Breast Imaging Reporting And Data System Assessment Research Articles

Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment.

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis, in which real-time or near real-time diagnosis with high accuracy is desired. However, DOT's relatively slow data processing and image reconstruction speeds have hindered real-time diagnosis. Here, we propose a real-time classification scheme that combines US breast imaging reporting and data system (BI-RADS) readings and DOT frequency domain measurements. A convolutional neural network is trained to generate malignancy probability scores from DOT measurements. Subsequently, these scores are integrated with BI-RADS assessments using a support vector machine classifier, which then provides the final diagnostic output. An area under the receiver operating characteristic curve of 0.978 is achieved in distinguishing between benign and malignant breast lesions in patient data without image reconstruction.

A bimodal nomogram as an adjunct tool to reduce unnecessary breast biopsy following discordant ultrasonic and mammographic BI-RADS assessment.

To develop a bimodal nomogram to reduce unnecessary biopsies in breast lesions with discordant ultrasound (US) and mammography (MG) Breast Imaging Reporting and Data System (BI-RADS) assessments. This retrospective study enrolled 706 women following opportunistic screening or diagnosis with discordant US and MG BI-RADS assessments (where one assessed a lesion as BI-RADS 4 or 5, while the other assessed the same lesion as BI-RADS 0, 2, or 3) from two medical centres between June 2019 and June 2021. Univariable and multivariable logistic regression analyses were used to develop the nomogram. DeLong's and McNemar's tests were used to assess the model's performance. Age, MG features (margin, shape, and density in masses, suspicious calcifications, and architectural distortion), and US features (margin and shape in masses as well as calcifications) were independent risk factors for breast cancer. The nomogram obtained an area under the curve of 0.87 (95% confidence interval (CI), 0.83-0.91), 0.91 (95% CI, 0.87 - 0.96), and 0.92 (95% CI, 0.86-0.98) in the training, internal validation, and external testing samples, respectively, and demonstrated consistency in calibration curves. Coupling the nomogram with US reduced unnecessary biopsies from 74 to 44% and the missed malignancies rate from 13 to 2%. Similarly, coupling with MG reduced missed malignancies from 20 to 6%, and 63% of patients avoided unnecessary biopsies. Interobserver agreement between US and MG increased from - 0.708 (poor agreement) to 0.700 (substantial agreement) with the nomogram. When US and MG BI-RADS assessments are discordant, incorporating the nomogram may improve the diagnostic accuracy, avoid unnecessary breast biopsies, and minimise missed diagnoses. The nomogram developed in this study could be used as a computer program to assist radiologists with detecting breast cancer and ensuring more precise management and improved treatment decisions for breast lesions with discordant assessments in clinical practice. • Coupling the nomogram with US and mammography improves the detection of breast cancers without the risk of unnecessary biopsy or missed malignancies. • The nomogram increases mammography and US interobserver agreement and enhances the consistency of decision-making. • The nomogram has the potential to be a computer program to assist radiologists in identifying breast cancer and making optimal decisions.

Solitary Dilated Ducts Revisited: Malignancy Rate and Implications for Management

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.

A breast cancer (BC) risk model incorporating Tyrer-Cuzick version 8 (TCv8) and a polygenic risk score (PRS) for diverse ancestries.

