Ultrasound Breast Imaging Reporting And Data System Research Articles

Classification of multi-feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks.

Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI-RADS) category 4 has the highest false-positive value of about 30% among five categories. The classification task in BI-RADS category 4 is challenging and has not been fully studied. This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B-mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy. First, original US images coming directly from the hospital were cropped and resized. In 1385 B-mode US BI-RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K-means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B-mode images, K-means clustering images and entropy images, they are fused in a three-channel form multi-feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1-score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro-Wilk test. DeLong test and independent t-test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti-log compression was conducted but no improvement has shown in CNNs classification results. With multi-feature fusion images, DenseNet121 has highest accuracy of 80.22±1.45% compared to other CNNs, precision of 77.97±2.89% and AUC of 0.82±0.01. Multi-feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B-mode images (p<0.05). The CNNs with multi-feature fusion show a good potential of reducing the false-positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false-positive rate in breast tumor within US BI-RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise.

Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study.

The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts. A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort. The DL model was developed by incorporating both US and MG images. The predictive performance of the combined DL model for malignancy was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The combined DL model was then compared to a clinical nomogram model and to the DL model trained using US image only and to that trained MG image only. The combined DL model showed satisfactory diagnostic performance for predicting malignancy in breast lesions, with an AUC of 0.940 (95% CI: 0.874-1.000) in the test cohort, and an AUC of 0.906 (95% CI: 0.817-0.995) in the validation cohort, which was significantly higher than the clinical nomogram model, and the DL model for US or MG alone ( P <0.05). The study developed an objective DL model combining both US and MG imaging features, which was proven to be more accurate for predicting malignancy in the BI-RADS US 4A breast lesions of patients with dense breasts. This model may then be used to more accurately guide clinicians' choices about whether performing biopsies in breast cancer diagnosis.

Metaplastic breast carcinoma with osseous differentiation: A report of a rare case and literature review.

Metaplastic matrix-producing breast carcinoma is a type of metaplastic breast carcinoma (MBC), which is a rare malignancy, accounting for 0.2-1% of breast carcinomas. A 52-year-old female visited a hospital because of a palpable painless mass in the right breast and was diagnosed with Breast Imaging Reporting and Data System (BI-RADS) category 4A via ultrasound (US) with a suspected positive lymph node at the right axillary region. Excision of the breast mass was performed and histopathologically confirmed that it was MBC with osseous differentiation. No distant metastasis was revealed before a modified radical mastectomy; however, metastasis to a lymph node of the right axillary region was observed (1/22). She received six cycles of TEC scheme chemotherapy (docetaxel, epirubicin, and cyclophosphamide, 21 days) and 5 weeks of radiotherapy (48 Gy/25 f/5 days a week), but without any follow-up examinations since radiotherapy. Twenty-four months after surgery, distant metastases to lungs and liver were confirmed and died 3 months later. This case provides valuable information for clinicians on MBC and suggests that further examination or biopsy should be performed to US BI-RADS 4A masses before surgery. In addition, regular postoperative follow-up plays important roles in detecting metastases early and improving prognosis.

Predicting female breast cancer by artificial intelligence: Combining clinical information and BI-RADS ultrasound descriptors

This study aimed to use Artificial Intelligence (AI) Deep Learning (DL) techniques to predict female breast cancer detected by ultrasound based on clinical data and Breast Imaging Reporting and Data System (BI-RADS) Ultrasound (US) descriptors. We retrospectively gathered data on clinical information and BI-RADS US descriptors of breast lesions from 1051 female patients, forming a comprehensive dataset. Two datasets (A and B) were derived by selecting different variables. A BI-RADS DL-based Network (BD-Net) was developed and trained on Dataset A and B, and its performance was evaluated on an external test set. Radiologists also classified Dataset B and the external test set using BI-RADS US. Performance in predicting the probability of malignancy was evaluated by calculating the Area Under Curve (AUC), accuracy, sensitivity, and specificity. BD-Net achieved an accuracy of 92.5% (95%CI, 90.5–94.2) in predicting breast cancer with a sensitivity of 93.0% (95%CI, 90.3–95.4), a specificity of 92.1% (95%CI, 89.7–94.6), and an AUC of 0.97 (95%CI, 0.96–0.98) on the training data set of dataset A. On the external dataset, the BD-Net showed a sensitivity of 93.8% (95%CI, 87.5–98.8), a specificity of 91.0% (95%CI, 85.0–96.0), and an AUC of 0.92 (95%CI, 0.88–0.97) for predicting breast cancer. The radiologists predicted breast cancer on Dataset B and the external test set with AUC values between 0.75 (95%CI, 0.75–0.79) and 0.82 (95%CI, 0.77–0.87). These results indicate that the BD-Net is effective for predicting ultrasound-detected female breast cancer.

