BI-RADS Research Articles

Intraradiological pathology-calibrated electrical impedance spectroscopy in the evaluation of excision-required breast lesions.

Evaluating a real-time complementary bioelectrical diagnostic device based on electrical impedance spectroscopy (EIS) for improving breast imaging-reporting and data system (BI-RADS) scoring accuracy, especially in high-risk or borderline breast diseases. The primary purpose is to characterize breast tumors based on their dielectric properties. Early detection of high-risk lesions and increasing the accuracy of tumor sampling and pathological diagnosis are secondary objectives of the study. The tumor detection probe (TDP) was first applied to the mouse model for electrical safety evaluations by electrical current measurement. Then it was utilized for characterization of 138 human palpable breast lesions that were to undergo core needle biopsy (CNB), vacuum-assisted biopsy (VAB), or fine needle aspiration (FNA) on the surgeon's requests. Impedance phase slope (IPS) in frequency ranges of 100- 500kHz and impedance magnitude in f=1kHz were extracted as the classification parameters. Consistency of radiological and pathological declarations for the excisional recommendation was then compared with the IPS values. Considering pathological results as the gold standard, meaningful correlations between IPS and pathophysiological status of lesions recommended for excision (such as atypical ductal hyperplasia, papillary lesions, complex sclerosing adenosis, and fibroadenoma) were observed (p<0.0001). These pathophysiological properties may include cell size, membrane permeability, packing density, adenosis, cytoplasm structure, etc. Benign breast lesions showed IPS values greater than 0, while high-risk proliferative, precancerous, or cancerous lesions had negative IPS values. Statistical analysis showed 95% sensitivity with area under the curve (AUC) equal to 0.92. Borderline breast diseases and high-risk lesions that should be excised according to standard guidelines can be diagnosed with TDP before any sampling process. It is an important outcome for high-risk lesions that are radiologically underestimated to BI-RADS3, specifically in younger patients with dense breast masses that present challenges in mammographic and sonographic evaluations. Also, the lowest IPS value detects the most pathologic portions of the tumor for increasing sampling accuracy in large tumors. Precise detection of high-risk breast masses, which may be declared BI-RADS3 instead of BI-RADS4a.

Machine learning–based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study

Ultrasound is commonly used in breast cancer screening but lacks quantification ability and diagnostic power due to its low specificity, which can lead to overdiagnosis and unnecessary biopsies. This study evaluated the diagnostic efficacy and clinical utility of adding shear-wave elastography (SWE) to the screening of the Breast Imaging Reporting and Data System (BI-RADS) category 4 breast cancer. A machine learning-based diagnostic model was constructed using data retrospectively collected from 3 independent cohorts with features selected using lasso regression and support vector machine-recursive feature elimination algorithms. Propensity score matching (PSM) was used to preclude confounding baseline characteristics between malignant and benign lesions. A decision curve analysis (DCA) was used to evaluate the clinical benefit of the diagnostic model in identifying high-risk tumor patients for intervention while simultaneously avoiding overtreatment of low-risk patients with integrative evaluation using a net benefit value and treatment reduction rate. In our training center, a total of 122 patients were enrolled, and 577 breast tumors were collected. The comparison between malignant and benign lesions revealed significant differences in patient age, tumor size, resistance index (RI), and elasticity values. The maximum elasticity value (Emax) was identified as an independent diagnostic feature and was included in the diagnostic model. The combination of Emax with BI-RADS category 4 demonstrated a significantly better diagnostic efficacy than the BI-RADS category alone [BI-RADS+Emax: AUC =0.908, 95% confidence interval (CI): 0.842-0.974; BI-RADS: AUC =0.862, 95% CI: 0.784-0.94; P=0.024] and significantly increased the clinical benefit for patients and policy makers by effectively reducing overdiagnosis and biopsy rates. In the BI-RADS category 4A subgroup, adding Emax to breast cancer screening benefited patients and showed a greater absolute benefit than did the BI-RADS category alone when used for patients with a higher probability of cancer (>0.403), demonstrating a 50% overtreatment reduction. Adding Emax to BI-RADS category 4 breast cancer screening using SWE significantly reduced overdiagnosis and biopsy rates compared with the BI-RADS category alone, especially for BI-RADS 4A patients.