557 Background: BC risk assessment is important for guiding personalized screening and risk-reducing interventions. TCv8 is used to estimate BC risk based on age, breast density, family cancer history and other clinical factors. Accuracy may be improved by combining TCv8 with a PRS. We developed and validated a PRS for diverse ancestries based on 149 common genetic variants (PRS-149) comprised of 56 ancestry-informative and 93 BC-associated variants. Here, we describe a BC risk model that combines PRS-149 with TCv8. Methods: Subjects had multigene panel testing for hereditary cancer and were negative for pathogenic variants in known BC susceptibility genes. A combined risk score (CRS), incorporating PRS-149 and TCv8, was developed based on 189,230 women, including 43,444 (23%) with a history of BC. Breast Imaging Reporting and Data System (BI-RADS) breast density measurements were available for 12,363 women. We used multivariable logistic regression to test breast density and PRS-149 for association with risk of BC. An independent test cohort of 6,030 BC-unaffected women with BI-RADS assessment was used to evaluate the effect of PRS-149 on risk stratification. Relative contributions of family history, breast density, other clinical factors in TCv8 and PRS-149 were examined by adding terms sequentially to an ANOVA model. We compared differences in classification of women as high (20%) or low/moderate (20%) remaining lifetime risk according to TCv8 versus CRS. Results: In the development cohort, increased breast density was significantly associated with higher risk of BC (p=3.0x10-6) with an effect size consistent with TCv8. PRS-149 improved BC risk prediction over age, breast density and family history (OR per unit standard deviation: 1.41, 95% CI: 1.37 – 1.46; p: 1.8x10-105). PRS-149 was weakly but significantly correlated with both family history (r=0.09) and breast density (r=0.01). After adjusting for multiple testing, no other factors were correlated with PRS-149. In the independent test cohort, PRS-149 explained 27% of CRS variability after accounting for family history, breast density and other clinical factors. Adding PRS-149 to TCv8 significantly altered risk estimates, with 16.3% (983/6,030) of patients classified differently by CRS versus TCv8. By TCv8 alone, 38.0% (2,289/6,030) of patients were classified as high-risk. Among patients who were high-risk by TCv8, 25.2% (576/2,289) were downgraded by CRS. Conclusions: This is the first BC risk model that includes breast density, family history, and a PRS based on genetically determined ancestry that is validated for diverse populations. Addition of PRS-149 improved risk prediction and substantially modified risk stratification compared to TCv8 alone. Implementation of CRS may therefore lead to improved identification of women who are likely to benefit from increased surveillance and preventive medications.

Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist.

Background parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assessment tools have been developed but none has surpassed radiologist BPE designations. To develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations. Retrospective. Consecutive high-risk patients (i.e. &gt;20% lifetime risk of breast cancer) who underwent contrast-enhanced screening breast MRI from October 2013 to January 2019. The study included 5224 breast MRIs, divided into 3998 training, 444 validation, and 782 testing exams. On radiology reports, 1286 exams were categorized as high BPE (i.e., marked or moderate) and 3938 as low BPE (i.e., mild or minimal). A 1.5 T or 3T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging. Breast MRIs were used to develop two deep learning models (Slab artificial intelligence (AI); maximum intensity projection [MIP] AI) for BPE categorization using radiology report BPE labels. Models were tested on a heldout test sets using radiology report BPE and three-reader averaged consensus as the reference standards. Model performance was assessed using receiver operating characteristic curve analysis. Associations between high BPE and BI-RADS assessments were evaluated using McNemar's chi-square test (α*=0.025). The Slab AI model significantly outperformed the MIP AI model across the full test set (area under the curve of 0.84 vs. 0.79) using the radiology report reference standard. Using three-reader consensus BPE labels reference standard, our AI model significantly outperformed radiology report BPE labels. Finally, the AI model was significantly more likely than the radiologist to assign "high BPE" to suspicious breast MRIs and significantly less likely than the radiologist to assign "high BPE" to negative breast MRIs. Fully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports. 4 TECHNICAL EFFICACY STAGE: 3.

Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists' Diagnosis Performance? An Observer Study.

Radiologists’ diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists. This study aims to evaluate the impact of a CAD method based on perceptive features learned from quantitative BI-RADS descriptions on breast mass diagnosis performance. We conducted a retrospective multi-reader multi-case (MRMC) study to assess the perceptive feature-based CAD method. A total of 416 digital mammograms of patients with breast masses were obtained from 2014 through 2017, including 231 benign and 185 malignant masses, from which we randomly selected 214 cases (109 benign, 105 malignant) to train the CAD model for perceptive feature extraction and classification. The remaining 202 cases were enrolled as the test set for evaluation, of which 51 patients (29 benign and 22 malignant) participated in the MRMC study. In the MRMC study, we categorized six radiologists into three groups: junior, middle-senior, and senior. They diagnosed 51 patients with and without support from the CAD model. The BI-RADS category, benign or malignant diagnosis, malignancy probability, and diagnosis time during the two evaluation sessions were recorded. In the MRMC evaluation, the average area under the curve (AUC) of the six radiologists with CAD support was slightly higher than that without support (0.896 vs. 0.850, p = 0.0209). Both average sensitivity and specificity increased (p = 0.0253). Under CAD assistance, junior and middle-senior radiologists adjusted the assessment categories of more BI-RADS 4 cases. The diagnosis time with and without CAD support was comparable for five radiologists. The CAD model improved the radiologists’ diagnostic performance for breast masses without prolonging the diagnosis time and assisted in a better BI-RADS assessment, especially for junior radiologists.