Nomograms for prediction of breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: A single-center retrospective study based on radiomics features.

PurposeTo develop nomograms for predicting breast malignancy in BI-RADS ultrasound (US) category 4 or 5 lesions based on radiomics features.MethodsBetween January 2020 and January 2022, we prospectively collected and retrospectively analyzed the medical records of 496 patients pathologically proven breast lesions in our hospital. The data set was divided into model training group and validation testing group with a 75/25 split. Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram was developed for BI-RADS US category 4 or 5 lesions according to the results of multivariate regression analysis from the training group.ResultThe AUCs of radiomics score consisting of 31 US features was 0.886. The AUC of the model constructed with radiomics score, patient age, lesion diameter identified by US and BI-RADS category involved was 0.956 (95% CI, 0.910–0.972) for the training group and 0.937 (95% CI, 0.893–0.965) for the validation cohort. The calibration curves showed good agreement between the predictions and observations.ConclusionsBoth nomogram and radiomics score can be used as methods to assist radiologists and clinicians in predicting breast malignancy in BI-RADS US category 4 or 5 lesions.

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.

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.

Analysis of breast cancer subtypes and their correlations with receptors and ultrasound.

The study aim was to evaluate the ultrasound (US) signs of the mammary lesions classified in the Breast Imaging-Reporting and Data System (BI-RADS) score category 3, 4, and 5, corresponding to US BI-RADS. It also followed the correlation between US changes of lesions suggestive for malignancy with the histopathological results and evaluated the proper management of those lesions. There were correlations of breast cancer (BC) subtypes with the receptors [estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2)], and Ki67 index, and the signs of conventional ultrasonography and US elastography. We selected 108 female patients examined with US, mammography and fine-needle biopsy who presented suspicions for malignancy lesions. Following the immunohistochemical analysis, they were classified in one of the BC subtypes. According to chi-squared analysis of molecular cancer subtypes correlation to receptors and Ki67 index, we found significant associations between both luminal A and luminal B HER2-negative subtypes and hormone receptors (ER, PR). These have an inverse relationship with Ki67 index elevated values; luminal B HER2-positive subtype has a direct association with HER2 presence; HER2-enriched subtype was statistically significant associated to HER2 presence and elevated Ki67 index values but had an inverse relationship to hormone receptors (ER, PR); triple-negative subtype was strongly associated to Ki67 index values and inversely correlated to ER and PR. We found luminal A subtype as being the most common and luminal B HER2-positive subtype as having the fewer cases.

Likelihood of cancer in breast cancer imaging according to BI-RADS

Background/Aim: Breast cancer is the most common type of cancer among women and one of the most common causes of cancer-related death. Breast Imaging-Reporting and Data System (BI-RADS) is widely used in breast imaging and aims to provide effective communication between physicians. This study aimed to investigate the positive predictive values (PPVs) of BI-RADS categories as assessed by different imaging modalities in reference to Tru-Cut biopsy results. Methods: This retrospective cross-sectional observational study included 415 lesions obtained by Tru-Cut biopsy between March 2018 and December 2020. The lesions were examined by ultrasound (US), mammography, and magnetic resonance imaging (MRI) and categorized as BI-RADS 3, 4, or 5. In this system, every category has its own likelihood of cancer ratio. Results: The most common malign and benign lesions were invasive ductal carcinoma and fibroepithelial lesion, respectively. The PPVs of US BI-RADS category 3, 4, and 5 lesions were 2.15%, 47.44%, and 95.19%, respectively, those of mammographic BI-RADS 3, 4, and 5 lesions were 3.79%, 53.45%, and 94.2%, respectively, and those of MRI BI-RADS 3, 4, and 5 lesions were 0%, 57.89%, and 88.1%, respectively. Conclusion: Predicting the probability of cancer in breast imaging is of significance for patient management and effective communication between the radiologist and other physicians. We demonstrated the compatibility of our experience with the literature with this study, in which we demonstrated the possibility of imaging modalities to predict cancer according to BIRADS categories.

