A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI.

Breast density, or the amount of fibroglandular tissue (FGT) relative to the overall breast volume, increases the risk of developing breast cancer. Although previous studies have utilized deep learning to assess breast density, the limited public availability of data and quantitative tools hinders the development of better assessment tools. Our objective was to (1) create and share a large dataset of pixel-wise annotations according to well-defined criteria, and (2) develop, evaluate, and share an automated segmentation method for breast, FGT, and blood vessels using convolutional neural networks. We used the Duke Breast Cancer MRI dataset to randomly select 100 MRI studies and manually annotated the breast, FGT, and blood vessels for each study. Model performance was evaluated using the Dice similarity coefficient (DSC). The model achieved DSC values of 0.92 for breast, 0.86 for FGT, and 0.65 for blood vessels on the test set. The correlation between our model's predicted breast density and the manually generated masks was 0.95. The correlation between the predicted breast density and qualitative radiologist assessment was 0.75. Our automated models can accurately segment breast, FGT, and blood vessels using pre-contrast breast MRI data. The data and the models were made publicly available.

How does genetic variation modify ND-CNV phenotypes?

Rare copy-number variants (CNVs) associated with neurodevelopmental disorders (NDDs), i.e., ND-CNVs, provide an insight into the neurobiology of NDDs and, potentially, a link between biology and clinical outcomes. However, ND-CNVs are characterised by incomplete penetrance resulting in heterogeneous carrier phenotypes, ranging from non-affected to multimorbid psychiatric, neurological, and physical phenotypes. Recent evidence indicates that other variants in the genome, or 'other hits', may partially explain the variable expressivity of ND-CNVs. These may be other rare variants or the aggregated effects of common variants that modify NDD risk. Here we discuss the recent findings, current questions, and future challenges relating to other hits research in the context of ND-CNVs and their potential for improved clinical diagnostics and therapeutics for ND-CNV carriers.

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.

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.

Initial Clinical Experience with Stationary Digital Breast Tomosynthesis.

RATIONALE AND OBJECTIVES: A linear array of carbon nanotube-enabled x-ray sources allows for stationary digital breast tomosynthesis (sDBT), during which projection views are collected without the need to move the x-ray tube. This work presents our initial clinical experience with a first-generation sDBT device. MATERIALS AND METHODS: Following informed consent, women with a "suspicious abnormality" (Breast Imaging Reporting and Data System 4), discovered by digital mammography and awaiting biopsy, were also imaged by the first generation sDBT. Four radiologists participated in this paired-image study, completing questionnaires while interpreting the mammograms and sDBT image stacks. Areas under the receiver operating characteristic curve were used to measure reader performance (likelihood of correctly identifying malignancy based on pathology as ground truth), while a multivariate analysis assessed preference, as readers compared one modality to the next when interpreting diagnostically important image features. RESULTS: Findings from 43 women were available for analysis, in whom 12 cases of malignancy were identified by pathology. The mean areas under the receiver operating characteristic curve was significantly higher (p < 0.05) for sDBT than mammography for all breast density categories and breast thicknesses. Additionally, readers preferred sDBT over mammography when evaluating mass margins and shape, architectural distortion, and asymmetry, but preferred mammography when characterizing microcalcifications. CONCLUSION: Readers preferred sDBT over mammography when interpreting soft-tissue breast features and were diagnostically more accurate using images generated by sDBT in a Breast Imaging Reporting and Data System 4 population. However, the findings also demonstrated the need to improve microcalcification conspicuity, which is guiding both technological and image-processing design changes in future sDBT devices.

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.

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.

Radiology Trainee Performance in Digital Breast Tomosynthesis: Relationship Between Difficulty and Error-Making Patterns.