Can Ultrasound Elastography Help Better Manage Mammographic BI-RADS Category 4 Breast Lesions?

BackgroundTo assess the performance of conventional ultrasound (US) combined with strain elastography (SE) in the Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions on mammography. Materials and MethodsWomen with breast lesions identified as having mammography BI-RADS 4 lesions and underwent US examination were included in China. US features and US BI-RADS assessment were recorded in real-time and prospectively reported. The pathological result was referred to as the gold standard. The performance of US in the mammographic BI-RADS category 4 lesions was evaluated. Diagnostic performances of US BI-RADS, SE and combined both were compared. ResultsA total of 751 women with 751 breast lesions classified as mammographic BI-RADS category 4 were included. For mammographic findings, 530 (70.6%) were true positive and 221 (29.4%) were false positive. Conventional US achieved higher positive predictive value (PPV) than mammography (78.5% vs. 70.6%, P=.001). The specificity increased from 34.4% to 47.1% (P< .001) without any loss in sensitivity and the PPV increased to 81.9% (P = .122) when conventional US was used in combination with SE. For conventional US combined with SE, it led to a correct diagnosis of no breast cancer in 104 of the 221 false-positive findings (47.1%) and achieved higher PPV than mammography regardless of patient age and lesion size. ConclusionConventional US combined with SE is a helpful tool for the noninvasive examination of breast lesions classified as BI-RADS category 4 on mammography. It helped increase the PPV and had the potential to avoid unnecessary biopsies of BI-RADS category 4 lesions detected on mammography.

EVALUATION OF DIAGNOSTIC ACCURACY OF ULTRASOUND-GUIDED SEMI-AUTOMATED CORE NEEDLE BIOPSY OF BREAST SUSPICIOUS MICROCALCIFICATIONS

Back ground: Breast microcalcifications are considered an early mammographic sign of breast cancer which are present with tiny bright spots of different morphology. In an ultrasound (US) image, the presence of microcalcifications within breast is an important indicator of malignancy.With an improved sonographic detection of microcalcification, ultrasound could be used for biopsy guidance for target sampling of tissue containing suspicious microcalcifications. If the biopsied lesions had a suspicious microcalcifications, specimen radiograph is mandatory to confirm the presence of microcalcifications in which a core containing any calcification on specimen radiography was defined as asuccessfully retrieved core. 14-G(Gauge) semi-automated core needle biopsy(CNB) device is less costly than other biopsy device. Methods:A prospective study was conducted on thirty female patients with thirty- two lesions their mean age was 48.53 years (age range, 21-75 years) radiologically classified as BIRADS (Breast Imaging Reporting and Data system) 4 (b, c) and5 from those referred to women imaging health unit in Radiology department for Sono- Mammography and those patients were referred from the inpatient wards and outpatient clinics of the surgery departments during the period from September 2018 till May 2019.All patients underwent conventional digital mammography and B-mode ultrasound examination and Ultrasound guided biopsy, specimen radiography then tissue samples were sent to histopathology department in which slide picture was done.Then (28) females done surgery and (2) patient still for follow up. Results: Radiological diagnosis of our lesions using BIRADS(Breast Imaging Reporting and Data system) categorical method were identified as two intermediate suspicious lesions (BIRADS 4B 6.3 %), twelve moderate suspicious lesions (BIRADS 4C 34.4 %), and 19 highly suggestive of malignancy lesions (BIRADS 5 59.4 %). Retrieval rate of calcifications according to specimen radiography were successfully retrieved in 30/32 lesions (93.8 %) and confirmed on specimen radiography .CNB( core needle biopsy) identified 28 lesions (87.5 %) malignant and four (12.5 %) benign lesions. Distribution of lesions according to pathological diagnosis in successful and fail retrieval group, the successful retrieval group comprised 26 malignant (22 IDC(Invasive ductal carcinoma), 2 DCIS(Ductal carcinoma Insitu), 2 ILC(Invasive Lobuler carcinoma) ) and four benign lesions.After CNB( core needle biopsy), 30 lesions underwent surgical excision and final pathology results were provided. All 24 IDC on 14-G semi-automated CNB (core needle biopsy) were confirmed to be IDC(Invasive ductal carcinoma) on final pathology. Of the two DCIS(Ductal carcinoma Insitu) on US(Ultrasound) guided CNB( core needle biopsy), one DCIS, were upgraded to IDC, the other one confirmed to be DCIS on final pathology,the two ILC were confirmed to be ILC on final pathology.Of the four benign lesions, the two ADH(Atypical Ductal hyperplasia) underwent surgical excision, finally diagnosed as DCIS,the other two benign lesions (fibroadenoma and fibroadenosis) their BIRADS assessments were category 4C and were recommended for follow-up.The overall accuracy of US-guided 14-G(Gauge) semi-automated CNB( core needle biopsy) was 90.0 % (27/32). Conclusion: The present study confirms the good retrieval rate (93.8%) of US-guided 14-G semi-automated CNB and diagnostic accuracy of (90.0%). Thus, this could be useful procedure for suspicious microcalcifications apparent on sonography.Specimen radiography proved to be a great help in deciding the accurate retrieval of microclcifications by core biopsy. It should be done in all cases of core biopsy for calcifications with marking of the core containing calcifications to be specially assessed by histopathology. We should try to do core biopsy before using VAB (Vaccum-Assisted Biobsy)for calcifications if well seen by ultrasound.