Ultrasound-guided wire localization biopsy in non-palpable breast lesions: predictive factors for malignancy.

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. 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. 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 BI-RADS 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). 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.

Multi-Task/Single-Task Joint Learning of Ultrasound BI-RADS Features.

Breast cancer is one of the most common cancers among women in the world. The breast imaging reporting and data system (BI-RADS) features effectively improve the accuracy and sensitivity of breast tumors. Based on the description of signs in BI-RADS, a quantitative scoring scheme is proposed based on ultrasound (US) data. This scheme includes feature extraction of high-level semantic, that is, an intermediate step interpreting the subsequent diagnosis. However, the scheme requires doctors to score the features of breast data, which is labor-intensive. To reduce the burden of doctors, we design a multi-task learning (MTL) framework, which can directly output the scores of different BI-RADS features from the raw US images. The MTL framework consists of a shared network that learns global features and K soft attention networks for different BI-RADS features. Thus, it enables the network not only to learn the potential correlation among different BI-RADS features, but also learn the unique specificity of each feature, which can assist each other and jointly improve the scoring accuracy. In addition, we group different BI-RADS features according to the correlation among tasks and build a multi-task/single-task joint framework. Experimental results on the US breast tumor dataset collected from 1859 patients with 4458 US images show that the proposed BI-RADS feature scoring framework achieves an average scoring accuracy of 84.91% for 11 BI-RADS features on the test dataset, which is helpful for the subsequent diagnosis of breast tumors.

An MRI-Based Radiomics Model for Predicting the Benignity and Malignancy of BI-RADS 4 Breast Lesions.

ObjectivesThe probability of Breast Imaging Reporting and Data Systems (BI-RADS) 4 lesions being malignant is 2%–95%, which shows the difficulty to make a diagnosis. Radiomics models based on magnetic resonance imaging (MRI) can replace clinicopathological diagnosis with high performance. In the present study, we developed and tested a radiomics model based on MRI images that can predict the malignancy of BI-RADS 4 breast lesions.MethodsWe retrospective enrolled a total of 216 BI-RADS 4 patients MRI and clinical information. We extracted 3,474 radiomics features from dynamic contrast-enhanced (DCE), T2-weighted images (T2WI), and diffusion-weighted imaging (DWI) MRI images. Least absolute shrinkage and selection operator (LASSO) and logistic regression were used to select features and build radiomics models based on different sequence combinations. We built eight radiomics models which were based on DCE, DWI, T2WI, DCE+DWI, DCE+T2WI, DWI+T2WI, and DCE+DWI+T2WI and a clinical predictive model built based on the visual assessment of radiologists. A nomogram was constructed with the best radiomics signature combined with patient characteristics. The calibration curves for the radiomics signature and nomogram were conducted, combined with the Hosmer-Lemeshow test.ResultsPearson’s correlation was used to eliminate 3,329 irrelevant features, and then LASSO and logistic regression were used to screen the remaining feature coefficients for each model we built. Finally, 12 related features were obtained in the model which had the best performance. These 12 features were used to build a radiomics model in combination with the actual clinical diagnosis of benign or malignant lesion labels we have obtained. The best model built by 12 features from the 3 sequences has an AUC value of 0.939 (95% CI, 0.884-0.994) and an accuracy of 0.931 in the testing cohort. The sensitivity, specificity, precision and Matthews correlation coefficient (MCC) of testing cohort are 0.932, 0.923, 0.982, and 0.791, respectively. The nomogram has also been verified to have calibration curves with good overlap.ConclusionsRadiomics is beneficial in the malignancy prediction of BI-RADS 4 breast lesions. The radiomics predictive model built by the combination of DCE, DWI, and T2WI sequences has great application potential.

Mammographic density to predict response to neoadjuvant systemic breast cancer therapy.

