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To develop a nomogram by integrating B-mode ultrasound (US), strain ratio (SR), and radiomics signature (RS) effectively differentiating between benign and malignant lesions in the Breast Imaging Reporting and Data System (BI-RADS) 4. We retrospectively recruited 709 consecutive patients who were assigned a BI-RADS 4 and underwent curative resection or biopsy between 2017 and 2022. US images were collected before surgery. A RS was developed through a multistep feature selection and construction process. Histology findings served as the gold standard. Univariate and multivariate regression analysis were employed to analyze the clinical and US characteristics and identify variables for developing a nomogram. The calibration and discrimination of the nomogram were conducted to evaluate its performance. The study included a total of 709 patients, with 497 in the training set and 212 in the validation set. In the training set, the B-mode US had an AUC of 0.84 (95% confidence interval [CI], 0.80, 0.87). The SR demonstrated an AUC of 0.78 (95% CI, 0.74, 0.82), while the RS showed an AUC of 0.85 (95% CI, 0.81, 0.88). Notably, the nomogram exhibited superior performance compared to the conventional US, SR, and RS (AUC = 0.93, both p < 0.05, as per the Delong test). The clinical usefulness of the nomogram was favorable. The calibrated nomogram can be specifically designed to predict the malignancy of breast lesions in the BI-RADS 4 category. [ABSTRACT FROM AUTHOR]

The Ugandan Ministry of Health adopted BI-RADS as standard of care in 2016. The authors performed a medical audit of breast ultrasound practices at four tertiary-level hospitals to assess interpretive performance. The authors also determined the effect of a low-cost navigation program linking breast imaging and pathology on the percentage of patients completing diagnostic care. The authors retrieved 966 consecutive diagnostic breast ultrasound reports, with complete data, for studies performed on women aged >18 years presenting with symptoms of breast cancer between 2018 and 2020 from participating hospitals. Ultrasound results were linked to tumor registries and patient follow-up. A medical audit was performed according to the ACR's BI-RADS Atlas, fifth edition, and results were compared with those of a prior audit performed in 2013. At Mulago Hospital, an intervention was piloted on the basis of patient navigation, cost sharing, and same-day imaging, tissue sampling, and pathology. In total, 888 breast ultrasound examinations (91.9%) were eligible for inclusion. Compared with 2013, the postintervention cancer detection rate increased from 38 to 148.7 cancers per 1,000 examinations, positive predictive value 2 from 29.6% to 48.9%, and positive predictive value 3 from 62.7% to 79.9%. Specificity decreased from 90.5% to 87.7% and sensitivity from 92.3% to 81.1%. The mean time from tissue sampling to receipt of a diagnosis decreased from 60 to 7 days. The intervention increased the percentage of patients completing diagnostic care from 0% to 100%. Efforts to establish a culture of continuous quality improvement in breast ultrasound require robust data collection that links imaging results to pathology and patient follow-up. Interpretive performance met BI-RADS benchmarks for palpable masses, except sensitivity. This resource-appropriate strategy linking imaging, tissue sampling, and pathology interpretation decreased time to diagnosis and rates of loss to follow-up and improved the precision of the audit. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) systems have been increasingly applied to breast ultrasonography. They are expected to decrease the workload of radiologists and to improve diagnostic accuracy. The aim of this study is to evaluate the performance of an AI system for the BI-RADS category assessment in breast masses detected on breast ultrasound. A total of 715 masses detected in 530 patients were analyzed. Three breast imaging centers of the same institution and nine breast radiologists participated in this study. Ultrasound was performed by one radiologist who obtained two orthogonal views of each detected lesion. These images were retrospectively reviewed by a second radiologist blinded to the patient's clinical data. A commercial AI system evaluated images. The level of agreement between the AI system and the two radiologists and their diagnostic performance were calculated according to dichotomic BI-RADS category assessment. This study included 715 breast masses. Of these, 134 (18.75%) were malignant, and 581 (81.25%) were benign. In discriminating benign and probably benign from suspicious lesions, the agreement between AI and the first and second radiologists was moderate statistically. The sensitivity and specificity of radiologist 1, radiologist 2, and AI were calculated as 98.51% and 80.72%, 97.76% and 75.56%, and 98.51% and 65.40%, respectively. For radiologist 1, the positive predictive value (PPV) was 54.10%, the negative predictive value (NPV) was 99.58%, and the accuracy was 84.06%. Radiologist 2 achieved a PPV of 47.99%, NPV of 99.32%, and accuracy of 79.72%. The AI system exhibited a PPV of 39.64%, NPV of 99.48%, and accuracy of 71.61%. Notably, none of the lesions categorized as BI-RADS 2 by AI were malignant, while 2 of the lesions classified as BI-RADS 3 by AI were subsequently confirmed as malignant. By considering AI-assigned BI-RADS 2 as safe, we could potentially avoid 11% (18 out of 163) of benign lesion biopsies and 46.2% (110 out of 238) of follow-ups. AI proves effective in predicting malignancy. Integrating it into the clinical workflow has the potential to reduce unnecessary biopsies and short-term follow-ups, which, in turn, can contribute to sustainability in healthcare practices. [ABSTRACT FROM AUTHOR]

Background: Breast MRI is the imaging modality of choice in patients with palpable breast lesions unequivocal on mammography and ultrasonography. This study aims to evaluate the role of dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient mapping in the characterization and differentiation of various palpable breast lesions. This prospective observational study was conducted in a tertiary care hospital between July 2019 and June 2021. Sixty-six patients with palpable breast lesions were undergone MRI scans of the breasts. The various palpable breast lesions were categorized according to the 5th edition BI-RADS lexicon. The sensitivity of ADC mapping and DCE-MRI was determined for differentiation of various palpable breast lesions according to the BI-RADS category and gold standard histopathological findings. Results: Of 66 patients with palpable breast lesions, 36 patients (54.5%) were benign and 30 patients (45.5%) were malignant lesions. Malignant palpable breast lesions had a mean ADC value of 0.939 ± 0.166[SD] × 10−3 mm2/s, and benign lesions had 1.891 ± 0.524[SD] × 10−3 mm2/s where unpaired Student t-test showed statistically significant difference of P value 0.0005. BI-RADS 2 lesions had a mean ADC value of 2.056 ± 0.471[SD] × 10−3 mm2/s, BI-RADS 3 had 1.314 ± 0.151[SD] × 10−3 mm2/s, BI-RADS 4 had 0.935 ± 0.119[SD] × 10−3 mm2/s, and BI-RADS 5 had 0.930 ± 0.943[SD] × 10−3 mm2/s. BI-RADS 2 category showed optimal cutoff mean ADC of 1.508 × 10−3 mm2/s with a sensitivity of 85.7% and specificity of 94.7%, BI-RADS 3 lesions had 1.208 × 10−3 mm2/s with a sensitivity of 75% and specificity of 55.2%, BI-RADS 4 lesions had 1.064 × 10−3 mm2/s with a sensitivity 80% and specificity of 67.9%, and BI-RADS 5 lesions had 1.013 × 10−3 mm2/s with a sensitivity of 80% and specificity of 82.6%. Conclusions: Breast MRI is superior to the other imaging modalities for the characterization and differentiation of various palpable breast lesions. The combined use of ADC mapping and DCE-MRI had more sensitivity than conventional MRI, ADC mapping or DCE-MRI alone. [ABSTRACT FROM AUTHOR]