Utility of ultrasound strain elastography to differentiate benign from malignant lesions of the breast

Background:The purpose of this study was to determine the utility and diagnostic performance of strain elastography (SE) in differentiating benign from malignant lesions of the breast.Methods:In this prospective study, 50 palpable breast masses in 50 patients were examined by mammography, B-mode ultrasound (US) and SE. Lesions were categorized using Breast Imaging Reporting and Data System (BIRADS) scoring based on mammographic and sonographic features. Elasticity scores were assessed on a five-point scale based on the distribution of strain, and the lesion size on SE imaging and B-mode (elasticity imaging/B mode [EI/B] ratio) was compared. Findings were correlated with the BIRADS assessment and diagnostic performance of sonoelastography was evaluated taking histopathology as reference standard.Results:Histopathology revealed 29 (58%) malignant and 21 (42%) benign lesions. Infiltrative ductal carcinoma and fibroadenoma were the most common malignant and benign lesions, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of SE was 100%, 76.1%, 85.2%, 100%, and 90%, respectively. Higher elasticity score was significantly associated with malignant histopathology (P < 0.00001). The mean EI/B ratio for malignant lesions was 1.36 ± 0.24 while that of benign lesions was 1.03 ± 0.30 (P = 0.000).Conclusion:Real-time SE of the breast, with its superior sensitivity and specificity, could provide improved characterization of benign and malignant breast masses compared with mammography and conventional US. Due to greater diagnostic accuracy, SE can be an effective adjunctive tool to B-mode US in predicting malignancy of breast, as well as in reducing the need for biopsies in benign breast lesions.

Correlation Between 18F-FDG PET/CT Findings and BI-RADS Assessment Using Ultrasound in the Evaluation of Breast Lesions: A Multicenter Study