PURPOSE: The aim of this study was to better understand the relationship between digital breast tomosynthesis (DBT) difficulty and radiology trainee performance. METHODS: Twenty-seven radiology residents and fellows and three expert breast imagers reviewed 60 DBT studies consisting of unilateral craniocaudal and medial lateral oblique views. Trainees had no prior DBT experience. All readers provided difficulty ratings and final BI-RADS(®) scores. Expert breast imager consensus interpretations were used to determine the ground truth. Trainee sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated for low- and high-difficulty subsets of cases as assessed by each trainee him or herself (self-assessed difficulty) and consensus expert-assessed difficulty. RESULTS: For self-assessed difficulty, the trainee AUC was 0.696 for high-difficulty and 0.704 for low-difficulty cases (P = .753). Trainee sensitivity was 0.776 for high-difficulty and 0.538 for low-difficulty cases (P < .001). Trainee specificity was 0.558 for high-difficulty and 0.810 for low-difficulty cases (P < .001). For expert-assessed difficulty, the trainee AUC was 0.645 for high-difficulty and 0.816 for low-difficulty cases (P < .001). Trainee sensitivity was 0.612 for high-difficulty and .784 for low-difficulty cases (P < .001). Trainee specificity was 0.654 for high-difficulty and 0.765 for low-difficulty cases (P = .021). CONCLUSIONS: Cases deemed difficult by experts were associated with decreases in trainee AUC, sensitivity, and specificity. In contrast, for self-assessed more difficult cases, the trainee AUC was unchanged because of increased sensitivity and compensatory decreased specificity. Educators should incorporate these findings when developing educational materials to teach interpretation of DBT.

Does Breast Imaging Experience During Residency Translate Into Improved Initial Performance in Digital Breast Tomosynthesis?

PURPOSE: To determine the initial digital breast tomosynthesis (DBT) performance of radiology trainees with varying degrees of breast imaging experience. METHODS: To test trainee performance with DBT, we performed a reader study, after obtaining IRB approval. Two medical students, 20 radiology residents, 4 nonbreast imaging fellows, 3 breast imaging fellows, and 3 fellowship-trained breast imagers reviewed 60 unilateral DBT studies (craniocaudal and medio-lateral oblique views). Trainees had no DBT experience. Each reader recorded a final BI-RADS assessment for each case. The consensus interpretations from fellowship-trained breast imagers were used to establish the ground truth. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated. For analysis, first- through third-year residents were classified as junior trainees, and fourth-year residents plus nonbreast imaging fellows were classified as senior trainees. RESULTS: The AUCs were .569 for medical students, .721 for junior trainees, .701 for senior trainees, and .792 for breast imaging fellows. The junior and senior trainee AUCs were equivalent (P < .01) using a two one-sided test for equivalence, with a significance threshold of 0.1. The sensitivities and specificities were highest for breast imaging fellows (.778 and .815 respectively), but similar for junior (.631 and .714, respectively) and senior trainees (.678 and .661, respectively). CONCLUSIONS: Initial performance with DBT among radiology residents and nonbreast imaging fellows is independent of years of training. Radiology educators should consider these findings when developing educational materials.

Frequency of Malignancy and Imaging Characteristics of Probably Benign Lesions Seen at Breast MRI.

OBJECTIVE: The purposes of this study were to evaluate the frequency, follow-up compliance, and cancer rate of MRI BI-RADS category 3 lesions and to determine the cancer rate for individual BI-RADS descriptors. MATERIALS AND METHODS: A retrospective review was conducted of breast MRI examinations with an assessment of probably benign (BI-RADS category 3) from among 4279 consecutive breast MRI examinations performed from January 2005 through December 2009. The review revealed 282 (6.6%) examinations with 332 lesions defined as BI-RADS 3. Pathologic results, 2 years of follow-up imaging findings, or both were reviewed. The frequency of BI-RADS 3 assessments, follow-up imaging compliance, and cancer yield were calculated. Three fellowship-trained breast imagers reevaluated all lesions and recorded descriptors from the MRI lexicon of the fifth edition of the BI-RADS atlas. The distribution and likelihood of malignancy for each descriptor were calculated. RESULTS: The follow-up compliance rate was 84.3% (280/332), and the malignancy rate was 4.3% (12/280). There were 50 (17.9%) individual foci, 61 (21.8%) multiple foci, 74 (26.4%) masses, and 95 (33.9%) nonmass enhancement lesions. Masses were most commonly oval (59.5% [44/74]), circumscribed (75.7% [56/74]), and homogeneously enhancing (43.2% [32/74]). Nonmass enhancement was most commonly focal (57.9% [55/95]) with heterogeneous enhancement (53.7% [51/95]) Most of the lesions had persistent kinetics (74.3% [208/280]). The background parenchymal enhancement was most commonly mild (51.1% [143/280]). CONCLUSION: MRI BI-RADS category 3 is not frequently used, and the levels of patient compliance with follow-up imaging are acceptable. The cancer yield for probably benign lesions is greater for MRI-detected than for mammographically detected lesions, especially for specific BI-RADS descriptors.