Mammographic density (MD) is a risk factor for breast cancer (BC) development, and recurrence. However, its predictive value has been less studied. Herein, we challenged MD as a biomarker associated with response in patients treated with neoadjuvant therapy (NAT). Data on all NAT treated BC patients prospectively collected in the registry of Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy (2009-2019) were identified. Diagnostic mammograms were used to evaluate and score MD as categorized by the Breast Imaging-Reporting and Data System (BI-RADS), which identifies 4 levels of MD in keeping with relative increase of fibro-glandular over fat tissue. Each case was classified according to the following categories a (MD < 25%), b (26-50%), c (51-75%), and d (> 75%). The association between MD and pathological complete response (pCR), i.e., absence of BC cells in surgical specimens, was analyzed in multivariable setting used logistic regression models with adjustment for clinical and pathological variables. A total of 442 patients were analyzed, 120 of which (27.1%) attained a pCR. BI-RADS categories a, b, c, and d accounted for 10.0%, 37.8%, 37.1% and 15.2% of cases. Corresponding pCR were 20.5%, 26.9%, 30.5%, 23.9%, respectively. At multivariable analysis, when compared to cases classified as BI-RADS a, those with denser breast showed an increased likelihood of pCR with odds ratio (OR) of 1.70, 2.79, and 1.47 for b, c and d categories, respectively (p = 0.0996), independently of age, BMI [OR underweight versus (vs) normal = 3.76], clinical nodal and tumor status (OR T1/Tx vs T4 = 3.87), molecular subtype (HER2-positive vs luminal = 10.74; triple-negative vs luminal = 8.19). In subgroup analyses, the association of MD with pCR was remarkable in triple-negative (ORs of b, c and d versus a: 1.85, 2.49 and 1.55, respectively) and HER2-positive BC cases (ORs 2.70, 3.23, and 1.16). Patients with dense breast are more likely to attain a pCR at net of other predictive factors. The potential of MD to assist decisions on BC management and as a stratification factor in neoadjuvant clinical trials should be considered.

Diagnostic performance of superb microvascular imaging combined with shear-wave elastography in distinguishing invasive ductal carcinoma molecular subtypes

IntroductionTo explore the diagnostic value of combining superb microvascular imaging (SMI), shear-wave elastography (SWE), and Breast Imaging Reporting and Data System (BI-RADS) to distinguish different molecular subtypes of invasive ductal carcinoma (IDC).Material and methodsA total of 239 surgically confirmed IDC masses in 201 patients underwent conventional ultrasound, SMI, and SWE examination, the information such as echo pattern, posterior features, margins, SMI pixels, and hardness of the masses was recorded. According to the St. Gallen standard, breast masses were classified as Luminal A, Luminal B, HER2 overexpression, and triple-negative subtype. We further explored the differences between different molecular subtypes of IDC.ResultsLuminal A subtype had the following characteristics: low histologic grade, posterior acoustic shadowing (p= 0.019), spiculated margins (p&lt;0.001) , and relatively soft. Luminal B subtype was characterized by low histological grade (p &lt;0.0001), posterior acoustic shadowing or indifference, and indistinct margins. HER2 overexpression breast cancers were characterized by high histological grade, enhanced posterior acoustics or indifference, calcifications (p= 0.005), spiculated or indistinct margins, vascularity (p=0.005), and relative stiffness. Triple-negative breast cancers had the characteristics of high histological grade, posterior echogenic enhancement, lack of calcifications, circumscribed or microlobulated margins, low blood flow signals, and stiff tissue (p=0.013).ConclusionsOur study demonstrated the significant differences and trends among the IDC four subtypes by the combined application of SMI, SWE, and BI-RADS lexicon, which are of great significance for early diagnosis, selection of treatment methods, and evaluation of prognosis of IDC.

Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions.

