Breast Imaging Reporting And Data System Category 4a Research Articles

Differential diagnosis of B-mode ultrasound Breast Imaging Reporting and Data System category 3-4a lesions in conjunction with shear-wave elastography using conservative and aggressive approaches.

The high false-positive rates of US Breast Imaging Reporting and Data System (BI-RADS) category 3-4a breast lesions leads to excessive biopsies of many benign lesions, and our aim was to investigate the diagnostic performance achieved by adding a maximum elasticity (Emax) of shear-wave elastography (SWE) to ultrasound (US) to evaluate US BI-RADS category 3-4a breast lesions using conservative and aggressive approaches. We explored the capacity of using this method to avoid unnecessary biopsies without increasing the probability of missing breast cancers. A total of 123 breast lesions of 120 patients classified as BI-RADS category 3 or 4a were enrolled from January 2019 to December 2019. The US features were evaluated according to the US BI-RADS lexicon. The maximum diameter measured on the US was defined as the size of the lesion. The Emax was assessed by SWE, and the average Emax of breast lesions on two images were calculated and recorded as the final maximum Young's modulus. The diagnostic performance of the combined B-mode US and SWE approach for BI-RADS category 3-4a breast lesions was tested using a conservative approach and an aggressive approach. In the conservative approach, the lesions were downgraded with Emax of 30 kPa or less and upgraded with Emax of 160 kPa or more. In the aggressive approach, the lesions were downgraded with Emax of 80 kPa or less and upgraded with Emax of 160 kPa or more. Pathologic results were defined as the reference standard. Among all 123 breast lesions, there were 60 lesions classified as BI-RADS category 3 and 63 lesions classified as BI-RADS category 4a. Compared to the B-mode US, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC) of the combined B-mode US and SWE with a conservative approach changed from 88.9% to 94.4%, 55.2% to 60.0%, 25.4% to 28.8%, 96.7% to 98.4%, 60.2% to 65.0%, and 0.721 to 0.772, respectively. The specificity, PPV, and accuracy of combined B-mode US and SWE with an aggressive approach increased from 55.2% to 72.4%, 25.4% to 29.3%, and 60.2% to 71.5%, respectively, but this was accompanied with decreases in the sensitivity from 88.9% to 66.7%, the NPV from 96.7% to 92.7%, and the AUC from 0.721 to 0.695. The addition of SWE improves the diagnostic performance of breast US. Adding the diagnostic criteria of SWE to the BI-RADS assessment of B-mode US, downgrading the lesions with Emax 30 kPa or less, and upgrading the lesions with Emax 160 kPa or more helped discriminate low suspicion lesions from benign lesions in order to decrease false-positive findings and avoid missing cancer diagnosis.

Combined Use of Shear Wave Elastography, Microvascular Doppler Ultrasound Technique, and BI-RADS for the Differentiation of Benign and Malignant Breast Masses.

ObjectiveTo evaluate the value of the combined use of Breast Imaging Reporting and Data System (BI-RADS), qualitative shear wave elastography (SWE), and AngioPLUS microvascular Doppler ultrasound technique (AP) for distinguishing benign and malignant breast masses.Materials and MethodsA total of 210 pathologically confirmed breast lesions in 210 patients were reviewed using BI-RADS, qualitative SWE, and AP. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), accuracy, and area under the receiver operating characteristic curve (AUC) of BI-RADS and the combination of qualitative SWE and/or AP with BI-RADS were compared, respectively.ResultsCompared with using BI-RADS alone, the use of combined qualitative SWE and/or AP with BI-RADS had higher AUC values (P < 0.001). Besides this, the combination of qualitative SWE and AP with BI-RADS had the best diagnostic performance for differentiating between benign and malignant masses. When AP and SWE were combined with BI-RADS, 49/76 benign masses were downgraded from BI-RADS category 4a into BI-RADS category 3, while no benign masses were upgraded from BI-RADS category 3 into BI-RADS category 4a. Three sub-centimeter malignant masses were downgraded from BI-RADS category 4a into BI-RADS category 3, while three malignant masses remain in BI-RADS category 3 due to a benign manifestation in both AP and qualitative SWE. Moreover, 5/6 of them were sub-centimeter masses, and 4/6 of them were intraductal carcinoma. The sensitivity, specificity, PPV, NPV, accuracy, and AUC were 91.0%, 81.1%, 69.3%, 95.1%, 84.3%, and 0.861 (95% confidence interval, 0.806–0.916; P < 0.001), respectively. Compared with BI-RADS alone, the sensitivity slightly decreased, while the specificity, PPV, NPV, and accuracy were significantly improved.ConclusionCombination of qualitative SWE and AP with BI-RADS improved the diagnostic performance in differentiating benign from malignant breast lesions, which is helpful for avoiding unnecessary biopsies. However, we should be careful about the downgrading of sub-centimeter BI-RADS 4a category lesions.

