Breast Imaging Reporting And Data System Category Research Articles

Improving the Diagnostic Performance and Breast Imaging Reporting and Data System Category Agreement of Less Experienced Radiologists by Utilizing Computer-Aided Diagnosis Software for Breast Ultrasound

Abstract This study aimed to assess the effectiveness of intelligence-based computer-aided diagnosis (CAD) software in ultrasound (US) and its potential to improve the diagnostic performance of less experienced radiologists, as well as the agreement on Breast Imaging Reporting and Data System (BI-RADS) categories with the experienced radiologist. Images of 385 breast lesions in 351 female taken from January 2019 to December 2020 were included. Two less experienced radiologists independently reviewed US images with and without CAD assistance, recording final assessments using the BI-RADS category. The diagnostic performance of CAD and radiologists were calculated and compared. Kappa statistics were used to determine agreement between the experienced radiologist and the less experienced radiologists, based on BI-RADS category before and after using CAD software. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CAD software were 95.5%, 71.5%, 81.3%, 69.8%, and 95.9%, respectively, and those were improved in junior radiologist and intermediate-level radiologist after the addition of CAD. Additionally, with the assistance of CAD, the area under the curve was improved for both the junior radiologist and radiologist (0.704 vs 0.847 and 0.876 vs 0.900, P = 0.009, 0.005), although it remained lower than the senior radiologist. The agreement of BI-RADS category between the less experienced and the experienced radiologists showed a significant improvement (P = 0.04, 0.000). The CAD on US could improve less experienced radiologists' diagnostic performance and agreement on BI-RADS categories, making it an effective decision-making tool in clinical practice.

Effectiveness of ultrasound-guided vacuum-assisted excision for treating benign breast lesions.

Ultrasound‑guided vacuum-assisted excision (UGVAE) and breast biopsy are widely used for the diagnosis and treatment of both benign and suspicious breast lesions. In this retrospective study, we aimed to determine the safety of UGVAE for benign breast lesions and provide guidance for clinical practice. We analyzed clinical and pathological data of female patients who had undergone UGVAE between January 2015 and December 2017 at our institution. All breast lesions were categorized according to the Breast Imaging Reporting and Data System (BI-RADS) before performing UGVAE. In our study cohort, UGVAE was used to resect 10,378 breast lesions from 5789 patients, and selected clinical and histopathological data were analyzed. The most common adverse events were postoperative bleeding (0.24%) and skin hypersensitivity (0.67%). The residual lesion rate was 2.27%. Fibroadenomas accounted for most of the benign lesions (7932 of 10,193; 77.82%). Breast cancer was diagnosed in 150 lesions from 128 patients. Multivariable binary logistic regression analyses showed that older age (odds ratio [OR] = 2.034, 95% confidence interval [CI]: 1.668-2.480, p < 0.001), higher BI-RADS category (OR = 9.514, 95% CI: 6.790-13.332, p < 0.001), and larger legion size (OR = 1.048, 95% CI: 1.019-1.077, p = 0.001) were associated with an increased likelihood of breast cancer. Ninety-six patients with breast cancer had undergone follow-up treatment, achieving a 3-year disease-free survival rate of 97.2% and a 3-year overall survival rate of 100%. UGVAE is a safe and effective means of removing benign breast lesions, causing minimal postoperative trauma and fewer complications compared with open surgery. Moreover, UGVAE had little impact on the follow-up treatment and survival of patients diagnosed with breast cancer.

TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines

BackgroundRecently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports’ classification in the respective medical domain. In order to classify radiological reports properly, a high-quality annotated and curated dataset is required. Currently, no publicly available breast imaging-based radiological dataset exists for the classification of Breast Imaging Reporting and Data System (BI-RADS) categories and breast density scores, as characterized by the American College of Radiology (ACR). To tackle this problem, we construct and annotate a breast imaging-based radiological reports dataset and its benchmark results. The dataset was originally in Spanish. Board-certified radiologists collected and annotated it according to the BI-RADS lexicon and categories at the Breast Radiology department, TecSalud Hospitals Monterrey, Mexico. Initially, it was translated into English language using Google Translate. Afterwards, it was preprocessed by removing duplicates and missing values. After preprocessing, the final dataset consists of 5046 unique reports from 5046 patients with an average age of 53 years and 100% women. Furthermore, we used word-level NLP-based embedding techniques, term frequency-inverse document frequency (TF-IDF) and word2vec to extract semantic and syntactic information. We also compared the performance of ML, DL and large language models (LLMs) classifiers for BI-RADS category classification.ResultsThe final breast imaging-based radiological reports dataset contains 5046 unique reports. We compared K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient-Boosting (GB), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT) and Biomedical Generative Pre-trained Transformer (BioGPT) classifiers. It is observed that the BioGPT classifier with preprocessed data performed 6% better with a mean sensitivity of 0.60 (95% confidence interval (CI), 0.391-0.812) compared to the second best performing classifier BERT, which achieved mean sensitivity of 0.54 (95% CI, 0.477-0.607).ConclusionIn this work, we propose a curated and annotated benchmark dataset that can be used for BI-RADS and breast density category classification. We also provide baseline results of most ML, DL and LLMs models for BI-RADS classification that can be used as a starting point for future investigation. The main objective of this investigation is to provide a repository for the investigators who wish to enter the field to push the boundaries further.