A solitary dilated duct (SDD) is a single asymmetrically dilated breast duct with diameter more than 2 mm. The Breast Imaging Reporting and Data System (BI-RADS) fifth edition recommends additional imaging and biopsy for SDDs without demonstrated benign etiology, however management of this rare entity remains controversial. This study describes practice patterns, malignancy rate, and features associated with high-risk/malignant SDDs to better stratify patients requiring biopsy versus follow-up. This IRB-approved retrospective study identified mammographic, sonographic and MRI exams utilizing the term "solitary dilated duct" at a multisite academic institution between 1/1/2010 and 12/31/2020. Clinical and imaging features, BI-RADS assessments, and outcomes were analyzed. Univariate and multivariate analyses identified predictors of high-risk/malignant histology. SDDs identified in 49 women (mean age 56.1 years) were assessed as BI-RADS 4/5 (31/49, 63%), BI-RADS 3 (9/49, 18%), or BI-RADS 2 (9/49, 18%). Most sampled lesions were benign (16/31, 52%) and the remaining were high-risk (15/31, 48%, all papillary lesions). The only papilloma with atypia on core biopsy upgraded to grade 2 DCIS on excision (malignancy rate 1/49, 2%). All anechoic SDDs were benign (n =13), and all benign SDDs lacked internal vascularity. SDDs with associated masses were associated with malignant/high-risk outcomes on multivariate analysis (p <.001). The BI-RADS fifth edition recommends biopsy for SDDs without demonstrated benign etiology. In our 11-year study period, practice patterns were variable with a low malignancy rate of 2%. Our findings suggest that anechoic SDDs may be followed, and SDDs with associated masses or internal vascularity require biopsy. [ABSTRACT FROM AUTHOR]

Breast Imaging Reporting and Data System (BI-RADS) is a communication and data tracking system that standardizes and controls the quality of reporting by presenting lexicon descriptors, assessment categories, and recommendations for managing breast lesions. Using standardized terminology recommended by BI-RADS, radiologists can concisely and reproducibly communicate breast imaging results to clinicians. They can also provide the estimated malignant probability of the lesions found and guide management for them by determining the final assessment category. The limitations of BI-RADS 5th edition currently in use are that there are some areas for which standardized terminologies still need to be established, and that the diagnostic criteria of MRI assessment categories 3 and 4 are ambiguous compared to those for mammography or ultrasound. The next revision of BI-RADS is expected to include solutions for overcoming current limitations. [ABSTRACT FROM AUTHOR]

Mammographic density is a well-defined risk factor for breast cancer and having extremely dense breast tissue is associated with a one-to six-fold increased risk of breast cancer. However, it is questioned whether this increased risk estimate is applicable to current breast density classification methods. Therefore, the aim of this study was to further investigate and clarify the association between mammographic density and breast cancer risk based on current literature. Medline, Embase and Web of Science were systematically searched for articles published since 2013, that used BI-RADS lexicon 5th edition and incorporated data on digital mammography. Crude and maximally confounder-adjusted data were pooled in odds ratios (ORs) using random-effects models. Heterogeneity regarding breast cancer risks were investigated using I2 statistic, stratified and sensitivity analyses. Nine observational studies were included. Having extremely dense breast tissue (BI-RADS density D) resulted in a 2.11-fold (95% CI 1.84–2.42) increased breast cancer risk compared to having scattered dense breast tissue (BI-RADS density B). Sensitivity analysis showed that when only using data that had adjusted for age and BMI, the breast cancer risk was 1.83-fold (95% CI 1.52–2.21) increased. Both results were statistically significant and homogenous. Mammographic breast density BI-RADS D is associated with an approximately two-fold increased risk of breast cancer compared to having BI-RADS density B in general population women. This is a novel and lower risk estimate compared to previously reported and might be explained due to the use of digital mammography and BI-RADS lexicon 5th edition. • So far, breast density was linked to a one-to sixfold increased breast cancer risk. • Prior risk estimates were based on old BI-RADS lexicon and screen-film mammography. • We included only studies using digital mammography and BI-RADS lexicon 5th edition. • Breast density D is associated with a two-fold increased risk of breast cancer. • This is a lower risk estimate compared to previously reported studies. [ABSTRACT FROM AUTHOR]

OBJECTIVE: In this study, we aim at investigating the magnetic resonance (MR) imaging features of molecular subtypes, according to the BIRADS Atlas. PATIENTS AND METHODS: The preoperative MRI examinations of 104 breast cancer patients were reviewed retrospectively using the 5th Edition Breast Imaging-Reporting and Data System (BI-RADS) Atlas. According to BIRADS, cases were classified as mass or non-mass enhancement. Background parenchymal enhancements of the cases were evaluated. The population was examined for shape, contour, enhancement characteristics of masses, distribution and internal enhancement patterns of nonmass enhancements, background parenchymal enhancement, multifocal/multicentric (MFMS) status, presence of axillary LAP, ADC values, and kinetic aspects. The Kruskal-Wallis and Chi-square tests were used to explore the connection between molecular subtypes and MR data. RESULTS: The link between molecular subtypes and mass/non-mass enhancement was discovered to be statistically significant (p=0.007). The shape (p=0.001) and contour (p=0.001) properties of the masses were observed to differ depending on the molecular subtypes. The Luminal types were usually irregularly shaped with irregular/spiculated contours, whereas the HER-2 (+) and Triple (-) subtypes were mostly oval/round with smooth contours. The subtype with the highest non-mass enhancement rate (70%) was HER-2 (+). Axillary lymphadenopathy was most common (64.3%) in the Triple (-) subtype (p=0.033). CONCLUSIONS: According to the BIRADS Atlas, molecular subtypes exhibit a wide range of imaging properties in MR in our study. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The aim of the study was to evaluate the results of ultrasound-guided excisional biopsy in patients with nonpalpable breast lesions and examine factors associated with malignancy. PATIENTS AND METHODS: A total of 380 patients who underwent ultrasound-guided excisional biopsy for suspected nonpalpable breast masses, between May 2012 and 2018, were retrospectively examined. Histopathological results of the patients were compared regarding age, ultrasound findings, ultrasonographic and mammographic Breast Imaging Reporting and Data System (BI-RADS) categories and factors predicting malignancy were determined. RESULTS: The mean age of the patients was 48.35 ± 11.23 (17-86) years. There was a history of breast cancer in the families of 22 (5.8%) patients, and 187 (49.2%) patients were in menopause. The complication rate was found to be 6.6%. Malignant lesions were detected in 76 (20%) patients and benign lesions were detected in 304 (79.99%) patients. Some benign lesions were high-risk lesions (16.8%). Most of the patients with malignant lesions had early-stage breast cancer (83.3%). In univariate analyzes, ultrasonographic BI-RADS, mammographic BIRADS and age variables were found to be associated with malignancy (p = 0.0001). In the multiple logistic regression analysis, ultrasonographic and mammographic BI-RADS values were found to be risk factors for malignancy (p = 0.0001). CONCLUSIONS: BI-RADS scoring was used to determine risk factors in predicting malignancy in the evaluation of suspected nonpalpable lesions. The ultrasound-guided wire localization biopsy is a useful method in nonpalpable breast lesions with suspected malignancy that cannot be diagnosed by core/vacuum biopsy or in cases where incompatibility between pathology and radiology results exists. [ABSTRACT FROM AUTHOR]

