calcifications in older women • Coarse heterogeneous calcifications may warrant a probable benign designation.

Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study.

This study aims to characterize the effect of background tissue density and heterogeneity on the detection of irregular masses in breast tomosynthesis, while demonstrating the capability of the sophisticated tools that can be used in the design, implementation, and performance analysis of virtual clinical trials (VCTs). Twenty breast phantoms from the extended cardiac-torso (XCAT) family, generated based on dedicated breast computed tomography of human subjects, were used to extract a total of 2173 volumes of interest (VOIs) from simulated tomosynthesis images. Five different lesions, modeled after human subject tomosynthesis images, were embedded in the breasts and combined with the lesion absent condition yielded a total of [Formula: see text] VOIs. Effects of background tissue density and heterogeneity on the detection of the lesions were studied by implementing a composite hypothesis signal detection paradigm with location known exactly, lesion known exactly or statistically, and background known statistically. Using the area under the receiver operating characteristic curve, detection performance deteriorated as density was increased, yielding findings consistent with clinical studies. A human observer study was performed on a subset of the simulated tomosynthesis images, confirming the detection performance trends with respect to density and serving as a validation of the implemented detector. Performance of the implemented detector varied substantially across the 20 breasts. Furthermore, background tissue density and heterogeneity affected the log-likelihood ratio test statistic differently under lesion absent and lesion present conditions. Therefore, considering background tissue variability in tissue models can change the outcomes of a VCT and is hence of crucial importance. The XCAT breast phantoms have the potential to address this concern by offering realistic modeling of background tissue variability based on a wide range of human subjects, comprising various breast shapes, sizes, and densities.

Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics.

PURPOSE: To assess the interobserver variability of readers when outlining breast tumors in MRI, study the reasons behind the variability, and quantify the effect of the variability on algorithmic imaging features extracted from breast MRI. METHODS: Four readers annotated breast tumors from the MRI examinations of 50 patients from one institution using a bounding box to indicate a tumor. All of the annotated tumors were biopsy proven cancers. The similarity of bounding boxes was analyzed using Dice coefficients. An automatic tumor segmentation algorithm was used to segment tumors from the readers' annotations. The segmented tumors were then compared between readers using Dice coefficients as the similarity metric. Cases showing high interobserver variability (average Dice coefficient <0.8) after segmentation were analyzed by a panel of radiologists to identify the reasons causing the low level of agreement. Furthermore, an imaging feature, quantifying tumor and breast tissue enhancement dynamics, was extracted from each segmented tumor for a patient. Pearson's correlation coefficients were computed between the features for each pair of readers to assess the effect of the annotation on the feature values. Finally, the authors quantified the extent of variation in feature values caused by each of the individual reasons for low agreement. RESULTS: The average agreement between readers in terms of the overlap (Dice coefficient) of the bounding box was 0.60. Automatic segmentation of tumor improved the average Dice coefficient for 92% of the cases to the average value of 0.77. The mean agreement between readers expressed by the correlation coefficient for the imaging feature was 0.96. CONCLUSIONS: There is a moderate variability between readers when identifying the rectangular outline of breast tumors on MRI. This variability is alleviated by the automatic segmentation of the tumors. Furthermore, the moderate interobserver variability in terms of the bounding box does not translate into a considerable variability in terms of assessment of enhancement dynamics. The authors propose some additional ways to further reduce the interobserver variability.