Improving the assessment of breast imaging reporting and data system (BI-RADS) 4 lesions and reducing unnecessary biopsies are urgent clinical issues. In this prospective study, a radiomic nomogram based on the automated breast volume scanner (ABVS) was constructed to identify benign and malignant BI-RADS 4 lesions and evaluate its value in reducing unnecessary biopsies. A total of 223 histologically confirmed BI-RADS 4 lesions were enrolled and assigned to the training and validation cohorts. A radiomic score was generated from the axial, sagittal, and coronal ABVS images. Combining the radiomic score and clinical-ultrasound factors, a radiomic nomogram was developed by multivariate logistic regression analysis. The nomogram integrating the radiomic score, lesion size, and BI-RADS 4 subcategories showed good discrimination between malignant and benign BI-RADS 4 lesions in the training (AUC, 0.959) and validation (AUC, 0.925) cohorts. Moreover, 42.5% of unnecessary biopsies would be reduced by using the nomogram, but nine (4%) malignant BI-RADS 4 lesions were unfortunately missed, of which 4A (77.8%) and small-sized (<10 mm) lesions (66.7%) accounted for the majority. The ABVS radiomics nomogram may be a potential tool to reduce unnecessary biopsies of BI-RADS 4 lesions, but its ability to detect small BI-RADS 4A lesions needs to be improved.

Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment.

We evaluated and compared the mammographic density assessment of an artificial intelligence-based computer-assisted diagnosis (AI-CAD) program using inter-rater agreements between radiologists and an automated density assessment program. Between March and May 2020, 488 consecutive mammograms of 488 patients (56.2 ± 10.9years) were collected from a single institution. We assigned four classes of mammographic density based on BI-RADS (Breast Imaging Reporting and Data System) using commercial AI-CAD (Lunit INSIGHT MMG), and compared inter-rater agreements between radiologists, AI-CAD, and another commercial automated density assessment program (Volpara®). The inter-rater agreement between AI-CAD and the reader consensus was 0.52 with a matched rate of 68.2% (333/488). The inter-rater agreement between Volpara® and the reader consensus was similar to AI-CAD at 0.50 with a matched rate of 62.7% (306/488). The inter-rater agreement between AI-CAD and Volpara® was 0.54 with a matched rate of 61.5% (300/488). In conclusion, density assessments by AI-CAD showed fair agreement with those of radiologists, similar to the agreement between the commercial automated density assessment program and radiologists.

Primary neuroendocrine carcinomas of the breast and neuroendocrine differentiated breast cancers: Relationship between histopathological and radiological features

PurposeThe aim of this study wasto investigate whole-breast imaging findings (mammography, ultrasonography (US), magnetic resonance imaging (MRI),clinical, and histopathological findings of primary neuroendocrine carcinomas of the breast (NEC) and neuroendocrine differentiated breast cancers (NEBC). MethodsPatients withadiagnosis of breast cancer with histopathological neuroendocrine features between the years 2010 and 2021 were retrospectively screened.The lesions were divided into two main groups depending on staining with neuroendocrine markers (synaptophysin and chromogranin A). Those showing focal staining were categorized as NEBC while those with diffuse staining as NEC.The mammography, US, and MRI of the lesionswere reviewed in consensus by two breast radiologists in order to assess imaging featuresretrospectively according to the Breast Imaging Reporting and Data System (BI-RADS) 5th lexicon.The findings were compared with breast cancers without neuroendocrine features (BC-WNE) which were randomly selected from the same database. ResultsA total of 105 lesions [NEBC (n = 44), NEC(n = 11), BC-WNE (n = 50)] were evaluated.Patients with neuroendocrine tumors were older (p < 0.001) than those with BC-WNE. Compared with BC-WNE tumors, radiological findings typical of malignancy such as irregular shape [NEBC (7/20); NEC(3/7) vs BC-WNE (35/43); p < 0.001], spiculation [NEBC (2/20); NEC(0/7) vs BC-WNE (21/43); p < 0.001], architectural distortion [(NEBC (3/24); NEC(0/9) vs BC-WNE (31/50); p < 0.001)], calcification [(NEBC (6/24), NEC(0/9) vs BC-WNE (n = 27/50); p = 0.001)] on mamography, non-parallel orientation to skin [(NEBC (n = 17/29), NEC(n = 4/9), BC-WNE (n = 35/42); p = 0.008)], acoustic shadowing [(NEBC (n = 12/29), NEC(1/9), BC-WNE (n = 29/42); p = 0.009)], axillary lymphadenopathy [(NEBC(n = 3/30), NEC(n = 1/9), BC-WNE (21/50); p < 0.001)]on US were less common features of the neuroendocrine carcinomas of breast. Aside from shape features, there was no significant difference in contrast pattern (p = 0.866), kinetic curve (p = 0.454) and diffusion restriction (p = 0.242) on MRI. ConclusionCharacteristic malignant imaging features, including irregular shape, spiculated margins, suspicious calcifications, and posterior acoustic shadowing, are uncommon in neuroendocrine carcinomas of breast. These carcinomas tend to show more benign imaging features when compared with BC-WNE.