Purpose: To assess the diagnostic value of strain ratio elastography (SRE), a semiquantitative elastosonographic method based on the displacement of the tissue from an external source (manual compression with the transducer), as compared and in combination with conventional ultrasound for the differentiation of breast lesions. Methods: One hundred and eighty-two patients with breast lesions consecutively underwent B-mode, color Doppler US, and strain US-elastography. Each lesion was classified according to the BI-RADS lexicon by evaluating the size, the B-mode, and color Doppler features and then evaluated by SRE. Histology proven by biopsy was used as the gold standard and the patients with malignant lesions subsequently underwent operations. The diagnostic performance of each method was assessed with 2 × 2 contingency tables and ROC curve analysis. To maximize the SRE sensitivity and specificity, the SRE cut-off value was calculated using the Youden test. Results: Histological examination revealed 66 benign and 116 malignant breast lesions. The conventional ultrasound showed sensitivity and specificity for the differentiation of benign and malignant lesions of 86.2% and 75.8%, respectively. Similar results were found for strain US-elastography with a cut-off of 2.49, with sensitivity and specificity of 89.7% and 72.7%, respectively. The association of conventional ultrasound with the SRE value increased the sensitivity (98.3%) but decreased the specificity compared with conventional US alone (63.6%). Conclusion: Strain US-elastography can be associated with BI-RADS US examination. According to our preliminary results, it helped increase the sensitivity although it decreased the specificity. However, further multicenter studies on a larger population are warranted. [ABSTRACT FROM AUTHOR]

Background: To assess inter-reader agreement for US BI-RADS descriptors using S-Detect: a computer-guided decision-making software assisting in US morphologic analysis. Methods: 73 solid focal breast lesions (FBLs) (mean size: 15.9 mm) in 73 consecutive women (mean age: 51 years) detected at US were randomly and independently assessed according to the BI-RADS US lexicon, without and with S-Detect, by five independent reviewers. US-guided core-biopsy and 24-month follow-up were considered as standard of reference. Kappa statistics were calculated to assess inter-operator agreement, between the baseline and after S-Detect evaluation. Agreement was graded as poor (≤ 0.20), moderate (0.21–0.40), fair (0.41–0.60), good (0.61–0.80), or very good (0.81–1.00). Results: 33/73 (45.2%) FBLs were malignant and 40/73 (54.8%) FBLs were benign. A statistically significant improvement of inter-reader agreement from fair to good with the use of S-Detect was observed for shape (from 0.421 to 0.612) and orientation (from 0.417 to 0.7) (p < 0.0001) and from moderate to fair for margin (from 0.204 to 0.482) and posterior features (from 0.286 to 0.522) (p < 0.0001). At baseline analysis isoechoic (0.0485) and heterogeneous (0.1978) echo pattern, microlobulated (0.1161) angular (0.1204) and spiculated (0.1692) margins and combined pattern (0.1549) for posterior features showed the worst agreement rate (poor). After S-Detect evaluation, all variables but isoechoic pattern showed an agreement class upgrade with a statistically significant improvement of inter-reader agreement (p < 0.0001). Conclusions: S-Detect significantly improved inter-reader agreement in the assessment of FBLs according to the BI-RADS US lexicon but evaluation of margin and echo pattern needs to be further improved, particularly isoechoic pattern. [ABSTRACT FROM AUTHOR]

• Pediatric breast masses are very rarely malignant. • BI-RADS ultrasound categories has not been validated in pediatric and adolescent patients. • BI-RADS overestimates the likelihood of malignancy in pediatric breast masses. • Pediatric breast procedures should not be performed solely based on BI-RADS. Background/Purpose: Breast masses in the pediatric population cause patient and family concern, partially driven by public awareness of adult breast cancer. However, the spectrum of breast masses in children differs greatly from that in adults, and malignancy is exceedingly rare. The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) ultrasound-based classification system is the diagnostic standard, yet no study has validated BI-RADS in pediatric patients. This study compares BI-RADS classification with histologic diagnoses to evaluate BI-RADS validity in pediatric patients. Methods: Multicenter retrospective evaluation of breast masses in patients under 21 years. Ultrasound reports were compared with histologic diagnoses. Results: There were 283 patients with breast pathology results after excluding clinical diagnoses of gynecomastia. Mean age was 16.9 (SD 2.3), ranging 10–20 years. 227 had pre-operative ultrasounds, and 84% (191/227) were assigned a BI-RADS category. BI-RADS 4 was the most frequent category (55%, n = 124), by definition predicting 2 – 95% likelihood of malignancy. However, pathology was benign in all patients. Conclusions: The current BI-RADS categorization system overestimates cancer risk when applied to pediatric patients. BI-RADS scores should not be assigned to pediatric patients, and BIRADS-defined recommendations for biopsy should be disregarded. A pediatric-specific classification system could be useful. [ABSTRACT FROM AUTHOR]

Objective: We developed deep learning algorithms to automatically assess BI-RADS breast density.Methods: Using a large multi-institution patient cohort of 108,230 digital screening mammograms from the Digital Mammographic Imaging Screening Trial, we investigated the effect of data, model, and training parameters on overall model performance and provided crowdsourcing evaluation from the attendees of the ACR 2019 Annual Meeting.Results: Our best-performing algorithm achieved good agreement with radiologists who were qualified interpreters of mammograms, with a four-class κ of 0.667. When training was performed with randomly sampled images from the data set versus sampling equal number of images from each density category, the model predictions were biased away from the low-prevalence categories such as extremely dense breasts. The net result was an increase in sensitivity and a decrease in specificity for predicting dense breasts for equal class compared with random sampling. We also found that the performance of the model degrades when we evaluate on digital mammography data formats that differ from the one that we trained on, emphasizing the importance of multi-institutional training sets. Lastly, we showed that crowdsourced annotations, including those from attendees who routinely read mammograms, had higher agreement with our algorithm than with the original interpreting radiologists.Conclusion: We demonstrated the possible parameters that can influence the performance of the model and how crowdsourcing can be used for evaluation. This study was performed in tandem with the development of the ACR AI-LAB, a platform for democratizing artificial intelligence. [ABSTRACT FROM AUTHOR]