Correlation of ultrasound, cytological, and histological features of 110 benign BI-RADS categories 4C and 5 nonpalpable breast lesions. The Institut Curie's experience.

The purpose of this study was to determine the pathological and ultrasound (US) features of benign nonpalpable breast lesions (NPBLs) classified as Breast Imaging Reporting and Data System (BI-RADS) category 4C or 5. Between 2003 and 2007, 849 consecutive NPBLs detected at US and classified as BI-RADS category 4C (505)or 5 (344) initially underwent US-guided fine needle aspiration (FNA) at our institution. Benign diagnoses were established according to surgical excision findings or during a minimal 6-month imaging follow-up (mean, 3.7 years [SD, 2.6 years]). US BI-RADS features were reviewed and compared retrospectively using a chi-square test for the following pathological categories: epithelial and fibrous proliferation (EFP), cystic and papillary lesion (C&P), inflammatory lesion (IL), benign tumor (BT), intramammary lymph node (ILN), intraepithelial proliferative lesion (IPL), and nonspecific morphological alteration (NMA). The performance of FNA in the diagnosis of benignity was assessed. Of 849 NPBLs, 110 (12.9%) NPBLs were benign: 88 (17.4%) were BI-RADS category 4C, and 22 (6.4%) were BI-RADS category 5. Forty-four (40%) were EFPs, 21 (19%) were C&Ps, 13 (12%) were NMAs, 11 (10%) were ILs, 11 (10%) were BTs, 8 (7%) were IPLs, and 2 (2%) were ILNs. Lesion shape, US pattern distribution, and posterior features showed statistically significant differences between these categories (P < .05): 33 (75%) EFPs exhibited posterior shadowing, 18 (86%) C&Ps were homogenous, 9 (82%) ILs were heterogeneous, 11 (100%) BTs were homogeneous, 9 (82%) BTs were oval, and 6 (75%) IPLs were irregularly shaped. Of the 110 benign NPBLs, FNA diagnosis was falsely positive in 7 (6%), suspicious in 10 (9%), and benign in 90 (82%), and 3 (3%) were inadequate for diagnosis. A diverse array of benign NPBLs can be classified as BI-RADS category 4C or 5 on US, each showing specific imaging presentations.

Multimodal Ultrasound Imaging in Breast Imaging-Reporting and Data System 4 Breast Lesions: A Prediction Model for Malignancy

The purpose of this study was to develop, validate and test a prediction model for discriminating malignant from benign breast lesions using conventional ultrasound (US), US elastography of strain elastography and contrast-enhanced ultrasound (CEUS). The study included 454 patients with breast imaging-reporting and data system (BI-RADS) category 4 breast lesions identified on histologic examinations. Firstly, 228 breast lesions (cohort 1) were analyzed by logistic regression analysis to identify the risk factors, and a breast malignancy prediction model was created. Secondly, the prediction model was validated in cohort 2 (84 patients) and tested in cohort 3 (142 patients) by using analysis of the area under the receiver operating characteristic curve (AUC). Univariate regression indicated that age ≥40 y, taller than wide shape on US, early hyperenhancement on CEUS and enlargement of enhancement area on CEUS were independent risk factors for breast malignancy (all p < 0.05). The logistic regression equation was established as follows: p = 1/1+Exp∑[–5.066 + 3.125 x (if age ≥40 y) + 1.943 x (if taller than wide shape) + 1.479 x (if early hyperenhancement) + 4.167 x (if enlargement of enhancement area). The prediction model showed good discrimination performance with an AUC of 0.967 in cohort 1, 0.948 in cohort 2 and 0.920 in cohort 3. By using the prediction model to selectively downgrade category 4a lesions, the re-rated BI-RADS yield an AUC of 0.880 (95% confidence interval [CI], 0.794–0.965) in cohort 2 and 0.870 (95% CI, 0.801–0.939) in cohort 3. The specificity increased from 0.0% (0/35) to 80.0% (28/35) without loss of sensitivity (from 100.0% to 95.9%, p = 0.153) in cohort 2. Similarly, the specificity increased from 0.0% (0/58) to 77.6% (45/58) without loss of sensitivity (from 100.0% to 96.4%, p = 0.081) in cohort 3. Multimodal US showed good diagnostic performance in predicting breast malignancy of BI-RADS category 4 lesions. Although the loss of sensitivity was existing, the addition of multimodal US to US BI-RADS could improve the specificity in BI-RADS category 4 lesions, which reduced unnecessary biopsies.