Evaluation of Real-world Data for Breast-MRI BI-RADS IV Lesions.

Magnetic resonance imaging (MRI) is an important diagnostic tool in the detection of breast cancer. The Breast Center of the municipal Hospital Holweide, Cologne, annually cares for and treats patients with changes in the breast. A special problem is posed by Breast Imaging-Reporting and Data System (BI-RADS) 4 lesions. If a BI-RADS 4 finding is present, is a vacuum biopsy indicated in every case or, if there is already an indication for surgery due to other findings, can the corresponding finding be removed openly without histological clarification? We require real world data regarding the actual in-center likelihood of a BIRADS 4 lesion to be DCIS (Ductal carcinoma in situ) or invasive disease. This is a retrospective study of 1,641 patients who received MRI examination in the radiological department of the municipal hospital Holweide in 2012 and 2013. Each BI-RADS 4 finding (or higher) classified by MRI was compared with the final histological result. 347 MRIs showed BI-RADS 4 findings or higher and 280 (80.7%) cases showed benign histology. In 67 (19.3%) cases, histology showed DCIS or invasive carcinoma. BI-RADS 4 lesions have a low probability of malignancy based on real-world data from this center. If there is already an indication for surgery due to other lesions, the patient can also be offered a simultaneous open biopsy in the context of the already initiated surgical treatment. Each center should know the sensitivity and specificity of the MRI imaging performed and counsel patients based on that.

An Improved Nomogram to Reduce False-Positive Biopsy Rates of Breast Imaging Reporting and Data System Ultrasonography Category 4A Lesions.

Background:The NCCN clinical guidelines recommended core needle biopsy for breast lesions classified as Breast Imaging Reporting and Data System (BI-RADS) 4, while category 4A lesions are only 2-10% likely to be malignant. Thus, a large number of biopsies of BI-RADS 4A lesions were ultimately determined to be benign, and those unnecessary biopsies may incur additional costs and pains. However, it is important to emphasize that the current risk prediction model focuses primarily on the details and complex risk features of US or MG findings, which may be difficult to apply in order to benefit from the model. To stratify and manage BI-RADS 4A lesions effectively and efficiently, a more effective and practical predictive model must be developed.Methods:We retrospectively analyzed 465 patients with BI-RADS ultrasonography (US) category 4A lesions, diagnosed between January 2019 and July 2019 in Tianjin Medical University Cancer Institute and Hospital and National Clinical Research Center for Cancer. Univariate and multivariate logistic regression analyses were conducted to identify risk factors. To stratify and predict the malignancy of BI-RADS 4A lesions, a nomogram combining the risk factors was constructed based on the multivariate logistic regression results. In order to determine the predictive performance of our predictive model, we used the concordance index (C-index), calibration curve, and receiver operating characteristic (ROC), and the decision curve analysis (DCA) to assess the clinical benefits.Results:Based on our analysis, 16.3% (76 out of 465) of patients were pathologically diagnosed with malignant lesions, while 83.6% (389 out of 465) were diagnosed with benign lesions. According to univariate and multivariate logistic regression analysis, age (OR = 3.414, 95%CI:1.849-6.303), nipple discharge (OR = .326, 95%CI:0.157-.835), palpable lesions (OR = 1.907, 95%CI:1.004-3.621), uncircumscribed margin (US) (OR = 1.732, 95%CI:1.033-2.905), calcification (mammography, MG) (OR = 2.384, 95%CI:1.366-4.161), BI-RADS(MG) (OR = 5.345, 95%CI:2.934-9.736) were incorporated into the predictive nomogram (C-index = .773). There was good agreement between the predicted risk and the observed probability of recurrence. Furthermore, we determined that 153 was the best cutoff score for distinguishing between patients in the low- and high-risk groups. Malignant lesions were significantly more prevalent in high-risk patients than in low-risk patients.Conclusion:Based on clinical, US, and MG features, we present a predictive nomogram to reliably predict the malignancy risk of BI-RADS(US) 4A lesions, which may assist clinicians in the selection of patients at low risk of malignancy and reduce the number of false-positive biopsies.