High-resolution ultrasonography (US) is a valuable tool in breast imaging. Nevertheless, US is an operator-dependent technique: to overcome this issue, the American College of Radiology (ACR) has developed the breast imaging-reporting and data system (BI-RADS) US lexicon. Despite this effort, the variability in the assessment of focal breast lesions (FBLs) with the use of BI-RADS US lexicon is still an issue. Within this framework, evidence shows that computer-aided image analysis may be effective in improving the radiologist's assessment of FBLs. In particular, S-Detect is a newly developed image-analytic computer program that provides assistance in morphologic analysis of FBLs seen on US according to the BI-RADS US lexicon. This pictorial essay describes state-of-the-art of sonographic characterization of FBLs by using S-Detect. [ABSTRACT FROM AUTHOR]

Objective: Assess rate of and factors associated with optimal follow-up in patients with BI-RADS 3 breast findings.Methods: This Institutional Review Board-approved, retrospective cohort study, performed at an academic medical center, included all women undergoing breast imaging (ultrasound and mammography) in 2016. Index reports for unique patients with an assessment of BI-RADS 3 (retrieved via natural language processing) comprised the study population. Patient-specific and provider-related features were extracted from the Research Data Warehouse. The Institutional Cancer Registry identified patients diagnosed with breast cancer. Optimal follow-up rate was calculated as patients with follow-up imaging on the same breast 3 to 9 months from the index examination among patients with BI-RADS 3 assessments. Univariate analysis and multivariable logistic regression determined features associated with optimal follow-up. Malignancy rate and time to malignancy detection were recorded.Results: Among 93,685 breast imaging examinations, 64,771 were from unique patients of which 2,967 had BI-RADS 3 findings (4.6%). Excluding patients with off-site index examinations and those with another breast examination <3 months from the index, 1,125 of 1,511 patients (74%) had optimal follow-up. In univariate and multivariable analysis, prior breast cancer was associated with optimal follow-up; younger age, Hispanic ethnicity, divorced status, and lack of insurance were associated with not having optimal follow-up. Malignancy rate was 0.86%, and mean time to detection was 330 days.Discussion: Follow-up of BI-RADS 3 breast imaging findings is optimal in only 74% of women. Further interventions to promote follow-up should target younger, unmarried women, those with Hispanic ethnicity, and women without history of breast cancer and without insurance coverage. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: This study uses a three-dimensional energy Doppler technique combined with the Virtual Organ Computer-aided Analysis (VOCAL) method in order to determine the diagnostic threshold of blood flow index in breast tumors to provide a reference for evaluation and treatment options.Materials and Methods: We collected 322 solid lesions which had been operated. Each lesion met the definite pathological diagnosis; collected lesions included 262 cases of benign lesions and 60 cases of malignant lesions. All examinations were performed by using GE LOGIQ E9 with VOCAL software. Volume and four distinct vascular indices of gray mean (MG), power mean, ratio (R), and vascular flow index (VFI) were calculated by using the VOCAL software. Sampling and calculation were repeated three times and the mean value was calculated.Results: The average age and power of the malignant group were greater than those of the benign group, ie p < .01 which had significant differences. The gray mean of the malignant group was lower than that of the benign group, ie p > .05 which had no significant differences between benign and malignant groups. The ratio, vascular flow index and volume had significant differences, i.e. p < .01. The area under the receiver operating characteristic curve (AUC) were 0.864, 0.830, 0.800, 0.758, and 0.764 for age, power, ratio, vascular flow index, and volume, respectively. The research indicators were higher than 50% of the curve showing their diagnostic value. The cut-off points of age, power, ratio, vascular flow index, and volume were 37.5, 26.56, 0.031, 0.846, and 1.75, respectively. Their corresponding sensitivity were 93.3%, 75%, 81.7%, 68.3%, 63.3%, and the specificity were 68.7%, 81%, 70.2%, 75.6%, and 81.7%, respectively. Comparison of vascular indices combined with the Breast imaging reporting and data System (BI-RADS) score and simple BI-RADS method, the AUC of power + BI-RADS, ratio + BI-RADS, VFI + BI-RADS, and BI-RADS alone are 0.928, 0.903, 0.895, and 0.796, respectively, which were higher than 50% of the curve. Sensitivity was 81.7%, 80%, 88.3%, 86.7%, and specificity was 88.5%, 85.5%, 77.1%, 69.5%, respectively. The power + BI-RADS method has the highest AUC among these three methods.Conclusions: Quantitative measurement of blood flow and blood vessel distribution in breast tumors by three-dimensional power Doppler ultrasound combined with the VOCAL method is more accurate and sensitive than the traditional two-dimensional ultrasound. And this method has potential promising applications in many current active research areas, such as the studies of random distribution of intratumoral blood vessels or the normalization of tumor blood vessels. Three-dimensional power Doppler ultrasound combined with the VOCAL method provides a new approach to achieving accurate judgments and the method evaluates the curative effect in breast cancer patients. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The Breast Imaging-Reporting and Data System (BI-RADS) atlas defines category 5 assessments as appropriate only for lesions that are almost certainly cancer, with a positive predictive value (PPV) of ≥95%. This study aims to demonstrate the feasibility of classifying lesions at diagnostic breast imaging with sufficiently high PPV to merit category 5 assessments, and to identify those lesion descriptors that yield such a high PPV.Materials and Methods: For this Health Insurance Portability and Accountability Act compliant and IRB exempt study, we reviewed diagnostic breast imaging examinations (mammography and/or ultrasound) assessed as highly suggestive of malignancy (BI-RADS category 5). Pathology diagnosis was considered the gold standard. PPV3 (biopsy performed) was calculated, and the BI-RADS descriptors for each lesion were analyzed.Results: Among 22,564 consecutive diagnostic breast imaging examinations between January 2010 and September 2015, we identified 239 exams (1.1%) assessed as BI-RADS category 5 (mean age 62.5 years). Malignancy (invasive breast carcinoma and/or ductal carcinoma in situ) was diagnosed in 233 examinations (PPV3 97.5% and 95% confidence interval: 96.2%-98.8%). The most common lesion types were mass (170) and calcifications (116). Of the 220 examinations involving both mammography and ultrasound, no category 5 lesions had <3 suspicious BI-RADS descriptors, only three lesions had three suspicious descriptors, but the remaining 217 lesions (98.6%) had ≥4 suspicious descriptors.Conclusion: In clinical practice, it is feasible to make BI-RADS category 5 assessments with the intended ≥95% PPV. To justify a category 5 assessment, at least four suspicious BI-RADS descriptors should be identified at the combination of diagnostic mammography and ultrasound examinations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Medical Imaging & Radiation Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: With much hype about artificial intelligence (AI) rendering radiologists redundant, a simple radiologist-augmented AI workflow is evaluated; the premise is that inclusion of a radiologist's opinion into an AI algorithm would make the algorithm achieve better accuracy than an algorithm trained on imaging parameters alone. Open-source BI-RADS data sets were evaluated to see whether inclusion of a radiologist's opinion (in the form of BI-RADS classification) in addition to image parameters improved the accuracy of prediction of histology using three machine learning algorithms vis-à-vis algorithms using image parameters alone.Materials and Methods: BI-RADS data sets were obtained from the University of California, Irvine Machine Learning Repository (data set 1) and the Digital Database for Screening Mammography repository (data set 2); three machine learning algorithms were trained using 10-fold cross-validation. Two sets of models were trained: M1, using lesion shape, margin, density, and patient age for data set 1 and image texture parameters for data set 2, and M2, using the previous image parameters and the BI-RADS classification provided by radiologists. The area under the curve and the Gini coefficient for M1 and M2 were compared for the validation data set.Results: The models using the radiologist-provided BI-RADS classification performed significantly better than the models not using them (P < .0001).Conclusion: AI and radiologist working together can achieve better results, helping in case-based decision making. Further evaluation of the metrics involved in predictor handling by AI algorithms will provide newer insights into imaging. [ABSTRACT FROM AUTHOR]

