Diagnostic value of contrast-enhanced ultrasound and shear-wave elastography for small breast nodules.

Background: The study aims to evaluate the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) and shear-wave elastography (SWE) in detecting small malignant breast nodules in an effort to inform further refinements of the Breast Imaging Reporting and Data System (BI-RADS) classification system. Methods: This study retrospectively analyzed patients with breast nodules who underwent conventional ultrasound, CEUS, and SWE at Gongli Hospital from November 2015 to December 2019. The inclusion criteria were nodules ≤ 2 cm in diameter with pathological outcomes determined by biopsy, no prior treatments, and solid or predominantly solid nodules. The exclusion criteria included pregnancy or lactation and low-quality images. Imaging features were detailed and classified per BI-RADS. Diagnostic accuracy was assessed using receiver operating characteristic curves. Results: The study included 302 patients with 305 breast nodules, 113 of which were malignant. The diagnostic accuracy was significantly improved by combining the BI-RADS classification with CEUS and SWE. The combined approach yielded a sensitivity of 88.5%, specificity of 87.0%, positive predictive value of 80.0%, negative predictive value of 92.8%, and accuracy of 87.5% with an area under the curve of 0.877. Notably, 55.8% of BI-RADS 4A nodules were downgraded to BI-RADS 3 and confirmed as benign after pathological examination, suggesting the potential to avoid unnecessary biopsies. Conclusion: The integrated use of the BI-RADS classification, CEUS, and SWE enhances the accuracy of differentiating benign and malignant small breast nodule, potentially reducing the need for unnecessary biopsies.

Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake.

In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually classified into four classes. The susceptibility of such an assessment to inter-reader variability highlights the urgent need for a standardized classification algorithm. In this retrospective study, the first post-contrast subtraction images for 27 healthy female subjects were included. The BPE was classified slice-wise by two expert radiologists. The extraction of radiomic features from segmented BPE was followed by dataset splitting and dimensionality reduction. The latent representations were then utilized as inputs to a deep neural network classifying BPE into BI-RADS classes. The network's predictions were elucidated at the radiomic feature level with Shapley values. The deep neural network achieved a BPE classification accuracy of 84 ± 2% (p-value < 0.00001). Most of the misclassifications involved adjacent classes. Different radiomic features were decisive for the prediction of each BPE class underlying the complexity of the decision boundaries. A highly precise and explainable pipeline for BPE classification was achieved without user- or algorithm-dependent radiomic feature selection.

Segmentation-based BI-RADS ensemble classification of breast tumours in ultrasound images.

BACKGROUND: The development of computer-aided diagnosis systems in breast cancer imaging is exponential. Since 2016, 81 papers have described the automated segmentation of breast lesions in ultrasound images using artificial intelligence. However, only two papers have dealt with complex BI-RADS classifications. PURPOSE: This study addresses the automatic classification of breast lesions into binary classes (benign vs. malignant) and multiple BI-RADS classes based on a single ultrasonographic image. Achieving this task should reduce the subjectivity of an individual operator's assessment. MATERIALS AND METHODS: Automatic image segmentation methods (PraNet, CaraNet and FCBFormer) adapted to the specific segmentation task were investigated using the U-Net model as a reference. A new classification method was developed using an ensemble of selected segmentation approaches. All experiments were performed on publicly available BUS B, OASBUD, BUSI and private datasets. RESULTS: FCBFormer achieved the best outcomes for the segmentation task with intersection over union metric values of 0.81, 0.80 and 0.73 and Dice values of 0.89, 0.87 and 0.82, respectively, for the BUS B, BUSI and OASBUD datasets. Through a series of experiments, we determined that adding an extra 30-pixel margin to the segmentation mask counteracts the potential errors introduced by the segmentation algorithm. An assembly of the full image classifier, bounding box classifier and masked image classifier was the most accurate for binary classification and had the best accuracy (ACC; 0.908), F1 (0.846) and area under the receiver operating characteristics curve (AUROC; 0.871) in the BUS B and ACC (0.982), F1 (0.984) and AUROC (0.998) in the UCC BUS datasets, outperforming each classifier used separately. It was also the most effective for BI-RADS classification, with ACC of 0.953, F1 of 0.920 and AUROC of 0.986 in UCC BUS. Hard voting was the most effective method for dichotomous classification. For the multi-class BI-RADS classification, the soft voting approach was employed. CONCLUSIONS: The proposed new classification approach with an ensemble of segmentation and classification approaches proved more accurate than most published results for binary and multi-class BI-RADS classifications.