Interobserver Variability Between Breast Imagers Using the Fifth Edition of the BI-RADS MRI Lexicon.

OBJECTIVE: The purpose of this study was to assess the interobserver variability of users of the MRI lexicon in the fifth edition of the BI-RADS atlas. MATERIALS AND METHODS: Three breast imaging specialists reviewed 280 routine clinical breast MRI findings reported as BI-RADS category 3. Lesions reported as BI-RADS 3 were chosen because variability in the use of BI-RADS descriptors may influence which lesions are classified as probably benign. Each blinded reader reviewed every study and recorded breast features (background parenchymal enhancement) and lesion features (lesion morphology, mass shape, mass margin, mass internal enhancement, nonmass enhancement distribution, nonmass enhancement internal enhancement, enhancement kinetics) according to the fifth edition of the BI-RADS lexicon and provided a final BI-RADS assessment. Interobserver variability was calculated for each breast and lesion feature and for the final BI-RADS assessment. RESULTS: Interobserver variability for background parenchymal enhancement was fair (ĸ = 0.28). There was moderate agreement on lesion morphology (ĸ = 0.53). For masses, there was substantial agreement on shape (ĸ = 0.72), margin (ĸ = 0.78), and internal enhancement (ĸ = 0.69). For nonmass enhancement, there was substantial agreement on distribution (ĸ = 0.69) and internal enhancement (ĸ = 0.62). There was slight agreement on lesion kinetics (ĸ = 0.19) and final BI-RADS assessment (ĸ = 0.11). CONCLUSION: There is moderate to substantial agreement on most MRI BI-RADS lesion morphology descriptors, particularly mass and nonmass enhancement features, which are important predictors of malignancy. Considerable disagreement remains, however, among experienced readers whether to follow particular findings.

Using the BI-RADS lexicon in a restrictive form of double reading as a strategy for minimizing screening mammography recall rates.

OBJECTIVE: The purpose of this article is to determine the potential reduction in screening recall rates by strictly following standardized BI-RADS lexicon for lesions seen on screening mammography. MATERIALS AND METHODS: Of 3084 consecutive mammograms performed at our screening facilities, 345 women with 437 lesions were recalled for additional imaging and constituted our study population. Three radiologists retrospectively classified lesions using the standard BI-RADS lexicon and assigned each to one of four groups: group A, the finding met criteria for recall by the BI-RADS lexicon; group B, the finding did not meet strict BI-RADS criteria for recall but was sufficiently indeterminate to warrant recall by the majority of the study panel; group C, the finding was classifiable by the BI-RADS lexicon but was not recalled because it was benign or stable; and group D, the questioned finding was not considered an abnormality by our study panel. Recall rates and the cancer detection rate were determined. The adjusted recall rate was calculated for lesions considered appropriate for recall (group A), and the reduction in the recall rate was determined. RESULTS: Nineteen malignancies were detected in our recalled population, for a cancer detection rate of 0.65%. All 19 malignancies were lesions considered appropriate for recall (group A). If only group A lesions had been recalled, the recall rate would have decreased from 11.4% to 6.2%, representing a 46% reduction in recalls without affecting the cancer detection rate. CONCLUSION: Using the BI-RADS lexicon as a decision-making aid may help adjust thresholds for recalling indeterminate or suspicious lesions and reduce recall rates from screening mammography.

Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection.

PURPOSE: Mammography is known to be one of the most difficult radiographic exams to interpret. Mammography has important limitations, including the superposition of normal tissue that can obscure a mass, chance alignment of normal tissue to mimic a true lesion and the inability to derive volumetric information. It has been shown that stereomammography can overcome these deficiencies by showing that layers of normal tissue lay at different depths. If standard stereomammography (i.e., a single stereoscopic pair consisting of two projection images) can significantly improve lesion detection, how will multiview stereoscopy (MVS), where many projection images are used, compare to mammography? The aim of this study was to assess the relative performance of MVS compared to mammography for breast mass detection. METHODS: The MVS image sets consisted of the 25 raw projection images acquired over an arc of approximately 45 degrees using a Siemens prototype breast tomosynthesis system. The mammograms were acquired using a commercial Siemens FFDM system. The raw data were taken from both of these systems for 27 cases and realistic simulated mass lesions were added to duplicates of the 27 images at the same local contrast. The images with lesions (27 mammography and 27 MVS) and the images without lesions (27 mammography and 27 MVS) were then postprocessed to provide comparable and representative image appearance across the two modalities. All 108 image sets were shown to five full-time breast imaging radiologists in random order on a state-of-the-art stereoscopic display. The observers were asked to give a confidence rating for each image (0 for lesion definitely not present, 100 for lesion definitely present). The ratings were then compiled and processed using ROC and variance analysis. RESULTS: The mean AUC for the five observers was 0.614 +/- 0.055 for mammography and 0.778 +/- 0.052 for multiview stereoscopy. The difference of 0.164 +/- 0.065 was statistically significant with a p-value of 0.0148. CONCLUSIONS: The differences in the AUCs and the p-value suggest that multiview stereoscopy has a statistically significant advantage over mammography in the detection of simulated breast masses. This highlights the dominance of anatomical noise compared to quantum noise for breast mass detection. It also shows that significant lesion detection can be achieved with MVS without any of the artifacts associated with tomosynthesis.