Copyright of Pesquisa Veterinaria Brasileira is the property of Colegio Brasileiro de Patologia Animal - CBPA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although recent technological advances, there is still discordance between mammography findings and pathologic diagnoses, especially for certain racial/ethnic populations. In this study we correlated the mammography BI-RADS categories with pathologic diagnoses, aiming to evaluate the performance of mammography in breast cancer detection in a unique poor population consisting of mostly Hispanics and African Americans. A total of 3935 female patients with a breast mammography and a subsequent breast pathology report within 90 days were retrospectively analyzed. There were 875 (22.2%) patients with a negative or probably benign mammography (BI-RADS 1, 2 and 3), and 33 (3.8%) of them had a malignant pathologic diagnosis. Patients with malignant pathologic diagnoses were older, higher in socioeconomic status (SES), and more likely to be African American or White, compared to those with non-malignant pathologic findings. They mostly presented with related symptoms (e.g. breast pain, mass or discharge) and/or family history or past history of breast cancers, which triggered secondary imaging examination and subsequent breast biopsy/excision, and eventually resulted to the diagnosis of breast cancers. In conclusion, our studies indicated that the performance of mammography is comparable in detection of breast cancers among Hispanics, African American and White populations, if it was done in the same facility. Our results also suggested that for patients with presenting symptoms, past history of breast cancer or strong family history of breast cancer, a secondary breast imaging examination may be warranted following a negative to probably benign mammography (BI-RADS 1–3). [ABSTRACT FROM AUTHOR]