Evaluation of Strain and Shear Wave Ultrasound Elastography in Predicting Malignancy in BI-RADS Categories 4A and 4B Breast Lesions

Objective: To evaluate elasticity (E) and optimal cut-off values in differentiating benign and malignant tumors in patients with breast imaging–reporting and data system (BI-RADS) 4A/4B lesions.Material and Methods: Patients aged above 18 years with BI-RADS 4A/4B lesions who had tissue biopsies November 2019 - January 2021 were enrolled. B-mode ultrasound BI-RADS classification, E/B distance ratio, and mean elasticity value in kPa and shear wave velocity (SWV) in m/s were recorded pre-biopsy.Results: A total of 148 lesions in 141 patients were included, 129 benign (87.2%), 3 high-risk (2.0%), and 16 malignant (10.8 %). Patients with breast cancer or high-risk lesions were approximately ten years older than those with benign lesions (58.9±10.5 years vs 48±10.6 years of age) (p-value&lt;0.001). The elasticity values were significantly different between the benign and malignant groups with median E/B distance ratios 0.9 and 1.2 (p-value&lt;0.001), median kPameans 4.2 and 7.2, and SWVmeans 1.2 m/s and 1.6 m/s, respectively. Setting SWVmean &gt;1.28 m/s as a cut-off value showed the highest sensitivity (68.0%) among the three single elasticity values, and kPamean &gt;7 the highest specificity (82.0%). The negative predictive value was very high, 91.0-93.0%, for all methods. When using a combined E/B ratio &gt;1 and SWVmean &gt;1.28 as a cut-off value, specificity increased to 93.0% with a high NPV but lower sensitivity.Conclusion: Using a combined E/B ratio &gt;1 and SWVmean &gt;1.28 as a cut-off value can help to differentiate between benign and malignant BI-RADS 4A and 4B lesions and avoid unnecessary biopsies.

Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study.

Purpose: To develop and to validate a risk-predicted nomogram for downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4a breast lesions.Patients and Methods: We enrolled 680 patients with breast lesions that were diagnosed as BI-RADS category 4a by conventional ultrasound from December 2018 to June 2019. All 4a lesions were randomly divided into development and validation groups at the ratio of 3:1. In the development group consisting of 499 cases, the multiple clinical and ultrasound predicted factors were extracted, and dual-predicted nomograms were constructed by multivariable logistic regression analysis, named clinical nomogram and ultrasound nomogram, respectively. Patients were twice classified as either “high risk” or “low risk” in the two nomograms. The performance of these dual nomograms was assessed by an independent validation group of 181 cases. Receiver Operating Characteristic (ROC) curve and diagnostic value were calculated to evaluate the applicability of the new model.Results: After multiple logistic regression analysis, the clinical nomogram included 2 predictors: age and the first-degree family members with breast cancer. The area under the curve (AUC) value for the clinical nomogram was 0.661 and 0.712 for the development and validation groups, respectively. The ultrasound nomogram included 3 independent predictors (margins, calcification and strain ratio), and the AUC value in this nomogram was 0.782 and 0.747 in the development and validation groups, respectively. In the development group of 499 patients, approximately 50.90% (254/499) of patients were twice classified “low risk”, with a malignancy rate of 1.18%. In the validation group of 181 patients, approximately 47.51% (86/181) of patients had been twice classified as “low risk”, with a malignancy rate of 1.16%.Conclusions: A dual-predicted nomogram incorporating clinical factors and imaging characteristics is an applicable model for downgrading the low-risk lesions in BI-RADS category 4a and shows good stability and accuracy, which is useful for decreasing the rate of invasive examinations and surgery.

Subclassification of BI-RADS 4 Magnetic Resonance Lesions: A Systematic Review and Meta-Analysis.