Breast self-examination: defining a cohort still in need.

BACKGROUND: The value of breast self-examination (BSE) to detect early breast cancer is controversial. METHODS: Within an institutional review board-approved prospective study, 147 high-risk women were enrolled from 2004 to 2007. Yearly clinical examination, BSE teaching, and mammography were performed simultaneously followed by interval breast magnetic resonance imaging (MRI). Women underwent additional BSE teaching at 6 months. Women reporting a mass on BSE underwent clinical evaluation. RESULTS: Fourteen breast cancers were detected in 12 women. BSE detected 6/14 breast cancers versus 6/14 detected by MRI and 2/14 by mammography. Of 24 masses detected by BSE, 6/24 were malignant. The sensitivity, specificity, and predictive value of BSE to detect breast cancer were 58.3%, 87.4%, and 29.2%, respectively. The sensitivity, specificity, and predictive value of a Breast Image Reporting and Data System (BI-RADS) score of >or=4 on MRI were 66.7%, 88.9%, and 34.8%, respectively. CONCLUSIONS: BSE detects new breast cancers in high-risk women undergoing screening mammogram, CBE, and yearly breast MRI.

Comparison of LCD and CRT displays based on efficacy for digital mammography.

RATIONALE AND OBJECTIVES: To compare two display technologies, cathode ray tube (CRT) and liquid crystal display (LCD), in terms of diagnostic accuracy for several common clinical tasks in digital mammography. MATERIALS AND METHODS: Simulated masses and microcalcifications were inserted into normal digital mammograms to produce an image set of 400 images. Images were viewed on one CRT and one LCD medical-quality display device by five experienced breast-imaging radiologists who rated the images using a categorical rating paradigm. The observer data were analyzed to determine overall classification accuracy, overall lesion detection accuracy, and accuracy for four specific diagnostic tasks: detection of benign masses, malignant masses, and microcalcifications, and discrimination of benign and malignant masses. RESULTS: Radiologists had similar overall classification accuracy (LCD: 0.83 +/- 0.01, CRT: 0.82 +/- 0.01) and lesion detection accuracy (LCD: 0.87 +/- 0.01, CRT: 0.85 +/- 0.01) on both displays. The difference in accuracy between LCD and CRT for the detection of benign masses, malignant masses, and microcalcifications, and discrimination of benign and malignant masses was -0.019 +/- 0.009, 0.020 +/- 0.008, 0.012 +/- 0.013, and 0.0094 +/- 0.011, respectively. Overall, the two displays did not exhibit any statistically significant difference (P > .05). CONCLUSION: This study explored the suitability of two different soft-copy displays for the viewing of mammographic images. It found that LCD and CRT displays offer similar clinical utility for mammographic tasks.

Streaming detection for evaluation of indeterminate sonographic breast masses: a pilot study.