Limited impact of adding digital breast tomosynthesis to full field digital mammography in an elevated breast cancer risk population.

PURPOSE: To investigate the impact of adding digital breast tomosynthesis (DBT) to full field digital mammography (FFDM) in screening asymptomatic women with an elevated breast cancer life time risk (BCLTR) but without known genetic mutation. METHODS: This IRB-approved single-institution multi-reader study on prospectively acquired FFDM + DBT images included 429 asymptomatic women (39-69y) with an elevated BC risk on their request form. The BCLTR was calculated for each patient using the IBISrisk calculator v8.0b. The screening protocol and reader study consisted of 4-view FFDM + DBT, which were read by four independent radiologists using the BI-RADS lexicon. Standard of care (SOC) included ultrasound (US) and magnetic resonance imaging (MRI) for women with > 30 % BCLTR. Breast cancer detection rate (BCDR), sensitivity and positive predictive value were assessed for FFDM and FFDM + DBT and detection outcomes were compared with McNemar-test. RESULTS: In total 7/429 women in this clinically elevated breast cancer risk group were diagnosed with BC using SOC (BCDR 16.3/1000) of which 4 were detected with FFDM. Supplemental DBT did not detect additional cancers and BCDR was the same for FFDM vs FFDM + DBT (9.3/1000, McNemar p = 1). Moderate inter-reader agreement for diagnostic BI-RADS score was found for both study arms (ICC for FFDM and FFDM + DBT was 0.43, resp. 0.46). CONCLUSION: In this single institution study, supplemental screening with DBT in addition to standard FFDM did not increase BCDR in this higher-than-average BC risk group, objectively documented using the IBISrisk calculator.

Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study.

The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736-0.903), along with an F1 score of 0.798 (0.594-0.905), precision of 0.806 (0.596-0.896), recall of 0.830 (0.650-0.946), and a Cohen's Kappa (κ) of 0.708 (0.562-0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model's competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes.

Breast MRI-the Importance of Type II and III Dynamic Curves Evaluation and Framing in BI-RADS 4C and 5 Score.

BACKGROUND: Breast Magnetic Resonance Imaging (MRI) offers the highest sensitivity in detecting breast cancer among existing clinical and imaging techniques, making it a crucial component of breast imaging protocols. This study aims to investigate MRI importance in correlation with previous imaging discordant procedures performed as echography and/or mammography to evaluate characteristics and framing in high-risk BI-RADS 4C or 5 categories based on morphological features and kinetic curves of masses found in the breasts of patients from our database. METHODS: A retrospective study with related statistical analysis was performed on a group of 33 cases, selected from a total of 488 patients who underwent breast MRI examinations at SPAD Imaging International S.R.L. Craiova, between 01.01.2021 and 31.12.2023, aged between 33 and 75 years. In all patients, MRI images parameters were analysed. RESULTS: In 33 patients, 23 had a single lesion and 10 had multiple lesions, 9 of them in the ipsilateral breast and, as a particularity, one of them, located in the contralateral breast. In 21 of the total patients with multiple or single lesions they had type III curves, which were classified in the BI-RADS 5 category, considering both criteria-morphology and type of curve, where the other previous techniques had not mentioned an increased risk, hence revealing that the situation in a percentage of 63.63 in the case of MRI investigation proved to be clearly superior. CONCLUSION: Combining both kinetic and morphologic criteria can enhance the diagnostic accuracy of MRI in breast lesion evaluation.