The Utility of the Fifth Edition of the BI-RADS Ultrasound Lexicon in Category 4 Breast Lesions: A Prospective Multicenter Study in China

The objective of this study was to evaluate the utility of the fifth edition of the Breast Imaging-Reporting and Data System (BI-RADS) in clinical breast radiology by using prospective multicenter real-time analyses of ultrasound (US) images. We prospectively studied 2049 female patients (age range, 19-86 years; mean age 46.88 years) with BI-RADS category 4 breast masses in 32 tertiary hospitals. All the patients underwent B-mode, color Doppler US, and US elastography examination. US features of the mass and associated features were described and categorized according to the fifth edition of the BI-RADS US lexicon. The pathological results were used as the reference standard. The positive predictive values (PPVs) of subcategories 4a-4c were calculated. A total of 2094 masses were obtained, including 1124 benign masses (54.9%) and 925 malignant masses (45.1%). For BI-RADS US features of mass shape, orientation, margin, posterior features, calcifications, architectural distortion, edema, skin changes, vascularity, and elasticity assessment were significantly different for benign and malignant masses (p< 0.05). Typical signs of malignancy were irregular shape (PPV, 57.2%), spiculated margin (PPV, 83.7%), nonparallel orientation (PPV, 63.9%), and combined pattern of posterior features (PPV, 60.6%). For the changed or newly added US features, the PPVs for intraductal calcifications were 80%, 56.4% for internal vascularity, and 80% for a hard pattern on elastography. The associated features such as architectural distortion (PPV, 89.3%), edema (PPV, 69.2%), and skin changes (PPV, 76.2%) displayed high predictive value for malignancy. The rate of malignant was 7.4% (72/975) in category 4a, 61.4% (283/461) in category 4b, and 93.0% (570/613) in category 4c. The PPV for category 4b was higher than the likelihood ranges specified in BI-RADS and the PPVs for categories 4a and 4c were within the acceptable performance ranges specified in the fifth edition of BI-RADS in our study. Not only the US features of the breast mass, but also associated features, including vascularity and elasticity assessment, have become an indispensable part of the fifth edition of BI-RADS US lexicon to distinguish benign and malignant breast lesions. The subdivision of category 4 lesions into categories 4a, 4b, and 4c for US findings is helpful for further assessment of the likelihood of malignancy of breast lesions.

Can Combined Screening of Ultrasound and Elastography Improve Breast Cancer Identification Compared with MRI in Women with Dense Breasts-a Multicenter Prospective Study.

Objectives: To assess the performance of elastography (ES) and ultrasound (US) in predicting the malignancy of breast lesions and to compare their combined diagnostic value with that of magnetic resonance imaging (MRI).Materials and Methods: The study prospectively enrolled 242 female patients with dense breasts treated in 35 heath care facilities in China between November 2018 and October 2019. Based on conventional US and elastography, radiologists classified the degree of suspicion of breast lesions according to the US Breast Imaging Reporting and Data System (BI-RADS) criteria. The diagnostic value was compared between US BI-RADS and MRI BI-RADS, with pathological results used as the reference standard.Results: The results demonstrated that irregular tumor shape, a nonparallel growth orientation, indistinct margins, angular contours, microcalcifications, color Doppler flow and ES score on US imaging were significantly related to breast cancer in dense breasts (P=0.001; P=0.001; P=0.008; P<0.001; P=0.019; P=0.008; P=0.002, respectively). The sensitivity, specificity, PPV, NPV, accuracy and AUC of US BI-RADS category were 94.7%, 90.7%, 95.8%, 88.0%, 93.4% and 0.93 (95%CI, 0.88-0.97), respectively, while those of MRI BI-RADS category were 98.2%, 57.5%, 84.3%, 83.3%, 86.0% and 0.78 (95%CI, 0.71-0.85), respectively. MRI BI-RADS showed a significantly higher sensitivity than US BI-RADS (98.2% vs 94.7%, P=0.043), whereas US BI-RADS showed significantly higher specificity (90.7% vs 57.5%, P<0.001). US BI-RADS showed better diagnostic efficiency in differentiating nodules in dense breasts than MRI BI-RADS (AUC 0.93 vs 0.78, P<0.001).Conclusion: By combining the use of ES and conventional US, US BI-RADS had better diagnostic efficiency in differentiating nodules in dense breasts than MRI. For the diagnosis of malignant tumors in patients with dense breasts, MRI and US BI-RADS can be used as supplemental diagnostic tools to detect lesions, with US BI-RADS considered the preferred adjunctive resource.