To analyze the correlation between ultrasound breast imaging reporting and data system (BI-RADS) category and fluorodeoxyglucose [18F] (18F-FDG) positron emission tomography/computed tomography (PET/CT) findings and their value in breast lesion diagnosis. Cases involving hypermetabolic lesions identified by 18F-FDG PET/CT and ultrasound were retrospectively analyzed. The correlation between the maximum standardized uptake values (SUVmax) of the lesions and the BI-RADS grades was calculated. Histologic diagnosis or evidence at the end of a 2-year follow-up as the standard of truth were analyzed to determine the sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) of the diagnostic methods. Area under the curve (AUC) of BI-RADS, SUVmax, and BI-RADS/SUVmax combined were obtained using receiver-operating characteristic curve (ROC) analysis. Of 206 cases, 92 were benign and 114 were malignant. The difference between the SUVmax and the BI-RADS grades was statistically significant (p < 0.001). The critical value of the optimal SUVmax was 2.325, and the accuracy, sensitivity, specificity, PPV, and NPV were 84.5%, 91.2%, 76.1%, 82.5%, and 87.5%, respectively. For diagnosis using BI-RADS, these values were 85.9%, 98.2%, 70.7%, 80.6%, and 97.0%, respectively. ROC analysis of 206 breast lesions for distinguishing benign from malignant lesions yielded AUCs of 0.948, 0.896, and 0.977 for BI-RADS, SUVmax, and BI-RADS/SUVmax combined, respectively. The critical value of the optimal SUVmax in grade 3 and 4 lesions (as determined using BI-RADS) was 2.705, and the accuracy, sensitivity, specificity, PPV, and NPV were 82.6%, 77.8%, 85.7%, 77.8%, and 85.7%, respectively. For diagnosis using BI-RADS in cases with grade 3 and 4 lesions, these values were 68.5%, 94.4%, 51.8%, 55.7%, and 93.5%, respectively. In ROC analysis for distinguishing benign from malignant for BI-RADS grade 3-4 lesions, the AUC of BI-RADS, SUVmax, and BI-RADS/SUVmax combined were 0.731, 0.859, and 0.882, respectively. Both 18F-FDG PET/CT and ultrasound-dependent BI-RADS grading are effective for diagnosing breast lesions. However, in cases of BI-RADS grades 3 and 4, 18F-FDG PET/CT has better specificity and may be useful for further differential diagnosis.

Analysis of Ordinal Logistic Regression Model on Breast Cancer Diagnosis by Birads Mammography

The right diagnosis is needed for appropriate therapy. The diagnosis of breast cancer is quite ambiguous and requires high accuracy. Mammography is a method of diagnosing breast cancer using BIRADS (Breast Imaging-Reporting and Data System) assessment. This study aimed to assess the accuracy of BIRADS classification in the diagnosis of breast cancer and predictors that influence it through a logistic regression model test. The research method was cross sectional study by collecting data from the results of mammography examinations obtained from Medical Record documents, SIRS (Hospital Information Systems), and the radiologist's expertise of mammography. The data came from 47 hospital breast cancer patients that contained information on potential predictors of breast cancer namely tumor location, metastases, age, weight, and education. Logistic regression model analysis was performed to find the best statistical test model for breast cancer diagnosis classification based on BIRADS assessment. The diagnosis classification of BIRADS was consisting of normal, benign, and malignant grades. For this reason, hypothesis testing was conducted with G test for simultaneous model testing. Then, a development of an appropriate logit model by using a partial test. Followed by conducting a suitability and feasibility test model with the Goodness of Fit using the Hosmer-Lemeshow Test. The results of the analysis revealed that the ordinal logistic regression was the best model of BIRADS classification diagnosis with an accuracy value of 52.5%. The result of ordinal logistic regression model for malignant breast cancer:

A Prospective Study on the Value of Ultrasound Microflow Assessment to Distinguish Malignant from Benign Solid Breast Masses: Association between Ultrasound Parameters and Histologic Microvessel Densities.