Purpose: To determine women's perceptions of breast cancer risk and thresholds for desiring biopsy when considering BI-RADS 3 and 4A scenarios and recommendations, respectively.Materials and Methods: Women presenting for screening mammography from five geographically diverse medical centers were surveyed. Demographic information and baseline anxiety were queried. Participants were presented with scenarios of short-term imaging follow-up recommendations (ie, BI-RADS 3) and biopsy recommendations (ie, BI-RADS 4A) for low-risk mammographic abnormalities and asked to estimate their breast cancer risk for each scenario. Participants reported the threshold (ie, likelihood of cancer) where they would feel comfortable undergoing short-term imaging follow-up and biopsy and their anticipated regret for choosing short-term follow-up versus biopsy.Results: Analysis of 2,747 surveys showed that participants estimated breast cancer risk of 32.8% for a BI-RADS 3 and 41.1% for a BI-RADS 4A scenarios are significantly greater rates than clinically established rates (<2% [P < .001] and 2%-10% [P < .001], respectively). Over one-half (55.4%) of participants reported they would never want imaging follow-up if there was any chance of cancer; two-thirds (66.2%) reported they would desire biopsy if there was any chance of cancer. Participants reported greater anticipated regret (P < .001) and less relief and confidence (P < .001) with the decision to undergo follow-up imaging versus biopsy.Conclusion: Women overestimate breast cancer risk associated with both BI-RADS 3 and 4A scenarios and desire very low biopsy thresholds. Greater anticipated regret and less relief and confidence was reported with the choice to undergo short-term imaging follow-up compared with biopsy. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Dedicated breast computed tomography (DBCT) is an emerging and promising modality for breast lesions. The objective of this study was to evaluate the potential use of applying the BI-RADS Mammography Atlas 5th Edition for reporting and assessing breast lesions on DBCT. Currently, no atlas exists for DBCT.Materials and Methods: Four radiologists trained in breast imaging were recruited in this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study. The enrolled radiologists, who were blinded to mammographic and histopathologic findings, individually reviewed 30 randomized DBCT cases that contained marked lesions. Thirty-four lesions were included in this study: 24 (70.6%) masses, 7 (20.6%) calcifications, and 3 (8.8%) architectural distortions. Eight (23.5%) lesions were malignant and 26 (76.5%) were benign. The reader was asked to specify according to the BI-RADS Mammography Atlas for each marked DBCT lesion: primary findings, features, breast density, and final assessment. We calculated readers' diagnostic performances for differentiating between benign and malignant lesions and interobserver variability for reporting and assessing lesions using a generalized estimating equation and the Fleiss kappa (κ) statistic.Results: The estimated overall sensitivity of the readers was 0.969, and the specificity was 0.529. There were no significant differences in the sensitivity and the specificity between lesion types. For reporting the presence of a primary finding, the overall substantial agreement (κ = 0.70) was seen. In assigning the breast density and the final assessment, the overall agreement was moderate (κ = 0.53) and fair (κ = 0.30).Conclusion: The use of the BI-RADS Mammography Atlas 5th Edition for DBCT showed high performance and good agreement among readers. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: This study aimed to compare the diagnostic value of automated breast volume scanning (ABVS) to that of handheld ultrasonography (HHUS) using breast magnetic resonance imaging (MRI) as the gold standard.Materials and Methods: Twenty-eight patients with 39 examined breasts with at least one lesion visible in breast MRI underwent HHUS and ABVS. Detection rate, localization, maximum diameter, and Breast Imaging Reporting and Data System classification were compared. Sensitivity, specificity, diagnostic accuracy, positive predictive value, and negative predictive value were calculated for HHUS and ABVS. Lesion localization and maximum diameters based on HHUS and ABVS were compared to size measurement in MRI. Breast Imaging Reporting and Data System categories based on each method were compared to the MRI diagnosis (malignant or benign) or, if available (21 cases), with the histologic diagnosis.Results: MRI detected 72 lesions, ABVS 59 lesions, and HHUS 54 lesions. Malignancy was proven histopathologically in 15 cases. There was no significant difference between ABVS and HHUS in terms of sensitivity (93.3% vs. 100%), specificity (83.3% vs. 83.3%), diagnostic accuracy (87.2% vs. 89.7%), positive predictive value (77.8% vs. 78.9%), and negative predictive value (95.2% vs. 100%). Agreement regarding lesion localization (same quadrant) was 94.3% for ABVS and MRI and 91.2% for HHUS and MRI. Lesion size compared to MRI lesion size was assessed correctly (+/- 3 mm) in 79.4% (HHUS) and 80% (ABVS). The correlation of size measurement was slightly higher for ABVS-MRI (r = 0.89) than for HHUS-MRI (r = 0.82) with P < .001.Conclusions: ABVS can be used as an alternative to HHUS. ABVS has the advantage of operator independence and better reproducibility although it is limited in evaluating axillary lymph nodes and lacks Doppler or elastrography capabilities, which sometimes provide important supplementary information in HHUS. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The study aimed to determine the inter-observer agreement among academic breast radiologists when using the Breast Imaging Reporting and Data System (BI-RADS) lesion descriptors for suspicious findings on diagnostic mammography.Materials and Methods: Ten experienced academic breast radiologists across five medical centers independently reviewed 250 de-identified diagnostic mammographic cases that were previously assessed as BI-RADS 4 or 5 with subsequent pathologic diagnosis by percutaneous or surgical biopsy. Each radiologist assessed the presence of the following suspicious mammographic findings: mass, asymmetry (one view), focal asymmetry (two views), architectural distortion, and calcifications. For any identified calcifications, the radiologist also described the morphology and distribution. Inter-observer agreement was determined with Fleiss kappa statistic. Agreement was also calculated by years of experience.Results: Of the 250 lesions, 156 (62%) were benign and 94 (38%) were malignant. Agreement among the 10 readers was strongest for recognizing the presence of calcifications (k = 0.82). There was substantial agreement among the readers for the identification of a mass (k = 0.67), whereas agreement was fair for the presence of a focal asymmetry (k = 0.21) or architectural distortion (k = 0.28). Agreement for asymmetries (one view) was slight (k = 0.09). Among the categories of calcification morphology and distribution, reader agreement was moderate (k = 0.51 and k = 0.60, respectively). Readers with more experience (10 or more years in clinical practice) did not demonstrate higher levels of agreement compared to those with less experience.Conclusions: Strength of agreement varies widely for different types of mammographic findings, even among dedicated academic breast radiologists. More subtle findings such as asymmetries and architectural distortion demonstrated the weakest agreement. Studies that seek to evaluate the predictive value of certain mammographic features for malignancy should take into consideration the inherent interpretive variability for these findings. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: This study aimed to determine whether a 2-day educational course using a condensed Breast Imaging Reporting and Data System (condensed BI-RADS) improved the accuracy of Ugandan healthcare workers interpreting breast ultrasound.Materials and Methods: The target audience of this intervention was Ugandan healthcare workers involved in performing, interpreting, or acting on the results of breast ultrasound. The educational course consisted of a pretest knowledge assessment, a series of lectures on breast imaging interpretation and standardized reporting using a condensed BI-RADS, and a posttest knowledge assessment. Participants interpreted 53 different ultrasound test cases by selecting the finding type, descriptors for masses, and recommendations. We compared the percent correct on the pretest and posttest based on occupation and training level.Results: Sixty-one Ugandan healthcare workers participated in this study, including 13 radiologists, 13 other physicians, 12 technologists, and 23 midlevel providers. Most groups improved in identifying the finding type (P < 0.05). All occupations showed improved use of descriptive terms for the shape and internal echogenicity of masses (P < 0.05). Most groups showed significant improvement in recommendations for normal and benign findings with a corresponding reduction in biopsy recommendations.Conclusions: Targeted breast ultrasound education using a condensed BI-RADS improved the interpretive performance of healthcare workers and was particularly successful in reducing the frequency of unnecessary biopsies for normal and benign findings. Multimodal educational efforts to improve accuracy and management of breast ultrasound findings may augment breast cancer early detection efforts in resource-limited settings. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The objective of our study was to calculate mammographic breast density with a fully automated volumetric breast density measurement method and to compare it to breast imaging reporting and data system (BI-RADS) breast density categories assigned by two radiologists. Materials and Methods: A total of 476 full-field digital mammography examinations with standard mediolateral oblique and craniocaudal views were evaluated by two blinded radiologists and BI-RADS density categories were assigned. Using a fully automated software, mean fibroglandular tissue volume, mean breast volume, and mean volumetric breast density were calculated. Based on percentage volumetric breast density, a volumetric density grade was assigned from 1 to 4. Results: The weighted overall kappa was 0.895 (almost perfect agreement) for the two radiologists' BI-RADS density estimates. A statistically significant difference was seen in mean volumetric breast density among the BI-RADS density categories. With increased BI-RADS density category, increase in mean volumetric breast density was also seen (P < 0.001). A significant positive correlation was found between BI-RADS categories and volumetric density grading by fully automated software (ρ = 0.728, P < 0.001 for first radiologist and ρ = 0.725, P < 0.001 for second radiologist). Pairwise estimates of the weighted kappa between Volpara density grade and BI-RADS density category by two observers showed fair agreement (κ = 0.398 and 0.388, respectively). Conclusions: In our study, a good correlation was seen between density grading using fully automated volumetric method and density grading using BI-RADS density categories assigned by the two radiologists. Thus, the fully automated volumetric method may be used to quantify breast density on routine mammography. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors.Materials and Methods: Our institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and to participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature, including demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to Breast Imaging Reporting and Data System (BI-RADS). We developed predictive models using logistic regression to determine the predictive ability of (1) demographic variables, (2) 10 selected genetic variants, or (3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross-validation, used this risk estimate to construct Receiver Operator Characteristic Curve (ROC) curves, and compared the area under the ROC curve (AUC) of each using the DeLong method.Results: The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants (P = 0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; P < 0.001) and the genetic model (AUC = .601; P < 0.001).Conclusions: BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy. [ABSTRACT FROM AUTHOR]