Predicting Breast Cancer in Breast Imaging Reporting and Data System (BI-RADS) Ultrasound Category 4 or 5 Lesions: A Nomogram Combining Radiomics and BI-RADS

Radiomics reflects the texture and morphological features of tumours by quantitatively analysing the grey values of medical images. We aim to develop a nomogram incorporating radiomics and the Breast Imaging Reporting and Data System (BI-RADS) for predicting breast cancer in BI-RADS ultrasound (US) category 4 or 5 lesions. From January 2017 to August 2018, a total of 315 pathologically proven breast lesions were included. Patients from the study population were divided into a training group (n = 211) and a validation group (n = 104) according to a cut-off date of March 1st, 2018. Each lesion was assigned a category (4A, 4B, 4C or 5) according to the second edition of the American College of Radiology (ACR) BI-RADS US. A radiomics score was generated from the US image. A nomogram was developed based on the results of multivariate regression analysis from the training group. Discrimination, calibration and clinical usefulness of the nomogram for predicting breast cancer were assessed in the validation group. The radiomics score included 9 selected radiomics features. The radiomics score and BI-RADS category were independently associated with breast malignancy. The nomogram incorporating the radiomics score and BI-RADS category showed better discrimination (area under the receiver operating characteristic curve [AUC]: 0.928; 95% confidence interval [CI]: 0.876, 0.980) between malignant and benign lesions than either the radiomics score (P = 0.029) or BI-RADS category (P = 0.011). The nomogram demonstrated good calibration and clinical usefulness. In conclusion, the nomogram combining the radiomics score and BI-RADS category is potentially useful for predicting breast malignancy in BI-RADS US category 4 or 5 lesions.

Incorporation of contrast-enhanced ultrasound in the differential diagnosis for breast lesions with inconsistent results on mammography and conventional ultrasound.

To identify the efficacy of contrast-enhanced ultrasound (CEUS) in re-evaluating masses with inconsistent Breast Imaging Reporting and Data System (BI-RADS) on mammography (MG) and conventional ultrasound (US). A total of 637 breast lesions were evaluated with MG, US, and CEUS within 6 months and assessed as BI-RADS MG and US. CEUS was used as an additional screening to rerate BI-RADS US according to a five-point system. Lesions were divided into consistent or inconsistent group on the basis of BI-RADS MG and US assessment. The performance of MG, US, and CEUS in the overall and inconsistent group as well as the clinicopathological differences between consistent and inconsistent group were compared using Z test, Mann-Whitney U test, and t-test. The respective AUCs of MG and US were 0.742, 0.843 for overall group and 0.412, 0.789 for inconsistent group. The corresponding values of rerated CEUS BI-RADS were 0.958 and 0.950, which were significantly prior to those of MG and US (p < 0.001). Younger age, negative lymph node status, and dense breast were significantly associated with inconsistent group. Incorporation of CEUS to re-evaluate lesions can improve the diagnostic efficacy comparing to MG or US alone especially when disagreement occurred.

Combination of different types of elastography in downgrading ultrasound Breast Imaging-Reporting and Data System category 4a breast lesions.