Spatial N-glycomics of the normal breast microenvironment reveals fucosylated and high-mannose N-glycan signatures related to BI-RADS density and ancestry.

Higher breast cancer mortality rates continue to disproportionally affect black women (BW) compared to white women (WW). This disparity is largely due to differences in tumor aggressiveness that can be related to distinct ancestry-associated breast tumor microenvironments (TMEs). Yet, characterization of the normal microenvironment (NME) in breast tissue and how they associate with breast cancer risk factors remains unknown. N-glycans, a glucose metabolism-linked post-translational modification, has not been characterized in normal breast tissue. We hypothesized that normal female breast tissue with distinct Breast Imaging and Reporting Data Systems (BI-RADS) categories have unique microenvironments based on N-glycan signatures that varies with genetic ancestries. Profiles of N-glycans were characterized in normal breast tissue from BW (n = 20) and WW (n = 20) at risk for breast cancer using matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). A total of 176 N-glycans (32 core-fucosylated and 144 noncore-fucosylated) were identified in the NME. We found that certain core-fucosylated, outer-arm fucosylated and high-mannose N-glycan structures had specific intensity patterns and histological distributions in the breast NME dependent on BI-RADS densities and ancestry. Normal breast tissue from BW, and not WW, with heterogeneously dense breast densities followed high-mannose patterns as seen in invasive ductal and lobular carcinomas. Lastly, lifestyles factors (e.g. age, menopausal status, Gail score, BMI, BI-RADS) differentially associated with fucosylated and high-mannose N-glycans based on ancestry. This study aims to decipher the molecular signatures in the breast NME from distinct ancestries towards improving the overall disparities in breast cancer burden.

Deep Learning for Describing Breast Ultrasound Images with BI-RADS Terms.

Breast cancer is the most common cancer in women. Ultrasound is one of the most used techniques for diagnosis, but an expert in the field is necessary to interpret the test. Computer-aided diagnosis (CAD) systems aim to help physicians during this process. Experts use the Breast Imaging-Reporting and Data System (BI-RADS) to describe tumors according to several features (shape, margin, orientation...) and estimate their malignancy, with a common language. To aid in tumor diagnosis with BI-RADS explanations, this paper presents a deep neural network for tumor detection, description, and classification. An expert radiologist described with BI-RADS terms 749 nodules taken from public datasets. The YOLO detection algorithm is used to obtain Regions of Interest (ROIs), and then a model, based on a multi-class classification architecture, receives as input each ROI and outputs the BI-RADS descriptors, the BI-RADS classification (with 6 categories), and a Boolean classification of malignancy. Six hundred of the nodules were used for 10-fold cross-validation (CV) and 149 for testing. The accuracy of this model was compared with state-of-the-art CNNs for the same task. This model outperforms plain classifiers in the agreement with the expert (Cohen's kappa), with a mean over the descriptors of 0.58 in CV and 0.64 in testing, while the second best model yielded kappas of 0.55 and 0.59, respectively. Adding YOLO to the model significantly enhances the performance (0.16 in CV and 0.09 in testing). More importantly, training the model with BI-RADS descriptors enables the explainability of the Boolean malignancy classification without reducing accuracy.

A Prospective Multicenter Study on the Additive Value of Contrast-Enhanced Ultrasound for Biopsy Decision of Ultrasound BI-RADS 4 Breast Lesions.