ObjectiveTo investigate the value of ultrasound (US) microflow assessment in distinguishing malignant from benign solid breast masses as well as the association between US parameters and histologic microvessel density (MVD).Materials and MethodsNinety-eight breast masses (57 benign and 41 malignant) were examined using Superb Microvascular Imaging (SMI) and contrast-enhanced US (CEUS) before biopsy. Two radiologists evaluated the quantitative and qualitative vascular parameters on SMI (vascular index, morphology, distribution, and penetration) and CEUS (time-intensity curve analysis and enhancement characteristics). US parameters were compared between benign and malignant masses and the diagnostic performance was compared between SMI and CEUS. Subgroup analysis was performed according to lesion size. The effect of vascular parameters on downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4A masses was evaluated. The association between histologic MVD and US parameters was analyzed.ResultsMalignant masses were associated with a higher vascular index (15.1 ± 7.3 vs. 5.9 ± 5.6), complex vessel morphology (82.9% vs. 42.1%), central vascularity (95.1% vs. 59.6%), penetrating vessels (80.5% vs. 31.6%) on SMI (all, p < 0.001), as well as higher peak intensity (37.1 ± 25.7 vs. 17.0 ± 15.8, p < 0.001), slope (10.6 ± 11.2 vs. 3.9 ± 4.2, p = 0.001), area (1035.7 ± 726.9 vs. 458.2 ± 410.2, p < 0.001), hyperenhancement (95.1% vs. 70.2%, p = 0.005), centripetal enhancement (70.7% vs. 45.6%, p = 0.023), penetrating vessels (65.9% vs. 22.8%, p < 0.001), and perfusion defects (31.7% vs. 3.5%, p < 0.001) on CEUS (p ≤ 0.023). The areas under the receiver operating characteristic curve (AUCs) of SMI and CEUS were 0.853 and 0.841, respectively (p = 0.803). In 19 masses measuring < 10 mm, central vascularity on SMI was associated with malignancy (100% vs. 38.5%, p = 0.018). Considering all benign SMI parameters on the BI-RADS assessment, unnecessary biopsies could be avoided in 12 category 4A masses with improved AUCs (0.500 vs. 0.605, p < 0.001). US vascular parameters associated with malignancy showed higher MVD (p ≤ 0.016). MVD was higher in malignant masses than in benign masses, and malignant masses negative for estrogen receptor or positive for Ki67 had higher MVD (p < 0.05).ConclusionUS microflow assessment using SMI and CEUS is valuable in distinguishing malignant from benign solid breast masses, and US vascular parameters are associated with histologic MVD.

An Abbreviated Protocol for High-risk Screening Breast Magnetic Resonance Imaging: Impact on Performance Metrics and BI-RADS Assessment

Annual breast magnetic resonance imaging (MRI) is recommended to screen high-risk populations for breast cancer, although costs are significant. This study assesses the performance of an abbreviated MRI protocol as a resource-efficient approach for screening patients at high-risk of breast cancer, and assesses whether the abbreviated protocol alters the assigned Breast Imaging Reporting and Data System (BI-RADS) category. This is a prospective paired cohort study performed in an academic ambulatory setting. MRI images of women at high risk of breast cancer were reviewed using an abbreviated MRI protocol, followed by an immediate review of additional sequences included in a full diagnostic protocol. BI-RADS assessments, including all changes and interpretation times, were recorded for both the abbreviated and full protocol reviews. Cancer detection rate, positive predictive value 3 (PPV3), sensitivity, and specificity were calculated. A total of 1052 MRI cases were reviewed. The cancer detection rate was 13.3 per 1000 with a PPV3 of 30.4% based on the full protocol. Review of sequences included in the full protocol resulted in a change in the final BI-RADS assessments in 3.4% of the cases, the majority of which did not change clinical management with respect to biopsy. The sensitivity and specificity of the abbreviated and full protocols were not significantly different. This pilot study of an abbreviated MRI protocol demonstrates effective performance in cancer detection. BI-RADS assessments were rarely altered with the additional information afforded by the full protocol. The abbreviated protocol holds promise for resource-efficient breast cancer screening in high-risk women.

Usefulness of combined BI-RADS analysis and Nakagami statistics of ultrasound echoes in the diagnosis of breast lesions

To develop a method combining the statistics of the ultrasound backscatter and the Breast Imaging-Reporting and Data System (BI-RADS) classification to enhance the differentiation of breast tumours. The Nakagami shape parameter m was used to characterise the scatter properties of breast tumours. Raw data from the radiofrequency (RF) echo-signal and B-mode images from 107 (32 malignant and 75 benign) lesions and their surrounding tissue were recorded. Three different characteristic values of the shape parameters of m (maximum [mLmax], minimum [mLmin] and average [mLavg]) and differences between m parameters (Δmmax, Δmmin, Δmavg) of the lesions and their surrounding tissues were assessed. A lesion with a BI-RADS score of 3 was considered benign, while a lesion with a score of 4 was considered malignant (a cut-off of BI-RADS 3/4 was set for all patients). The area under the receiver operating characteristic (ROC) curve (AUC) was equal to 0.966 for BI-RADS, with 100% sensitivity and 54.67% specificity. All malignant lesions were diagnosed correctly, whereas 34 benign lesions were biopsied unnecessarily. In assessing the Nakagami statistics, the sum of the sensitivity and specificity was the best for mLavg (62.5% and 93.33%, respectively). Only four of 20 lesions were found over the cut-off value in BI-RADS of 4a. When comparing the differences in m parameters, Δmavg had the highest sensitivity of 90% (only three of 32 lesions were false negative). These three lesions were classified as BI-RADS category 4c. The combined use of B-mode and mLmin parameter improve the AUC up to 0.978 (p=0.088), compared to BI-RADS alone. The combination of the parametric imaging and the BI-RADS assessment does not significantly improve the differentiation of breast lesions, but it has the potential to better identify the group of patients with mainly benign lesions that have a low level of suspicion for malignancy with a BI-RADS score of 4a.