To determine whether a combination of different types of elastography could improve the accuracy of elastography-aided downgrading ultrasound (US) Breast Imaging-Reporting and Data System (BI-RADS) category 4a lesions. From January 2016 to May 2018, 458 consecutive women with 494 US BI-RADS category 4a breast lesions were enrolled in the prospective study. These lesions were subject to conventional US supplemented with strain elastography of elasticity imaging (EI), virtual touch tissue imaging (VTI), and shear wave elastography of virtual touch imaging quantification (VTIQ). Diagnostic performances were calculated for BI-RADS, EI, VTI, and VTIQ as well as the combination of EI, VTI, and VTIQ (combination of EI and VTI [EI + VTI], combination of EI and VTIQ [EI + VTIQ], and combination of VTI and VTIQ [VTI + VTIQ]). Pathologically, 445 lesions (90.1%) were benign, and 49 (9.9%) were malignant. The specificities of EI, VTI, and VTIQ were significantly higher than those of BI-RADS (69.9%, 83.8%, 75.5% vs. 0, respectively, P < 0.001), while their sensitivities were significantly lower than those of BI-RADS (83.7%, 73.5%, 65.3% vs. 100%, respectively, P < 0.05). Among the combinations, EI + VTI and EI + VTIQ showed similar sensitivity to BI-RADS (98% vs 100%, P = 1.000; 93.9% vs 100%, P = 0.25), while the specificity of EI + VTI was significantly higher than that of EI + VTIQ and BI-RADS (P < 0.001). When using EI + VTI to downgrade lesions, 58.7% of these lesions were downgraded, among those 99.7% were benign. Combinations of EI and VTI to downgrade BI-RADS category 4a lesions may reduce the misdiagnosis of breast cancers and the number of unnecessary biopsies.

Application of automated breast volume scanner and mammography in differentiation of small breast lesions with BI-RADS-US 4

To evaluate the value of automatic breast volume scanner (ABVS) and mammography (MG) in differential diagnosis for small breast lesions with breast imaging reporting and data system ultrasound (BI-RADS-US) 4. Methods: ABVS and MG were performed for 103 patients with 109 breast lesions, which were classified as BI-RADS-US 4 by conventional ultrasound (US). Postoperative pathological diagnosis served as gold standard. The diagnostic efficacy for US, US combined with MG, US combined with ABVS and the combination of three methods were compared. Results: The sensitivity of US, US combined with MG, US combined with ABVS and the combination of three methods were 85.5%, 86.8%, 94.7% and 96.0%, respectively. The specificity for them were 66.7%, 69.7%, 81.8% and 84.9%, respectively. The accuracy for them were 79.8%, 81.6%, 90.8% and 92.7%, respectively. The area under the receiver operating characteristic (ROC) curve (AUC) for them were 0.76, 0.78, 0.88 and 0.90, respectively. The accuracy and AUC for US combined with ABVS in differential diagnosis of BI-RADS-US 4 small breast lesions were significantly higher than those of US and US combined with MG (P<0.05). There was no significant difference in sensitivity and specificity among these 3 groups (P>0.05). No significant difference was found in sensitivity, specificity, accuracy, and AUC between US combined with ABVS and the combination of three methods (P>0.05). Conclusion: Combination of US with ABVS can improve the diagnostic efficacy of conventional US in differential diagnosis for BI-RADS-US 4 small breast lesions, and it is superior to US combined with MG.

Sonographic Phenotypes of Molecular Subtypes of Invasive Ductal Cancer in Automated 3-D Breast Ultrasound

Our aim was to investigate whether Breast Imaging Reporting and Data System—Ultrasound (BI-RADS-US) lexicon descriptors can be used as imaging biomarkers to differentiate molecular subtypes (MS) of invasive ductal carcinoma (IDC) in automated breast ultrasound (ABUS). We included 125 IDCs diagnosed between 2010 and 2014 and imaged with ABUS at two institutes retrospectively. IDCs were classified as luminal A or B, HER2 enriched or triple negative based on reports of histopathologic analysis of surgical specimens. Two breast radiologists characterized all IDCs using the BI-RADS-US lexicon and specific ABUS features. Univariate and multivariate analyses were performed. A multinomial logistic regression model was built to predict the MSs from the imaging characteristics. BI-RADS-US descriptor margins and the retraction phenomenon are significantly associated with MSs (both p < 0.001) in both univariate and multivariate analysis. Posterior acoustic features and spiculation pattern severity were only significantly associated in univariate analysis (p < 0.001). Luminal A IDCs tend to have more prominent retraction patterns than luminal B IDCs. HER2-enriched and triple-negative IDCs present significantly less retraction than the luminal subtypes. The mean accuracy of MS prediction was 0.406. Overall, several BI-RADS-US descriptors and the coronal retraction phenomenon and spiculation pattern are associated with MSs, but prediction of MSs on ABUS is limited.