OBJECTIVE: The main aim of this study was to determine whether the use of contrast-enhanced ultrasound (CEUS) could improve the categorization of suspicious breast lesions based on the Breast Imaging Reporting and Data System (BI-RADS), thereby reducing the number of benign breast lesions referred for biopsy. METHODS: This prospective study, conducted between January 2017 and December 2018, enrolled consenting patients from eight teaching hospitals in China, who had been diagnosed with solid breast lesions classified as BI-RADS 4 using conventional ultrasound. CEUS was performed within 1 wk of diagnosis for reclassification of breast lesions. Histopathological results obtained from core needle biopsies or surgical excision samples served as the reference standard. The simulated biopsy rate and cancer-to-biopsy yield were used to compare the accuracy of CEUS and conventional ultrasound (US). RESULTS: Among the 1490 lesions diagnosed as BI-RADS 4 with conventional ultrasound, 486 malignant and 1004 benign lesions were confirmed based on histology. Following CEUS, 2, 395, and 211 lesions were reclassified as CEUS-based BI-RADS 2, 3, and 5, respectively, while 882 (59%) remained as BI-RADS 4. The actual cancer-to-biopsy yield based on US was 32.6%, which increased to 43.4% when CEUS-based BI-RADS 4A was used as the cut-off point to recommend biopsy. The simulated biopsy rate decreased to 73.4%. Overall, in this preselected BI-RADS 4 population, only 2.5% (12/486) of malignant lesions would have been miscategorized as BI-RADS 3 using CEUS-based reclassification. The diagnostic accuracy, sensitivity, and specificity of contrast-enhanced ultrasound reclassification were 57.65%, 97.53%, and 38.35%, respectively. CONCLUSION: Our collective findings indicate that CEUS is a valuable tool in further triage of BI-RADS category 4 lesions and facilitates a reduction in the number of biopsies while increasing the cancer-to-biopsy yield.

A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions.

OBJECTIVE: The aim of this study is to conduct a systematic evaluation of the diagnostic efficacy of Breast Imaging Reporting and Data System (BI-RADS) 4 benign and malignant breast lesions using magnetic resonance imaging (MRI) radiomics. METHODS: A systematic search identified relevant studies. Eligible studies were screened, assessed for quality, and analyzed for diagnostic accuracy. Subgroup and sensitivity analyses explored heterogeneity, while publication bias, clinical relevance and threshold effect were evaluated. RESULTS: This study analyzed a total of 11 studies involving 1,915 lesions in 1,893 patients with BI-RADS 4 classification. The results showed that the combined sensitivity and specificity of MRI radiomics for diagnosing BI-RADS 4 lesions were 0.88 (95% CI 0.83-0.92) and 0.79 (95% CI 0.72-0.84). The positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 4.2 (95% CI 3.1-5.7), 0.15 (95% CI: 0.10-0.22), and 29.0 (95% CI 15-55). The summary receiver operating characteristic (SROC) analysis yielded an area under the curve (AUC) of 0.90 (95% CI 0.87-0.92), indicating good diagnostic performance. The study found no significant threshold effect or publication bias, and heterogeneity among studies was attributed to various factors like feature selection algorithm, radiomics algorithms, etc. Overall, the results suggest that MRI radiomics has the potential to improve the diagnostic accuracy of BI-RADS 4 lesions and enhance patient outcomes. CONCLUSION: MRI-based radiomics is highly effective in diagnosing BI-RADS 4 benign and malignant breast lesions, enabling improving patients' medical outcomes and quality of life.

Contrast-enhanced Mammography versus MR Imaging of the Breast.

Breast MR imaging and contrast-enhanced mammography (CEM) are both techniques that employ intravenously injected contrast agent to assess breast lesions. This approach is associated with a very high sensitivity for malignant lesions that typically exhibit rapid enhancement due to the leakiness of neovasculature. CEM may be readily available at the breast imaging department and can be performed on the spot. Breast MR imaging provides stronger enhancement than the x-ray-based techniques and offers higher sensitivity. From a patient perspective, both modalities have their benefits and downsides; thus, patient preference could also play a role in the selection of the imaging technique.

Evaluating diagnostic significance: The utilization of elastography and contrast-enhanced ultrasound for differential diagnosis in breast lesions.