This research aims to investigate and evaluate the diagnostic efficacy of magnetic resonance imaging (MRI) in classifying Breast Imaging Reporting and Data System (BI-RADS) 4 lesions into subcategories: 4a, 4b, and 4c, so as to limit biopsies of suspected lesions in the breast. The PubMed, Web of Science, Embase, and Cochrane Library foreign language databases were searched for literature published between January 2000 and July 2018. After analyzing the selection, data extraction, and quality assessment, a meta-analysis was performed, including data pooling, heterogeneity testing, and meta-regression. Fourteen articles, including 18 studies, met the inclusion criteria. The diagnostic efficacy of MRI for BI-RADS 4-weighted summary assay sensitivity and specificity were estimated at 0.95 [95% confidence interval (CI), 0.89-0.98] and 0.87 (95% CI, 0.81-0.91), respectively. The positive and negative likelihood ratios were 7.1 (95% CI, 4.7-10.7) and 0.06 (95% CI, 0.02-0.14), respectively. The diagnostic odds ratio was 126 (95% CI, 37-426), and the area under the receiver operating characteristic curve was 0.95 (95% CI, 0.93-0.97). The malignancy ratio of BI-RADS 4a, 4b, and 4c and malignancy range were 2.5% to 18.3%, 23.5% to 57.1%, and 58.0% to 95.2%, respectively. Risk stratification of suspected lesions (BI-RADS categories 4a, 4b, and 4c) can be achieved by MRI. The MRI is an effective auxiliary tool to further subclassify BI-RADS 4 lesions and prevent unnecessary biopsy of BI-RADS 4a lesions.

Artificial Intelligence Applied to Breast MRI for Improved Diagnosis.

Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient treatment. Purpose To evaluate whether the diagnostic performance of radiologists in the differentiation of cancer from noncancer at dynamic contrast material-enhanced (DCE) breast MRI is improved when using an AI system compared with conventionally available software. Materials and Methods In a retrospective clinical reader study, images from breast DCE MRI examinations were interpreted by 19 breast imaging radiologists from eight academic and 11 private practices. Readers interpreted each examination twice. In the "first read," they were provided with conventionally available computer-aided evaluation software, including kinetic maps. In the "second read," they were also provided with AI analytics through computer-aided diagnosis software. Reader diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis, with the area under the ROC curve (AUC) as a figure of merit in the task of distinguishing between malignant and benign lesions. The primary study end point was the difference in AUC between the first-read and the second-read conditions. Results One hundred eleven women (mean age, 52 years ± 13 [standard deviation]) were evaluated with a total of 111 breast DCE MRI examinations (54 malignant and 57 nonmalignant lesions). The average AUC of all readers improved from 0.71 to 0.76 (P = .04) when using the AI system. The average sensitivity improved when Breast Imaging Reporting and Data System (BI-RADS) category 3 was used as the cut point (from 90% to 94%; 95% confidence interval [CI] for the change: 0.8%, 7.4%) but not when using BI-RADS category 4a (from 80% to 85%; 95% CI: -0.9%, 11%). The average specificity showed no difference when using either BI-RADS category 4a or category 3 as the cut point (52% and 52% [95% CI: -7.3%, 6.0%], and from 29% to 28% [95% CI: -6.4%, 4.3%], respectively). Conclusion Use of an artificial intelligence system improves radiologists' performance in the task of differentiating benign and malignant MRI breast lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Krupinski in this issue.

Annual Trends in Ultrasonography-Guided 14-Gauge Core Needle Biopsy for Breast Lesions.

ObjectiveTo examine time trends in ultrasonography (US)-guided 14-gauge core needle biopsy (CNB) for breast lesions based on the lesion size, Breast Imaging-Reporting and Data System (BI-RADS) category, and pathologic findings.Materials and MethodsWe retrospectively reviewed consecutive US-guided 14-gauge CNBs performed from January 2005 to December 2016 at our institution. A total of 22,297 breast lesions were included. The total number of biopsies, tumor size (≤ 10 mm to > 40 mm), BI-RADS category (1 to 5), and pathologic findings (benign, high risk, ductal carcinoma in situ [DCIS], invasive cancer) were examined annually, and the malignancy rate was analyzed based on the BI-RADS category.ResultsBoth the total number of US scans and US-guided CNBs increased while the proportion of US-guided CNBs to the total number of US scans decreased significantly. The number of biopsies classified based on the tumor size, BI-RADS category, and pathologic findings all increased over time, except for BI-RADS categories 1 or 2 and category 3 (odds ratio [OR] = 0.951 per year, 95% confidence interval [CI]: 0.902, 1.002 and odds ratio = 0.979, 95% CI: 0.970, 0.988, respectively). Both the unadjusted and adjusted total malignancy rates and the DCIS rate increased significantly over time. BI-RADS categories 4a, 4b, and 4c showed a significant increasing trend in the total malignancy rate and DCIS rate.ConclusionThe malignancy rate in the results of US-guided 14-gauge CNB for breast lesions increased as the total number of biopsies increased from 2005 to 2016. This trend persisted after adjusting for the BI-RADS category.