Diagnostic accuracy of mammography in characterizing breast masses using the 5th edition of BI-RADS: A retrospective study

Background: Breast imaging-reporting and data system (BI-RADS) is intended for standardizing mammography reporting.Objectives: We aimed to evaluate the diagnostic precision of the BI-RADS assessment scoring system using histopathological findings as the reference standard. We also aimed to assess the positive predictive value (PPV) of different morphological descriptors for malignancy.Materials and Methods: This retrospective record-based analytical study was conducted in the Department of Radiodiagnosis of Acharya Harihar Post Graduate Institute of Cancer, Cuttack, Odisha, a tertiary cancer center in eastern India. We included patients attending the breast cancer unit with various breast complaints who were subjected to mammographic imaging and histopathological examination. The primary outcomes were the sensitivity, PPV, negative predictive value (NPV), and diagnostic accuracy (DA) of the BI-RADS scores for the pathological reports; secondary objective was the evaluation of the mammographic morphological characteristics. Mammography was interpreted using the BI-RADS 5th edition guidelines, without prior knowledge of the biopsy report. A BI-RADS final assessment score between 1 and 5 was assigned, where 1 indicated a normal study, 2 benign, 3 possibly benign requiring follow up, 4 suspicious requiring biopsy, and 5 indicating likely malignant requiring biopsy and further actions.Results: Between February 2020 and December 2020, we included 247 patients. All the category 5 lesions were malignant, while 76.5% of category 4 lesions were malignant. PPVs of BI-RADS categories 4a, 4b, and 4c were 38%, 90%, and 94%, respectively. Mammography had a sensitivity, specificity, PPV, NPV, and DA of 98.7%, 47.6%, 87.5%, 90.9%, and 87.9%, respectively. Morphological features that were significantly associated with malignancy were spiculated margins (P = 0.003, PPV = 100%), microlobulated margins (P = 0.005, PPV = 96.5%), irregular shape (P = 0.002, PPV = 89.6%), microcalcification (P = 0.005, PPV = 92.8%), skin thickening (P < 0.0001, PPV = 100%), and architectural distortion (P = 0.003, PPV = 96.7%).Conclusion: Digital mammography is a sensitive tool for the evaluation of breast lumps, but BI-RADS final assessment score is subjective as it depends on the interpreter's expertise.

Determining the relative frequency of ultrasound findings in women under 30 years of age with a breast mass.

Mammography and ultrasound are the most reliable and common imaging techniques for early detection of breast mass. The aim of this study was to determine the relative frequency of ultrasound findings in women under 30 years of age by a feeling of mass in the breast. This cross-sectional study was performed on women under 30 years of age with a feeling of mass in the breast. The result evaluated in this study was the final opinion of the radiologist on the ultrasound report, which was expressed in the form of Breast imaging-reporting and data system (BIRADS) based on one to five scores. Ultrasound status of patients was reported to be normal, fibrocystic changes, and tumors (solid-cystic-mixed). The obtained data were analyzed using SPSS statistical software version 23. Significance level was considered to be < 0.05. The most common reason for referral was palpable mass in the left breast (56.2%). The shape of the mass was oval in most cases (91.2%). The highest frequency in terms of mass margin was related to Macrolobulated (82.4%), Hypoechoic (85%), and Solid (87.6%). Most of masses belonged to 12 o'clock (21.2%). According to the standard sonography report based on BIRADS, the highest frequency belonged to B4a (57.5%). Pathology report showed that the highest frequency was related to fibroadenoma (71.4%). Considering that most of the cases in this research with a feeling of mass in the breast in women under 30 years of age were BIRADS 4, and among the cases that underwent biopsy, 14.5% were diagnosed with cancer pathology. Therefore, ultrasound examination is very important in these cases, even at a young age.