OBJECTIVE: The primary aim of this study is to assess the diagnostic efficacy of elastography and contrast-enhanced ultrasound (CEUS) in the identification of breast lesions subsequent to the optimization and correction of the BI-RADS category 4 classification obtained through conventional ultrasound. The objective is to augment both the specificity and accuracy of breast lesion diagnosis, thereby establishing a reliable framework for reducing unnecessary biopsies in clinical settings. METHODS: A cohort comprising 50 cases of breast lesions classified under BI-RADS category 4 was collected during the period from November 2022 and November 2023. These cases were examined utilizing strain elastography (SE), shear wave elastography (SWE), and CEUS. Novel scoring methodologies for ultrasonic elastography (UE) and CEUS were formulated for this investigation. Subsequently, the developed UE and CEUS scoring systems were used to refine and optimize the conventional BI-RADS classification, either in isolation or in conjunction. Based on the revised classification, the benign group was classified as category 3 and the suspected malignant group was classified as category 4a and above, with pathological results serving as the definitive reference standard. The diagnostic efficacy of the optimized UE and CEUS, both independently and in combination, was meticulously scrutinized and compared using receiver operating characteristic (ROC) curve analysis, with pathological findings as the reference standard. RESULTS: Within the study group, malignancy manifested in 11 cases. Prior to the implementation of the optimization criteria, 78% (39 out of 50) of patients underwent biopsies deemed unnecessary. Following the application of optimization criteria, specifically a threshold of≥8.5 points for the UE scoring method and≥6.5 points for the CEUS scoring method, the incidence of unnecessary biopsies diminished significantly. Reduction rates were observed at 53.8% (21 out of 39) with the UE protocol, 56.4% (22 out of 39) with the CEUS protocol, and 89.7% (35 out of 39) with the combined UE and CEUS optimization protocols. CONCLUSION: The diagnostic efficacy of conventional ultrasound BI-RADS category 4 classification for breast lesions is enhanced following optimized correction using UE and CEUS, either independently or in conjunction. The application of the combined protocol demonstrates a notable reduction in the incidence of unnecessary biopsies.

Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance.

Objective: In physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers. Methods: Female patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves. Results: A total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists' group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer. Conclusions: The AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.

The application of computer-aided diagnosis in Breast Imaging Reporting and Data System ultrasound training for residents-a randomized controlled study.

Background: The consistency of Breast Imaging Reporting and Data System (BI-RADS) classification among experienced radiologists is different, which is difficult for inexperienced radiologists to master. This study aims to explore the value of computer-aided diagnosis (CAD) (AI-SONIC breast automatic detection system) in the BI-RADS training for residents. Methods: A total of 12 residents who participated in the first year and the second year of standardized resident training in Ningbo No. 2 Hospital from May 2020 to May 2021 were randomly divided into 3 groups (Group 1, Group 2, Group 3) for BI-RADS training. They were asked to complete 2 tests and questionnaires at the beginning and end of the training. After the first test, the educational materials were given to the residents and reviewed during the breast imaging training month. Group 1 studied independently, Group 2 studied with CAD, and Group 3 was taught face-to-face by experts. The test scores and ultrasonographic descriptors of the residents were evaluated and compared with those of the radiology specialists. The trainees' confidence and recognition degree of CAD were investigated by questionnaire. Results: There was no statistical significance in the scores of residents in the first test among the 3 groups (P=0.637). After training and learning, the scores of all 3 groups of residents were improved in the second test (P=0.006). Group 2 (52±7.30) and Group 3 (54±5.16) scored significantly higher than Group 1 (38±3.65). The consistency of ultrasonographic descriptors and final assessments between the residents and senior radiologists were improved (κ3 > κ2 > κ1), with κ2 and κ3 >0.4 (moderately consistent with experts), and κ1 =0.225 (fairly agreed with experts). The results of the questionnaire showed that the trainees had increased confidence in BI-RADS classification, especially Group 2 (1.5 to 3.5) and Group 3 (1.25 to 3.75). All trainees agreed that CAD was helpful for BI-RADS learning (Likert scale score: 4.75 out of 5) and were willing to use CAD as an aid (4.5, max. 5). Conclusions: The AI-SONIC breast automatic detection system can help residents to quickly master BI-RADS, improve the consistency between residents and experts, and help to improve the confidence of residents in the classification of BI-RADS, which may have potential value in the BI-RADS training for radiology residents. Trial Registration: Chinese Clinical Trial Registry (ChiCTR2400081672).