The value of ultrasound classification in BI-RADS category 4 of breast complex cystic masses

Objective: To analyze the feature of breast complex cystic masses and to classify it at ultrasonography (US), which applied to the Breast Imaging Reporting and Data System (BI-RADS) categories 4a to 4c with pathological results as the golden standards. Methods: The ultrasonographic data and clinical features of 78 patients with complex cystic masses confirmed by pathology in Cancer Hospital from July 2014 to June 2017 were retrospectively reviewed. The complex cystic breast masses were divided into four classes on the basis of their US features: type 1 [thick wall and (or) thick septa (> 0.5 mm)], type 2 (one or more intra-cystic masses), type 3 (mixed cystic and solid components with cystic components more than 50%) and type 4 (mixed cystic and solid components with solid components more than 50%). Positive values (PPVs) were calculated for each type. Multiple linear regression analysis was used to analyze the ultrasonographic features of the masses (lesion size, margins, blood flow resistance index, calcification, and axillary lymph nodes, etc.) with malignant correlation. Results: There were 81 lesions in 78 patients. Among the 81 masses based on US appearance, 14 (17.3%) were classified as type Ⅰ, 18 (22.2%) as type Ⅱ, 18 (22.2%) as type Ⅲ, and 31 (38.3%) as type Ⅳ. The positive predictive values of the malignant lesions of type Ⅰ, type Ⅱ, Ⅲ and Ⅳ were 7.1%, 16.7%, 61.1% and 48.3%, respectively (P=0.040). In all the 81 masses, 14 were BI-RADS categories 4a, 18 were BI-RADS categories 4b and 49 were BI-RADS categories 4c. Masses with maximum diameter equal to or larger than 2.0 cm, unclear margins, RI≥0.7 and presence of abnormal axillary nodes assessment had a high probability of malignancy (P=0.030, 0.038, <0.001 and 0.025, respectively). Conclusion: Ultrasound typing is helpful for differentiating benign and malignant breast complex cysts and classifying BI-AIDS 4a to 4c, thus providing clearer treatment for clinical practice.

Diagnostic value of breast imaging reporting and data system combined with wire-guided localization biopsy for breast microcalcifications in impalpable breast cancer

Objective To explore the diagnosis value of X-ray breast imaging reporting and data system (BI-RADS) combined with wire-guided localization biopsy for breast microcalcifications in impalpable breast cancer. Methods 192 palpation negative patients with 205 microcalcification lesions were detected by mammography. All lesions were classified according to BI-RADS descriptors for calcification and were categorized by the BI-RADS. The patients with BI-RADS category 4a and above underwent X-ray positioning guide wire-guided biopsy and pathological diagnosis. Results In 205 microcalcification lesions, 74 (36.1%) were malignant lesions, 131 (63.9%) were less than benign lesions. The positive predictive value of malignant breast lesions in clustered, segmental, regional linear branching calcifications were higher [83.3% (5/6), 100.0% (11/11), 100.0% (1/1)], followed by clustered, linear, segmental, regional pleomorphic calcifications [55.9% (38/68), 50.0% (1/2), 40.0% (8/20) and 33.3% (4/12), respectively]. The positive predictive values of malignant in linear branching calcifications and pleomorphic calcifications were significantly higher than those of coarse heterogeneous calcifications, amorphous or indistinct calcifications (χ2 values were 34.44, 51.87, 16.71, 29.86, all P < 0.05). The linear branching calcification had the highest possibility. The proportions of malignant lesions in four different types of glands were extremely dense 40.5% (30/74), heterogeneously dense39.2% (29/74), scattered areas of fibroglandular density 10.8% (8/74) and fat 9.5% (7/74), respectively. Conclusions BI-RADS categorization for breast microcalcification lesions can improve the detection rate of impalpable breast cancer. Linear branching calcification has higher predictive value for malignant lesions. Dense breast is the risk factors of breast cancer, which should be attached great importance. Key words: Mammography; Microcalcification; Biopsy; Palpation; Breast neoplasms

Mammographically Occult Asymptomatic Radial Scars/Complex Sclerosing Lesions at Ultrasonography-Guided Core Needle Biopsy: Follow-Up Can Be Recommended