Introduction The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) classification was developed to risk stratify breast lesions and guide surgical management based on imaging. Previous studies validating BI-RADS for US do not include pediatric patients. Most pediatric breast masses present as palpable lesions and frequently undergo ultrasound, which is often accompanied with a BI-RADS classification. Our study aimed to correlate BI-RADS with pathology findings to assess applicability of the classification system to pediatric patients. Methods We performed a retrospective review of all patients who underwent excision of a breast mass at a single center from July 2010 to November 2013. We identified all patients who underwent preoperative ultrasound with BI-RADS classification. Demographic data, imaging results, and surgical pathology were analyzed and correlated. Results A total of 119 palpable masses were excised from 105 pediatric patients during the study period. Of 119 masses, 81 had preoperative ultrasound, and BI-RADS categories were given to 51 masses. Of these 51, all patients were female and the average age was 15.9 years. BI-RADS 4 was given to 25 of 51 masses (49%), and 100% of these lesions had benign pathology, the most common being fibroadenoma. Conclusions Treatment algorithm based on BI-RADS classification may not be valid in pediatric patients. In this study, all patients with a BI-RADS 4 lesion had benign pathology. BI-RADS classification may overstate the risk of malignancy or need for biopsy in this population. Further validation of BI-RADS classification with large scale studies is needed in pediatric and adolescent patients. [ABSTRACT FROM AUTHOR]

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Abstract: Bayes'' theorem has proven to be one of the cornerstones in medical decision making. It allows for the derivation of post-test probabilities, which in case of a positive test result become positive predictive values. If several test results are observed successively Bayes'' theorem may be used with assumed conditional independence of test results or with incorporated conditional dependencies. Herein it is examined whether radiographic image features should be considered conditionally independent diagnostic tests when post-test probabilities are to be derived. For this purpose the mammographic mass dataset from the UCI (University of California, Irvine) machine learning repository is analysed. It comprises the description of 961 (516 benign, 445 malignant) mammographic mass lesions according to the BI-RADS (Breast Imaging: Reporting and Data System) lexicon. Firstly, an exhaustive correlation matrix is presented for mammography BI-RADS features among benign and malignant lesions separately; correlation can be regarded as measure for conditional dependence. Secondly, it is shown that the derived positive predictive values for the conjunction of the two features “irregular shape” and “spiculated margin” differ significantly depending on whether conditional dependencies are incorporated into the decision process or not. It is concluded that radiographic image features should not generally be regarded as conditionally independent diagnostic tests. [Copyright &y& Elsevier]

Rationale and Objective: The objective of this study is to test the hypothesis that there are patterns in erroneous assessment of BI-RADS features among radiology trainees when interpreting mammographic masses and that these patterns can be captured in individualized statistical user models. Identifying these patterns could be useful in personalizing and adapting educational material to complement the individual weaknesses of each trainee during his or her mammography education. Materials and Methods: Reading data of 33 mammographic cases containing masses was used. The cases were individually described by 10 radiology residents using four BI-RADS features: mass shape, mass margin, mass density and parenchyma density. For each resident, an individual model was automatically constructed that predicts likelihood (HIGH or LOW) of erroneously assigning each BI-RADS descriptor by the resident. Error was defined as deviation of the resident''s assessment from the expert assessments. We evaluated the predictive performance of the models using leave-one-out crossvalidation. Results: The user models were able to predict which assessments have higher likelihood of error. The proportion of actual errors to the number of situations in which these errors could potentially occur was significantly higher (P < .05) when user-model assigned HIGH likelihood of error than when LOW likelihood of error was assigned for three of the four BI-RADS features. Overall, the difference between the HIGH and LOW likelihood of error groups was statistically significant (P < .0001) combining all four features. Conclusion: Error making in BI-RADS descriptor assessment appears to follow patterns that can be captured with statistical pattern recognition-based user models. [Copyright &y& Elsevier]

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Copyright of Radiologia Brasileira is the property of Radiologia Brasileira and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Radiologia Brasileira is the property of Radiologia Brasileira and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Radiologia Brasileira is the property of Radiologia Brasileira and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Radiologia Brasileira is the property of Radiologia Brasileira and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Breast Imaging Reporting and Data System® (BI-RADS®) initiative, instituted by the ACR, was begun in the late 1980s to address a lack of standardization and uniformity in mammography practice reporting. An important component of the BI-RADS initiative is the lexicon, a dictionary of descriptors of specific imaging features. The BI-RADS lexicon has always been data driven, using descriptors that previously had been shown in the literature to be predictive of benign and malignant disease. Once established, the BI-RADS lexicon provided new opportunities for quality assurance, communication, research, and improved patient care. The history of this lexicon illustrates a series of challenges and instructive successes that provide a valuable guide for other groups that aspire to develop similar lexicons in the future. [Copyright &y& Elsevier]

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Rationale and Objectives: To investigate the feasibility using quantitative morphology/texture features of breast lesions for diagnostic prediction, and to explore the association of computerized features with lesion phenotype appearance on magnetic resonance imaging. Materials and Methods: Forty-three malignant/28 benign lesions were used in this study. A systematic approach from automated lesion segmentation, quantitative feature extraction, diagnostic feature selection using an artificial neural network (ANN), and lesion classification was carried out. Eight morphologic parameters and 10 gray level co-occurrence matrix texture features were obtained from each lesion. The diagnostic performance of selected features to differentiate between malignant and benign lesions was analyzed using receiver-operating characteristic analysis. Results: Six features were selected by an ANN using leave-one-out cross validation, including compactness, normalized radial length entropy, volume, gray level entropy, gray level sum average, and homogeneity. The area under the receiver-operating characteristic curve was 0.86. When dividing the database into half training and half validation set, a classifier of five features selected in the half training set achieved an area under the curve of 0.82 in the other half validation set. The selected morphology feature “compactness” was associated with shape and margin in the Breast Imaging Reporting and Data System lexicon, round shape and smooth margin for the benign lesions, and more irregular shape for the malignant lesions. The selected texture features were associated with homogeneous/heterogeneous patterns and the enhancement intensity. The malignant lesions had higher intensity and broader distribution on the enhancement histogram (more heterogeneous) compared to the benign lesions. Conclusion: Quantitative analysis of morphology/texture features of breast lesions was feasible, and these features could be selected by an ANN to form a classifier for differential diagnosis. Establishing the link between computer-based features and visual descriptors defined in the BI-RADS lexicon will provide the foundation for the acceptance of quantitative diagnostic features in the development of computer-aided diagnosis. [Copyright &y& Elsevier]

Summary: Our objective was to determine the interobserver variability of breast density assessment according to the Breast Imaging Reporting and Data System (BI-RADS) and to examine potential associations between breast density and risk factors for breast cancer. Four experienced breast radiologists received instructions regarding the use of BI-RADS and they assessed 57 mammograms into BI-RADS density categories of 1–4. The weighted kappa values for breast density between pairs of observers were 0.84 (A, B) (almost perfect agreement); 0.75 (A, C), 0.74 (A, D), 0.71 (B, C), 0.77 (B, D), 0.65 (C, D) (substantial agreement). The weighted overall kappa, measured by the intraclass correlation coefficient (ICC), was 0.77 (95% CI: 0.69–0.85). Body mass index was inversely associated with high breast density. In conclusion, overall interobserver agreement in mammographic interpretation of breast density is substantial and therefore, the BI-RADS classification for breast density is useful for standardization in a multicentre study. [Copyright &y& Elsevier]