Assessing the use of the triglyceride-glycemic index (TyG), neutrophil-lymphocyte Ratio (NLR), and platelet-lymphocyte Ratio (PLR) in distinguishing benign and malignant tumors among patients with complaints of breast mass.

Introduction: Breast cancer is a prevalent global health concern characterized by uncontrolled cell growth in breast tissue. In 2020, approximately 2.3 million cases were reported worldwide, with 162,468 new cases and 87,090 fatalities documented in India in 2018. Early diagnosis is crucial for reducing mortality. Our study focused on the use of markers such as the triglyceride-glycemic index and hematological markers to distinguish between benign and malignant breast masses. Methods: A prospective cross-sectional study included female patients with breast mass complaints. The target sample size was 200. Data collection included medical history, clinical breast examination, mammography, cytological assessment via fine-needle aspiration cytology (FNAC), and blood sample collection. The analyzed parameters included neutrophil-to-lymphocyte Ratio (NLR), platelet-to-lymphocyte Ratio (PLR), and triglyceride-glycemic index (TyG). Histopathological examination confirmed the FNAC results. Statistical analysis including propensity score matching, Kolmogorov-Smirnov tests, Mann-Whitney U tests, receiver's operator curve (ROC) analysis, and logistic regression models was conducted using SPSS and R Software. Additional validation was performed on 25 participants. Results: This study included 200 participants. 109 had benign tumors and 91 had malignant tumors. Propensity score matching balanced covariates. NLR did not significantly differ between the groups, while PLR and TyG index differed significantly. NLR correlated strongly with the breast cancer stage, but not with the BI-RADS score. PLR and TyG index showed moderate positive correlations with the BI-RADS score. ROC analysis was used to determine the optimal cutoff values for PLR and TyG index. Logistic regression models combining PLR and TyG index significantly improved malignancy prediction. Conclusions: TyG index and PLR show potential as adjunctive markers for distinguishing breast masses. NLR correlated with cancer stage but not lesion type. Combining TyG and PLR improves prediction, aiding clinical decisions, but large-scale multicenter trials and long-term validation are required for clinical implementation.

Breast Imaging Reporting and Data System (BI-RADS®): a success history and particularities of its use in Brazil.

BI-RADS® is a standardization system for breast imaging reports and results created by the American College of Radiology to initially address the lack of uniformity in mammography reporting. The system consists of a lexicon of descriptors, a reporting structure with final categories and recommended management, and a structure for data collection and auditing. It is accepted worldwide by all specialties involved in the care of breast diseases. Its implementation is related to the Mammography Quality Standards Act initiative in the United States (1992) and breast cancer screening. After its initial creation in 1993, four additional editions were published in 1995, 1998, 2003 and 2013. It is adopted in several countries around the world and has been translated into 6 languages. Successful breast cancer screening programs in high-income countries can be attributed in part to the widespread use of BI-RADS®. This success led to the development of similar classification systems for other organs (e.g., lung, liver, thyroid, ovaries, colon). In 1998, the structured report model was adopted in Brazil. This article highlights the pioneering and successful role of BI-RADS®, created by ACR 30 years ago, on the eve of publishing its sixth edition, which has evolved into a comprehensive quality assurance tool for multiple imaging modalities. And, especially, it contextualizes the importance of recognizing how we are using BI-RADS® in Brazil, from its implementation to the present day, with a focus on breast cancer screening.

Diagnostic accuracy of supplemental three-dimensional breast ultrasound in the work-up of BI-RADS 0 screening recalls.