Assessment of Diagnostic Accuracy and Efficiency of Categories 4 and 5 of the Second Edition of the BI-RADS Ultrasound Lexicon in Diagnosing Breast Lesions

The purpose of this study was to evaluate the diagnostic accuracy and efficiency of categories 4 and 5 of the second edition of the Breast Imaging Reporting and Data System (BI-RADS) ultrasound (US) lexicon in diagnosing breast lesions. In our retrospective study, 579 lesions in 544 patients were assessed by US as the preliminary diagnosis and classified in subcategories 4a–4c and category 5 based on the second edition of the BI-RADS US lexicon with some obvious changes, such as the redefined margin, new calcification type, associated features and some special cases. Inter-observer agreement was determined. Ultrasound results were compared with the pathologic results for confirmation. Positive predictive values (PPVs) of subcategories 4a–4c were compared with theoretical values using the χ2 test; the binomial test was used for category 5 lesions. Of the 579 lesions, 212 were confirmed as benign (36.61%), and the remaining 367 lesions were confirmed as borderline/malignant (63.39%). Inter-observer agreement was moderate for subcategories 4a–4c (κ = 0.52), moderate for subcategories 4a–4c and category 5 (κ = 0.56) and substantial for categories 4 and 5 (κ = 0.67). The PPVs for subcategories 4a–4c were 23.74%, 70.67% and 81.25%, respectively. In addition, the total PPV for category 4 was 46.92% (183/390), and the total PPV for category 5 was 97.35% (184/189). Statistical results revealed that the PPVs of subcategories 4a and 4b differed significantly from the theoretical values (p < 0.05); the PPVs of subcategory 4c and category 5 were significantly correlated with the theoretical PPVs (p > 0.05). In conclusion, subcategories 4a and 4b have lower diagnostic efficiency than subcategory 4c and category 5. Inter-observer agreement for subcategories 4a–4c remains to be improved. The most common features of subcategories 4a–4c differ, but overlap. It is recommended that inexperienced doctors in primary hospitals not classify lesions into subcategories in clinical practice.

Value of Virtual Touch Tissue Imaging Quantification for Evaluation of Ultrasound Breast Imaging-Reporting and Data System Category 4 Lesions

The purpose of the study was to evaluate the value of 2-D shear wave elastography (SWE) of virtual touch tissue imaging quantification (VTIQ) for ultrasound (US) Breast Imaging-Reporting and Data System (BI-RADS) category 4 lesions. One hundred sixteen lesions were subject to conventional US, conventional strain elastography (SE) of elasticity imaging (EI), acoustic radiation force impulse (ARFI)-induced SE of virtual touch tissue imaging (VTI) and VTIQ before biopsies. Of the 116 lesions, 69 (59.5%) were benign and 47 (40.5%) were malignant. Significant differences were found between benign and malignant lesions in EI score, VTI score and shear wave speed (SWS) on VTIQ (both p < 0.05). The cut-off values were EI score ≥4, VTI score ≥4 and SWS ≥3.49 m/s, respectively. The diagnostic performance of VTIQ in terms of area under receiver operating characteristic curve (AUROC) were the highest (i.e., AUROC = 0.907), in comparison with EI, VTI alone or a combination of both. The associated sensitivity, specificity and accuracy were 87.2%, 82.6% and 84.5%, respectively. The combination of VTI and VTIQ, however, was similar with US BI-RADS (p = 0.475) in sensitivity in that only two (4.3%) of 47 malignant lesions were misdiagnosed as benign that were BI-RADS category 4b on US. VTIQ is valuable to differentiate benign from malignant BI-RADS category 4 lesions, and the combination of VTI and VTIQ might be useful for patient selection before biopsy.