Mammographic density is the main correlate of tumors detected on ultrasound but not on mammography.

Although mammography screening programs do not include ultrasound examinations, some diagnostic units do provide women with both mammography and ultrasonography. This article is concerned with estimating the risk of a breast cancer patient diagnosed in a hospital-based mammography unit having a tumor that is visible on ultrasound but not on mammography. A total of 1,399 women with invasive breast cancer from a hospital-based diagnostic mammography unit were included in this retrospective study. For inclusion, mammograms from the time of the primary diagnosis had to be available for computer-assisted assessment of percentage mammographic density (PMD), as well as Breast Imaging Reporting and Data System (BIRADS) assessment of mammography. In addition, ultrasound findings were available for the complete cohort as part of routine diagnostic procedures, regardless of any patient or imaging characteristics. Logistic regression analyses were conducted to identify predictors of mammography failure, defined as BIRADS assessment 1 or 2. The probability that the visibility of a tumor might be masked at diagnosis was estimated using a regression model with the identified predictors. Tumors were only visible on ultrasound in 107 cases (7.6%). PMD was the strongest predictor for mammography failure, but age, body mass index and previous breast surgery also influenced the risk, independently of the PMD. Risk probabilities ranged from 1% for a defined low-risk group up to 40% for a high-risk group. These findings might help identify women who should be offered ultrasound examinations in addition to mammography.

Cystic breast lesions by conventional ultrasonography: sonographic subtype-pathologic correlation and BI-RADS Assessment

IntroductionAppropriate categorization is very important because the clinical management of each subtype of cystic breast lesions (CBLs) differs. The purpose was to evaluate the sonographic subtype-pathologic correlation, and to identify the effectiveness of the Breast Imaging Reporting and Data System (BI-RADS)-ultrasound (US) for differentiation of benign and malignant CBLs.Material and methodsA database from December 1, 2007 and November 30, 2009 was identified in the Department of Diagnostic Ultrasound, Rui Jin Hospital, School of Medicine, Shanghai Jiao Tong University, China. Those patients with palpable or clinical symptomatic breast masses were associated with a cystic component in lesions on breast US. All patients underwent a subsequent fine-needle/core-needle biopsy or surgical excision. The sonographic findings were analyzed according to the BI-RADS-US, and were categorized by two different methods of subtype categorization compared with the pathologic results.ResultsNinety-nine breast cystic lesions in 83 women were included, among whom 16 patients were identified with bilateral cystic lesions. The total malignancy rate of CBLs was 14.1% (95% confidence interval 7.3–21.0%). Among 99 CBLs, 14 malignant lesions were associated with sonographic appearances of complex cystic lesions, while the remaining subtypes were benign. Shape, margin, echo pattern, orientation, calcification, and vascularity were statistically significantly different between the benign and malignant lesions (p = 0.010, p = 0.004, p < 0.001, p < 0.001, p = 0.036, and p < 0.001, respectively) (degrees of freedom = 1).ConclusionsBy comparison of the two different methods of subtype categorization of CBLs, the appropriate 5-variety classification should be suggested. The BI-RADS-US was useful for differentiating benign from malignant cystic lesions.