Unusual presentation of dermal microcalcifications on mammography images: A case report

Introduction. Calcifications and microcalcifications are mineral deposits in the breast tissue. Breast calcifications are a common mammographic finding, present on over 80% of images, and they are usually benign. Characteristics such as size, shape, number, distribution pattern, location, density, and other findings help determine the pathology. Dermal calcifications of the breast are superficial and benign calcifications that are usually found on the sebaceous glands of the skin. In some cases, calcifications can be the first marker of underlying cancer development. They may be associated with the presence of ductal carcinoma in situ or even invasive ductal carcinoma that has spread to the surrounding breast tissue. By correct identification of benign calcifications as such, unnecessary interventions and use of limited resources can be avoided. Case Report. A 66-year-old female patient presented for a regular mammography check-up. On the previous mammographic examination, she was categorized as bilateral breast imaging reporting and data system 1. The new mammography images showed numerous newly formed grouped microcalcifications at the junction of the lower quadrants of the right breast, and a stereotactic vacuum-assisted biopsy was indicated. During the biopsy attempt, the image guided biopsy program did not detect any calcifications in the parenchyma or in any projection and therefore it did not allow the biopsy procedure to continue after several attempts. Examination of the right breast skin revealed lesions treated by the patient for a few weeks; a repeat mammogram was performed and it was observed that the calcifications were of dermal origin. Conclusion. Dermal calcifications are mostly tiny, about the size of skin pores, single or clustered, and often have a calcified rim surrounding a lucent center. However, dermal calcifications deserve a special attention, because they sometimes lack a lucent center and simulate grouped intraparenchymal calcifications that require careful monitoring or biopsy.

Clinical Utility of MicroPure US Imaging for Breast Microcalcifications.

PurposeTo evaluate the performance of MicroPure US imaging to detect and characterize microcalcifications.Materials and MethodsA total of 171 lesions with suspicious microcalcifications seen on mammography and B-mode US were included and simultaneously evaluated using MicroPure US imaging. The size of microcalcifications was divided into small (punctate, amorphous, fine pleomorphic, and fine linear) and large (coarse heterogeneous), and the extent was divided into narrow (grouped) and wide (others). MicroPure US imaging visibility was divided into four types based on the number of microcalcifications on the two images: B > M (more on B-mode), B = M (similar), B < M (more on MicroPure), and negative. Triple pairwise comparison was used to evaluate the imaging features according to the MicroPure US imaging visibility.ResultsAmong the 171 lesions examined, 157 lesions (91.8%) were detected by MicroPure US imaging. The proportion of Breast Imaging Reporting and Data System (BI-RADS) category 4A was significantly higher in the MicroPure positive group, and that of category 4B was significantly higher in the MicroPure negative group (p = 0.035). The other imaging features did not differ. Among the positive MicroPure subgroups, all features showed no significant difference.ConclusionMicroPure US imaging demonstrated 91.8% positivity in detecting microcalcifications on B-mode US. MicroPure US imaging visibility correlated with the BI-RADS category of microcalcifications.

Diagnostic Performance of Digital Breast Tomosynthesis with the Two-Dimensional Synthesized Mammogram for Suspicious Breast Microcalcifications Compared to Full-Field Digital Mammography in Stereotactic Breast Biopsy.