OBJECTIVE: To evaluate the diagnostic accuracy of supplemental 3D automated breast ultrasound (ABUS) in the diagnostic work-up of BI-RADS 0 recalls. We hypothesized that 3D ABUS may reduce the benign biopsy rate. MATERIALS AND METHODS: In this prospective multicenter diagnostic study, screening participants recalled after a BI-RADS 0 result underwent bilateral 3D ABUS supplemental to usual care: digital breast tomosynthesis (DBT) and targeted hand-held ultrasound (HHUS). Sensitivity, specificity, positive predictive value, and negative predictive value of 3D ABUS, and DBT plus HHUS, were calculated. New 3D ABUS findings and changes of management (biopsy or additional imaging) were recorded. RESULTS: A total of 501 women (median age 55 years, IQR [51-64]) with 525 BI-RADS 0 lesions were included between April 2018 and March 2020. Cancer was diagnosed in 45 patients. 3D ABUS sensitivity was 72.1% (95% CI [57.2-83.4%]), specificity 84.4% (95% CI [80.8-87.4%]), PPV 29.2% (95% CI [21.4-38.5%]), and NPV 97.1% 95.0-98.4%). Sensitivity of DBT plus HHUS was 100% (95% CI [90.2-100%]), specificity 71.4% (95% CI [67.2-75.2%]), PPV 23.8% (95% CI [18.1-30.5%]) and NPV 100% (95% CI [98.7-100%]). Twelve out of 43 (27.9%) malignancies in BI-RADS 0 lesions were missed on 3D ABUS, despite being detected on DBT and/or HHUS. Supplemental 3D ABUS resulted in the detection of 57 new lesions and six extra biopsy procedures, all were benign. CONCLUSION: 3D ABUS in the diagnostic work-up of BI-RADS 0 recalls may miss over a quarter of cancers detected with HHUS and/or DBT and should not be used to omit biopsy. Supplemental 3D ABUS increases the benign biopsy rate. TRIAL REGISTRATION: Dutch Trial Register, available via https://www.onderzoekmetmensen.nl/en/trial/29659 CRITICAL RELEVANCE STATEMENT: Supplemental 3D automated breast ultrasound in the work-up of BI-RADS 0 recalls may miss over a quarter of cancers detected with other methods and should not be used to omit biopsy; ABUS findings did increase benign biopsy rate. KEY POINTS: Automated breast ultrasound (ABUS) may miss over 25% of cancers detectable by alternative methods. Don't rely solely on 3D ABUS to assess indication for biopsy. New findings with supplemental 3D ABUS increase the benign biopsy rate.

Breast imaging reporting and data system for sonography: Positive and negative predictive values of sonographic features in Kumasi, Ghana.

BACKGROUND: Breast cancer is the most common female cancer globally. The method of choice for screening and diagnosing breast cancer is mammography, which is not widely available in Ghana as compared to ultrasonography. This study aimed to evaluate the sonographic features of solid breast lesions using the new sonographic Breast Imaging- Reporting and Data System (BI-RADS-US) lexicon for malignancy with histopathology as the gold standard. METHODS: This was a prospective quantitative study that sonographically scanned female patients with breast masses and consecutively selected cases recommended for core biopsy from May 2018 to May 2021. Sixty (60) solid breast masses were described using the sonographic BI-RADS lexicon features. Lesion description and biopsy results from histopathology were compared and analyzed using Pearson's Chi-square test. Odds ratios, sensitivity, specificity, and predictive values were also calculated. Statistical significance level was set at p ≤ 0.05. RESULTS: Irregular shape (p < 0.0001), spiculated mass margins (p < 0.0001), and not parallel mass orientation (p= 0.0007) were more commonly associated with malignant masses. The sensitivity of breast ultrasound for malignancy was 93.9 % and the specificity was 55.6 % with an overall accuracy rate of 76.6 %. The negative predictive value was 88.7 % and the positive predictive value was 72.1 %. Descriptors like irregular shape, non-parallel orientation, angular and spiculated margins, echogenic halo, and markedly hypoechoic internal content, demonstrated higher odds ratios for malignancy. CONCLUSIONS: This study adds valuable insights to the diagnosis of breast cancer using the sonographic BI-RADS lexicon features. The results demonstrate that specific sonographic descriptors can effectively differentiate between benign and malignant breast masses.