Inter- and intraradiologist variability in the BI-RADS assessment and breast density categories for screening mammograms

The aim of this study was to evaluate reader variability in screening mammograms according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) assessment and breast density categories. A stratified random sample of 100 mammograms was selected from a population-based breast cancer screening programme in Barcelona, Spain: 13 histopathologically confirmed breast cancers and 51 with true-negative and 36 with false-positive results. 21 expert radiologists from radiological units of breast cancer screening programmes in Catalonia, Spain, reviewed the mammography images twice within a 6-month interval. The readers described each mammography using BI-RADS assessment and breast density categories. Inter- and intraradiologist agreement was assessed using percentage of concordance and the kappa (κ) statistic. Fair interobserver agreement was observed for the BI-RADS assessment [κ=0.37, 95% confidence interval (CI) 0.36-0.38]. When the categories were collapsed in terms of whether additional evaluation was required (Categories III, 0, IV, V) or not (I and II), moderate agreement was found (κ=0.53, 95% CI 0.52-0.54). Intra-observer agreement for BI-RADS assessment was moderate using all categories (κ=0.53, 95% CI 0.50-0.55) and substantial on recall (κ=0.66, 95% CI 0.63-0.70). Regarding breast density, inter- and intraradiologist agreement was substantial (κ=0.73, 95% CI 0.72-0.74 and κ=0.69, 95% CI 0.68-0.70, respectively). We observed a substantial intra-observer agreement in the BI-RADS assessment but only moderate interobserver agreement. Both inter- and intra-observer agreement in mammographic interpretation of breast density was substantial. Advances in knowledge Educational efforts should be made to decrease radiologists' variability in BI-RADS assessment interpretation in population-based breast screening programmes.

Digital Breast Tomosynthesis Is Comparable to Mammographic Spot Views for Mass Characterization

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

Mammographic characteristics and vacuum-assisted breast biopsy (VABB) of non-palpable breast lesions

The new imaging technology made available today allows for an early detection of small subclinical breast lesions which frequently call for guided presurgical micro-histology. To evaluate the relationship between vacuum-assisted breast biopsy (VABB) histopathological diagnoses and mammographic findings in non-palpable breast lesions. The breast lesions of 1393 women who had received consecutive screening mammograms between 2001 and 2007 were assessed by VABB. The mammographic breast lesions, classified according to the Breast Imaging Reporting and Data System (BI-RADS), were subjected to VABB only if rated as highly suspicious (2%), suspicious (64.5%) for malignancy, or probably benign (33.5%). VABB findings included 981 (70.5%) probably benign lesions, 407 (29.2%) suspicious/malignant lesions, and five (0.3%) cases which were considered as inappropriate for diagnostic purposes. At histology, 10.2% of the suspicious/malignant lesions were classified as proliferative lesions, 11.1% as ductal carcinoma in situ (DCIS), and 8% as invasive ductal carcinoma (IDC). The positive predictive value (PPV) of BI-RADS assessment categories 3, 4 and 5 was 4.1%, 25.3% and 75%, respectively. The occurrence of obscured or spiculated masses was found to exhibit the highest PPV for malignancy (12.5% in BI-RADS 3 and 63% in BI-RADS 4), followed by microcalcifications which showed a malignancy rate of 6.4% in BI-RADS 3, and 20% in BI-RADS 4. VABB turns out to be effective in the assessment of many malignant and benign preclinical tumour lesions thus allowing for a significant reduction of the number of surgical biopsies.

Inter-observer variability of Breast Imaging Reporting and Data System(BI-RADS) ultrasound final assessment

Objective To evaluate the inter-observer variability of static breast sonogram final assessment among observers with different breast imaging experience, using the first edition of the Breast Imaging Reporting and Data System(BI-RADS) for ultrasound. Methods Thirty patients with 30 breast lesions were included who underwent beast lesions resection operation. A pathological diagnosis was available for all 30 lesions:16 (53%) malignant and 14 (47%) benign. Twelve radiologists independently reviewed two sonograms of each lesion, and assigned a final BI-RADS assessment category. Inter-observer variability was measured using kappa statistic. Positive predictive value(PPV) and negative predictive value (NPV) for final assessment were also calculated. Results As for the experienced observers,kappa values of categories 3,4 and 5 were 0.72,0.28 and 0.60,NPV of category 3 was 93% ,PPV of category 5 was 97% ,all of which decreased as the breast imaging experience reduced. PPVs of categories 4a,4b and 4c were 56 % ,88% and 69%, respectively. Conclusions Using BI-RADS final assessment, radiologists with sufficient breast imaging experience can provide accurate and consistent assessment for breast ultrasonography,but the agreement of diagnosis decreased as the breast imaging experience reduced. The clinical feasibility of 4a,4b and 4c subcategories is uncertain. Key words: Ultrasonography; Breast diseases