Rationale and Objectives: Based on the definitions in mass category of Breast Imaging Reporting and Data System developed by American College of Radiology, eight computerized features including shape, orientation, margin, lesion boundary, echo pattern, and posterior acoustic feature classes are proposed. Materials and Methods: Our experimental database consists of 265 pathology-proven cases including 180 benign and 85 malignant masses. The capacity of each proposed feature in differentiating malignant from benign masses was validated by Student's t test and the correlation between each proposed feature and the pathological result was evaluated by point biserial coefficient. Binary logistic regression model was used to relate all proposed features and pathological result as a computer-aided diagnosis (CAD) system. The diagnostic value of each proposed feature in the CAD system was further evaluated by the feature selection methods. Additionally, the likelihood of malignancy for each individual feature was also estimated by binary logistic regression. Results: On each proposed feature, the malignant cases were significantly different from the benign ones. The correlation between the angular characteristic and pathological result was indicated as very high. Three substantial correlations appear in features irregular shape, undulation characteristic, and degree of abrupt interface, but the relationship for orientation feature is low. For the constructed CAD system, the performance indices accuracy, sensitivity, specificity, PPV, and NPV were 91.70% (243 of 265), 90.59% (77 of 85), 92.22% (166 of 180), 84.62% (77 of 91), and 95.40% (166 of 174), respectively, and the area index in the ROC analysis was 0.97. Compared with the significant contribution of angular characteristic, the diagnostic values of posterior acoustic feature and orientation feature were relatively low for the CAD system. When three or more angular characteristics are discovered or the degree of abrupt interface is lower than 18, the likelihood of malignancy could be predicted as greater than 40%. Conclusion: The computerized BI-RADS sonographic features conform to the sign of malignancy in the clinical experience and efficiently help the CAD system to diagnose the mass. [ABSTRACT FROM AUTHOR]

Summary: Initial diagnosis and treatment of women with breast cancer is based on the imaging findings. Anecdotal experience suggests that the quality of breast imaging reports is variable; however, systematic evaluation of the content of reports has not been documented to date. We present an audit of the breast imaging reports of all new breast cancer cases referred to a multidisciplinary breast centre during 2004, based on 244 imaging reports from 253 cases. We focus on the quality of imaging reports from the perspective of completeness, concordance with standards, and provision of information considered relevant to clinical decision-making. The audit shows that many reports do not provide key information, and that there are substantial variations in the quality of reports between breast screening services (as part of a coordinated national programme) and community-based radiology services. About one-quarter of all reports do not provide an imaging diagnosis, and only half of all imaging reports are concordant with standards for structured reporting. The least reported variables were breast density category (reported in 24%), lesion depth (37%), lesion shape (55% for mammography, 39% for ultrasound), and location (59%). The most frequently provided information was mammography lesion type (99.6%), sonographic lesion size (90.4%), and recommendation for further investigation (89%). The vast majority of reports from screening services used structured reporting, and these were more likely to provide the information recommended in standards than were reports from community-based radiologists. This work indicates that the quality (content and completeness) of breast imaging reports, particularly community-based radiology reports, is not in line with standards. The clinical implications of these findings warrant further study. [Copyright &y& Elsevier]

Rationale and Objectives: Quantitative criteria for the Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories have recently been defined as <25% dense for almost entirely fatty, 25%–50% dense for scattered fibroglandular densities, 51%–75% for heterogeneously dense, and >75% dense for the extremely dense category. The purpose of this study is to compare the range of percent mammographic densities with radiologist-assigned BI-RADS mammographic density categories and compare with the recently issued definitions. Materials and Methods: In this study, 200 consecutive negative analog screening mammograms were assigned BI-RADS mammographic density categories independently by three radiologists blinded to the other readers’ density assignment. Quantitative assessment of percent mammographic density was performed using previously validated software. Results: All three readers agreed on BI-RADS mammographic density categories in 98 cases (49%), and two of three readers agreed in all 200 cases. Using two reader’s consensus, median mammographic density (range) was 6.0% (0.5%–19.2%) for fatty, 14.8% (1.2%–52.7%) for scattered densities, 51.2% (15.9%–82.2%) for heterogeneously dense, and 78.4% (60.1%–87.9%) for extremely dense breasts. The percent mammographic density ranges for fatty and extremely dense breasts correlated well with BI-RADS definitions, whereas the ranges of densities in the scattered and heterogeneously dense categories were considerably broader. Conclusion: Fatty and extremely dense BI-RADS categories compare relatively well to defined criteria, and therefore may be helpful in breast cancer risk models. Scattered fibroglandular densities and heterogeneously dense categories have broad percent mammographic density ranges and may not function well in breast cancer risk models. [Copyright &y& Elsevier]

Purpose The aim of this study was to evaluate the intradevice and interdevice reliability of four alternatives for telemammography—computed radiography, printed film, a film digitizer, and a digital camera—in terms of interpretation agreement when using the BI-RADS ® lexicon. Methods The ethics committee of the authors’ institution approved this retrospective study. A factorial design with repeated measures with 1,960 interpretations was used (70 patients, seven radiologists, and four devices). Reliability was evaluated using the κ coefficient for intradevice and interdevice agreement on malignancy classification and on BI-RADS final assessment category. Results Agreement on malignancy classification was higher than agreement for BI-RADS final assessment category. Interdevice agreement on malignancy classification between the film digitizer and computed radiography was ranked as almost perfect ( P < .001), whereas interdevice agreement for the other alternatives was ranked as substantial ( P < .001), with observed agreement ranging from 85% to 91% and κ values ranging from 0.70 to 0.81. Interdevice agreement on BI-RADS final assessment category was ranked as substantial or moderate ( P < .001), with observed agreement ranging from 64% to 77% and κ values ranging from 0.52 to 0.69. Interdevice agreement was higher than intradevice agreement. Conclusions The results of this study show very high interdevice agreement, especially for management recommendations derived from malignancy classification, which is one of the most important outcomes in screening programs. This study provides evidence to suggest the interchangeability of the devices evaluated, thereby enabling the provision of low-cost medical imaging services to underserved populations. [ABSTRACT FROM AUTHOR]

Abstract: As national health systems seek to apply breast cancer screening recommendations to an entire population of women (within target age ranges for which there is evidence that screening reduces mortality), the volume of screening tests and resulting diagnostic investigations arising from abnormal test results—and the cost associated with them—will grow dramatically. Population-based early detection (screening) programs will need information systems and management tools to help these programs. This report describes Brazil’s highly decentralized health care system and then describes in greater detail how the development and implementation of an information system for Brazil’s nationwide breast cancer early detection program was carried out with input from various stakeholders. Challenges encountered in the implementation are shared. Preliminary findings from the first 1.5 million mammograms are presented to demonstrate the kind of provocative management information such a system can yield in a relatively short period of time. The potential of such information systems for improving efficiency, efficacy and cost-effectiveness of early detection programs is emphasized. [Copyright &y& Elsevier]