An increasing number of radial scars are detected by ultrasound (US), but their management is controversial. This study investigated the upgrade rate in mammographically occult radial scars/complex sclerosing lesions without epithelial atypia at US-guided 14-gauge core needle biopsy in asymptomatic patients. Nineteen mammographically occult benign radial scars/complex sclerosing lesions (median size, 7 mm; range, 3–23 mm) were included. Patients underwent surgical excision (n = 10) or vacuum-assisted excision, with follow-up US at least 6 mo after benign vacuum-assisted excision results (n = 8), or underwent US follow-up for 2 y after core needle biopsy (n = 1). Any cases with change in diagnosis to high-risk lesions or malignancy at excision were considered upgrades. The upgrade rate was 0.0%. Based on US findings, 15.8% (3/19) were Breast Imaging Reporting and Data System (BI-RADS) category 3, 68.4% (13/19) were BI-RADS category 4a and 15.8% (3/19) were BI-RADS category 4b. Follow-up with US can be considered for mammographically occult benign radial scar/complex sclerosing lesions diagnosed by US core needle biopsy in asymptomatic patients.

Histopathologic Analysis of BI-RADS Category 4a Breast Lesions Diagnosed by Ultrasonography

Purpose: We analyzed the histopathologic findings of the patients with ultrasongraphic Breast Imaging Reporting and Data System (BI-RADS) Category 4a breast lesions to determine which patient can be excluded from any invasive, diagnostic procedure in the future. Methods: Of the 180 cases of BI-RADS Category 4a breast lesions that were diagnosed with ultrasonography during a 6 month-period, 132 cases were pathologically confirmed and these were analyzed retrospectively. Four benign cases that did not undergo any further procedure after fine needle biopsy and 6 malignant cases (4.5%) were excluded from this study. Results: Of the 122 cases, 77 cases (63.1%) showed homogeneous benign finding, and 45 cases (36.9%) showed heterogeneous finding that was made up of two or more different pathologic lesions. Fibroadenoma (55.8%) was the most frequent pathologic finding in the cases with homogeneous finding, followed by fibrocystic change (14.3%), and fibrosis (7.8%). The cases with heterogeneous finding presented fibrocystic change (55.5%), microcalcification (48.8 %), ductal hyperplasia (42.2%), and fibroadenoma (31.1%) in the order of frequency. Conclusion: Lesion with heterogeneous histopathologic nature was the most frequent finding defined as category 4a in breast ultrasonography, followed by fibrodenoma, fibrocystic change, microcalcification, and ductal hyperplasia. Refining more specific ultrasonographic findings of these lesions would guarantee that radiologists exclude more benign lesions from category 4a.

Evaluation of Computer-aided Detection Systems in the Detection of Small Invasive Breast Carcinoma

To retrospectively compare two CAD systems for detecting invasive breast cancers manifesting as noncalcified masses smaller than 16 mm. Waiver of informed consent was granted by the Institutional Review Board that approved this HIPAA-compliant study. Mammograms obtained from two institutions providing consecutive invasive carcinomas manifesting as noncalcified masses smaller than 16 mm were evaluated by using two commercially available CAD systems (R2 ImageChecker M1000, version 5.0A and iCAD Second Look, version 6.0 mid operating point). To provide statistical power to test for a possible 10% difference in the sensitivity performance between the systems, 192 consecutive mammographic studies (182 unifocal, six multifocal, and four bilateral cancers) were collected. Masses were characterized using the Breast Imaging Reporting and Data System (BI-RADS). Per study specificity and mass false marker rate were determined by using 51 normal four-view studies, while scoring only the mass false-positive marks for noncalcified masses. Associations between mass characteristics and supplying institution were compared by using chi2 tests. A P value of .05 was considered to indicate a significant difference. The respective per study sensitivity, per image (ie, per view) sensitivity, per study specificity, and mass false-positive marker rates were 81.8%, 64.7%, 39.2%, and 1.08 for the R2 ImageChecker M1000 system, and 60.9%, 42.6%, 31.4%, and 1.41 for the iCAD Second Look system. The overall per study and per image sensitivities were significantly better for R2 than for iCAD (McNemar test, all P<.001), with a nonsignificant higher per study specificity and lower mass false marker rate on normal studies. CAD results demonstrated at least a 20% variation between BI-RADS categories 4a and 5 for per study and per image sensitivity. A statistically significant difference was observed in per study and per image sensitivity in our mammography data set with small (<16 mm), noncalcified invasive breast malignancies between two CAD systems. Differences in per study specificity and mass false marker rate were noted but were not statistically significant.