OBJECTIVE: Streaming detection is a novel sonography technique that uses ultrasonic energy to induce movement in cyst fluid that is detected on Doppler sonography. This pilot study evaluates the utility of streaming detection for differentiating cysts from solid masses in breast lesions that are indeterminate on sonography. SUBJECTS AND METHODS: Thirty-nine lesions-11 simple cysts and seven solid masses (control group) and 21 masses with indeterminate findings for the diagnosis of a cyst versus a solid lesion (study group)-in 34 patients were evaluated using streaming detection. All lesions underwent cyst aspiration or biopsy (n = 35) or were diagnosed simple cysts (n = 4) on sonography. Lesion size and depth were recorded. Streaming detection software was placed on conventional sonography units. Acoustic pulses were focused on the lesion, and if fluid movement was generated, it was seen on the spectral Doppler display as velocity away from the transducer. Lesions were then aspirated or underwent biopsy, and the viscosity of the aspirated fluid was recorded. The sensitivity and specificity of the technique and the effect of cyst size, cyst depth, and fluid viscosity in diagnosing fluid-filled cysts were assessed. RESULTS: Overall, 31 cysts and eight solid masses (seven benign, one carcinoma) were diagnosed in the study and control groups. Aspiration of indeterminate lesions resulted in 20 cysts and one solid mass. Lesions ranged in size from 4 to 47 mm and in depth from 4 to 29 mm. In the control group, streaming detection correctly showed nine of the 11 simple cysts (sensitivity, 82%; positive predictive value, 100%), and acoustic streaming was absent in all seven solid masses (specificity, 100%; negative predictive value, 78%). Of the indeterminate lesions, streaming detection allowed correct identification of 10 of 20 cysts (sensitivity, 50%; positive predictive value, 100%). Acoustic streaming was not detected in the one solid study group lesion. Neither cyst size or depth nor fluid viscosity had a significant effect on the ability to detect fluid. CONCLUSION: The streaming detection technique improved differentiation of cysts from solid masses in indeterminate lesions and has potential for reducing the number of recommended cyst aspirations for the diagnosis of indeterminate breast masses.

The effects of gray scale image processing on digital mammography interpretation performance.

RATIONALE AND OBJECTIVES: To determine the effects of three image-processing algorithms on diagnostic accuracy of digital mammography in comparison with conventional screen-film mammography. MATERIALS AND METHODS: A total of 201 cases consisting of nonprocessed soft copy versions of the digital mammograms acquired from GE, Fischer, and Trex digital mammography systems (1997-1999) and conventional screen-film mammograms of the same patients were interpreted by nine radiologists. The raw digital data were processed with each of three different image-processing algorithms creating three presentations-manufacturer's default (applied and laser printed to film by each of the manufacturers), MUSICA, and PLAHE-were presented in soft copy display. There were three radiologists per presentation. RESULTS: Area under the receiver operating characteristic curve for GE digital mass cases was worse than screen-film for all digital presentations. The area under the receiver operating characteristic for Trex digital mass cases was better, but only with images processed with the manufacturer's default algorithm. Sensitivity for GE digital mass cases was worse than screen film for all digital presentations. Specificity for Fischer digital calcifications cases was worse than screen film for images processed in default and PLAHE algorithms. Specificity for Trex digital calcifications cases was worse than screen film for images processed with MUSICA. CONCLUSION: Specific image-processing algorithms may be necessary for optimal presentation for interpretation based on machine and lesion type.

Comparison of recall and cancer detection rates for immediate versus batch interpretation of screening mammograms.

PURPOSE: To retrospectively compare recall and cancer detection rates between immediate and subsequent batch methods for interpretation of screening mammograms. MATERIALS AND METHODS: Institutional review board approval was obtained, and informed consent was waived. Retrospective analysis was performed for 8698 screening mammograms obtained between January 1 and October 31, 2001, which were interpreted either immediately (n = 4113) or subsequently with batch method (n = 4585). Data were collected from data reporting system and patient billing records. Patients with high risk factors were excluded; 3441 patients were in the immediate group, and 3932 were in the batch group. The two groups were compared with respect to age, breast density, and availability of comparison films with Wilcoxon rank sum test. Recall rates and cancer detection rates for each group were determined and compared with Pearson chi(2) test; false-negative rates were compared with Fischer exact test. RESULTS: A significant difference (P < .001) was noted in recall rates between immediate (18%) and batch (14%) groups; however, no significant difference (P = .7) was noted in cancer detection rates (immediate, 0.5%; batch, 0.4%). Mean age of patients was 56.8 years (age range, 21-96 years) in the immediate group and 56.2 years (age range 24-98 years) in the batch group (P = .02). Comparison of breast densities between groups indicates no statistically significant difference (P = .4). The batch group had significantly fewer comparison mammograms (3106 [79%]) available than the immediate group (2856 [83%]) (P < .001). There was no significant difference in false-negative rates between the immediate group (0.1%) and the batch group (0.1%) (P > .99). CONCLUSION: Immediate interpretation of screening mammograms resulted in a statistically significant increase in recalls and additional clinical work-ups of perceived abnormalities; however, no significant difference in cancer detection rate was detected between groups.