PurposeTo evaluate the diagnostic performance of digital breast tomosynthesis (DBT) with the two-dimensional synthesized mammogram (2DSM), compared to full-field digital mammography (FFDM), for suspicious microcalcifications in the breast ahead of stereotactic biopsy and to assess the diagnostic image visibility of the images.Materials and MethodsThis retrospective study involved 189 patients with microcalcifications, which were histopathologically verified by stereotactic breast biopsy, who underwent DBT with 2DSM and FFDM between January 8, 2015, and January 20, 2020. Two radiologists assessed all cases of microcalcifications based on Breast Imaging Reporting and Data System (BI-RADS) independently. They were blinded to the histopathologic outcome and additionally evaluated lesion visibility using a five-point scoring scale.ResultsOverall, the inter-observer agreement was excellent (0.9559). Under the setting of category 4A as negative due to the low possibility of malignancy and to avoid the dilution of malignancy criteria in our study, McNemar tests confirmed no significant difference between the performances of the two modalities in detecting microcalcifications with a high potential for malignancy (4B, 4C, or 5; p = 0.1573); however, the tests showed a significant difference between their performances in detecting microcalcifications with a high potential for benignancy (4A; p = 0.0009). DBT with 2DSM demonstrated superior visibility and diagnostic performance than FFDM in dense breasts.ConclusionDBT with 2DSM is superior to FFDM in terms of total diagnostic accuracy and lesion visibility for benign microcalcifications in dense breasts. This study suggests a promising role for DBT with 2DSM as an accommodating tool for stereotactic biopsy in female with dense breasts and suspicious breast microcalcifications.

Ultrasound-Guided Needle Biopsy of Suspected Microcalcifications in the Breast.

Background:Sonography plays an important role in characterizing breast masses and in guiding needle core biopsies and wire localizations of suspicious sonographis abnormalities.Objective:The aim of this study was to show the possibilities of high frequency ultrasound devices in the presentation of microcalcifications of the breast, and the use of these possibilities in performing needle biopsy under ultrasound control.Methods:This was a retrospective study conducted from May 2017 to December 2020 on 32 women, 29-86 years of age using mammograms and ultrasound to show suspected microcalcifications (radiological findings of BI RADS 4B and 4C), and needle biopsy led by ultrasound that confirmed the diagnosis of breast cancer. Patients with suspected microcalcifications on mammography that had previously had the diagnosis of breast cancer confirmed in the same or contralateral breast were excluded from the study. Histology results from each core biopsy and surgical excision were reviewed. The positive predictive values of sonography and mammography for this population were calculated, and the sensitivity, specificity, and negative predictive value of sonography were determined. For analysis of the agreement of ultrasound findings with mammography the McNemar x2-test for dependent samples was used.Results:The sensitivity of mammography in the detection of microcalcifications in this study was 100%. The sensitivity of the ultrasound apparatus with a high frequency probe in the detection of microcalcifications after mammography examination in this study was 87.55%, while the specificity was 42.85%.Conclusion:Ultrasonic devices with high-frequency probes enable the display of accumulations of microcalcifications previously verified by mammography, and thus enable the performance of needle biopsy of suspected microcalcifications under ultrasonic control. An alternative is the much more complicated and significantly more expensive stereotactic biopsy under the control of mammography.

Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound.

In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.

An effective fine grading method of BI-RADS classification in mammography.

Mammography is an important imaging technique for the detection of early breast cancer. Doctors classify mammograms as Breast Imaging Reporting and Data Systems (BI-RADS). This study aims to provide an intelligent BI-RADS grading prediction method, which can help radiologists and clinicians to distinguish the most challenging 4A, 4B, and 4C cases in mammography. Firstly, the breast region, the lesion region, and the corresponding region in the contralateral breast were extracted. Four categories of features were extracted from the original images and the images after the wavelet transform. Secondly, an optimized sequential forward floating selection (SFFS) was used for feature selection. Finally, a two-layer classifier integration was employed for fine grading prediction. 45 cases from the hospital and 500 cases from Digital Database for Screening Mammography (DDSM) database were used for evaluation. The classification performance of the support vector machine (SVM), Bayes, and random forest is very close on the 45 testing set, with the area under the receiver operating characteristic curve (AUC) of 0.978, 0.967, and 0.968. On the DDSM set, the AUC achieves 0.931, 0.938, and 0.874. Using the mean probability prediction, the AUC on the two datasets reaches 0.998 and 0.916. However, they are all significantly higher than the doctors' diagnosis, with the AUC of 0.807 and 0.725. A BI-RADS fine grading (2, 3, 4A, 4B, 4C, 5) prediction model was proposed. Through the evaluation from different datasets, the performance is proved higher than that of the doctors, which may provide great help for clinical BI-RADS classification diagnosis. Therefore, our method can produce more effective and reliable results.