Diagnostic value of qualitative and quantitative enhancement parameters on contrast-enhanced mammography.

PURPOSE: To determine whether qualitative and quantitative enhancement parameters obtained from contrast-enhanced mammography (CEM) can be used in predicting malignancy. METHODS: After review board approval, consecutive 136 suspicious lesions with definite diagnosis were retrospectively analyzed on CEM. Acquisition was routinely started with craniocaudal view and ended with mediolateral oblique view of the affected breast. Lesion conspicuity (low, moderate, high), internal enhancement pattern (homogeneous, heterogeneous, rim), contrast-to-noise ratio (CNR), percentage of signal difference (PSD) and relative enhancement from early to late view were analyzed. PSD and relative enhancements were used to determine patterns of descending, steady or ascending enhancements. Receiver operating characteristic analysis, Cohen's kappa statistics and Spearman correlation tests were used. RESULTS: There were 29 benign and 107 malignant lesions. 64% of the malignant lesions exhibited high conspicuity compared to 14% of the benign lesions (P < 0.001). CNR values were higher in malignant lesions compared to benign ones (P ≤ 0.004). CNR from early view yielded 82% sensitivity, 72% specificity and PSD yielded 79% sensitivity, 65% specificity. Descending pattern and rim enhancement observed in 44% and 21% of breast cancers, respectively, and both provided 96% positive predictive value for malignancy. CONCLUSION: Diagnostic accuracy of quantitative parameters was higher than that of qualitative parameters. High CNR, rim enhancement, and descending pattern were features commonly seen in malignant lesions, while low CNR, homogeneous enhancement, and ascending pattern were commonly seen in benign lesions.

The Impact of Mammographic, Radiologist, and Patient Factors on the Likelihood of Probably Benign (BI-RADS 3) Assessment at Diagnostic Mammography.

OBJECTIVE: To evaluate the association of mammographic, radiologist, and patient factors on BI-RADS 3 assessment at diagnostic mammography in patients recalled from screening mammography. METHODS: This Institutional Review Board-approved retrospective study of consecutive unique diagnostic mammography examinations in asymptomatic patients recalled from screening mammography March 5, 2014, to December 31, 2019, was conducted in a single large United States health care institution. Mammographic features (mass, calcification, distortion, asymmetry), breast density, prior examination, and BI-RADS assessment were extracted from reports by natural language processing. Patient age, race, and ethnicity were extracted from the electronic health record. Radiologist years in practice, recall rate, and number of interpreted diagnostic mammograms were calculated. A mixed effect logistic regression model evaluated factors associated with likelihood of BI-RADS 3 compared with other BI-RADS assessments. RESULTS: A total of 12 080 diagnostic mammography examinations were performed during the study period, yielding 2010 (16.6%) BI-RADS 3 and 10 070 (83.4%) other BI-RADS assessments. Asymmetry (odds ratio [OR] = 6.49, P <.001) and calcification (OR = 5.59, P <.001) were associated with increased likelihood of BI-RADS 3 assessment; distortion (OR = 0.20, P <.001), dense breast parenchyma (OR = 0.82, P <.001), prior examination (OR = 0.63, P = .01), and increasing patient age (OR = 0.99, P <.001) were associated with decreased likelihood. Mass, patient race or ethnicity, and radiologist factors were not significantly associated with BI-RADS 3 assessment. Malignancy rate for BI-RADS 3 lesions was 1.6%. CONCLUSION: Asymmetry and calcifications had an increased likelihood of BI-RADS 3 assessment at diagnostic evaluation with low likelihood of malignancy, while radiologist features had no association.