Effect of patient age and breast parenchymal density on Breast Imaging-Reporting and Data System (BIRADS-4) Subcategorization.

BI-RADS (Breast Imaging-Reporting and Data System) is a standard radiological risk assessment for breast lesions, including six categories, of which category four is the widest range of likelihood cancer risk (2% to 95%). This study aimed to evaluate the effect of patient age and ACR breast density on the positive predictive value (PPV) of BI-RADS 4 subcategorization (4A, 4B, and 4C). A retrospective study was conducted at King Fahed Hospital (KFH) between September 1, 2021, and June 30, 2022. PPV was calculated based on a histopathological report for all lesions. The correlation was made with patients' age groups ranging from < 45 years, 45-55 years, and > 55 years and four types of breast density on mammography. We used IBM-SPSS for data synthesis. The Chi-square test was used to assess any correlation between variables. A p-value < 0.05 was considered statistically significant. The mean age was 44.62 ±14.32. Of the 248 cases, 81% were benign, 17% were malignant, and 2% were high-risk lesions. The age-related PPV of each BI-RADS category showed no significant differences among all age groups. However, the breast composition-related PPV cases with BI-RADS categories differed significantly within subcategory 4C (p-value<0.001). There were no positive relationships between increasing age and PPV in BI-RADS 4 subcategories 4A, B, and C. Breast composition-related PPV showed significant differences with BI-RADS 4C subcategories.

Ultrasound strain elastography to improve diagnostic performance of breast lesions by reclassifying BI-RADS 3 and 4a lesions: a multicenter diagnostic study.

To investigate the added value of strain elastography (SE) by recategorizing ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) 3 and 4a lesions. A total of 4371 patients underwent US and SE with BI-RADS 2-5 categories solid breast lesions were included from thirty-two hospitals. We evaluated the elastographic images according to elasticity scores (ES) and strain ratios (SR). Three combined methods (BI-RADS+ES, BI-RADS+SR, BI-RADS+ES+SR) and two reclassified methods were used (method one: upgrading BI-RADS 3 and downgrading BI-RADS 4a, method two: downgrading BI-RADS 4a alone). The diagnostic performance and the potential reduction of unnecessary biopsies were evaluated. Combining BI-RADS with SE had a higher area under the curve (AUC) than BI-RADS alone (0.822-0.898 vs. 0.794, P<0.01). For reclassified method one, the sensitivity, specificity, and accuracy were 99.36%, 66.70%, 78.36% for BI-RADS+ES and 98.01%, 66.45%, 77.72% for BI-RADS+SR, and 99.42%, 66.70%, 78.38% for BI-RADS+ES+SR, respectively. For reclassified method two, the sensitivity, specificity, and accuracy were 99.17%, 70.72%, 80.87% for BI-RADS+ES and 97.76%, 81.75%, 87.46% for BI-RADS+SR, and 99.23%, 69.83%, 80.32% for BI-RADS+ES+SR, respectively. Downgrading BI-RADS 4a alone had higher AUC, specificity, and accuracy (P<0.01) and similar sensitivity (P>0.05) to upgrading BI-RADS 3 and downgrading BI-RADS 4a. Combining SE with BI-RADS could help reduce unnecessary biopsies by 17.64%-55.20%. Combining BI-RADS with SE improved the diagnostic performance in distinguishing benign from malignant lesions and could decrease false-positive breast biopsy rates. Downgrading BI-RADS 4a lesions alone might be sufficient for achieving good diagnostic performance. Downgrading BI-RADS category 4a lesions alone had higher AUC, specificity, and accuracy, and similar sensitivity to upgrading or downgrading BI-RADS category 3 and 4a lesions.

Evaluating text and visual diagnostic capabilities of large language models on questions related to the Breast Imaging Reporting and Data System Atlas 5th edition.

This study aimed to evaluate the performance of large language models (LLMs) and multimodal LLMs in interpreting the Breast Imaging Reporting and Data System (BI-RADS) categories and providing clinical management recommendations for breast radiology in text-based and visual questions. This cross-sectional observational study involved two steps. In the first step, we compared ten LLMs (namely ChatGPT 4o, ChatGPT 4, ChatGPT 3.5, Google Gemini 1.5 Pro, Google Gemini 1.0, Microsoft Copilot, Perplexity, Claude 3.5 Sonnet, Claude 3 Opus, and Claude 3 Opus 200K), general radiologists, and a breast radiologist using 100 text-based multiple-choice questions (MCQs) related to the BI-RADS Atlas 5th edition. In the second step, we assessed the performance of five multimodal LLMs (ChatGPT 4o, ChatGPT 4V, Claude 3.5 Sonnet, Claude 3 Opus, and Google Gemini 1.5 Pro) in assigning BI-RADS categories and providing clinical management recommendations on 100 breast ultrasound images. The comparison of correct answers and accuracy by question types was analyzed using McNemar's and chi-squared tests. Management scores were analyzed using the Kruskal- Wallis and Wilcoxon tests. Claude 3.5 Sonnet achieved the highest accuracy in text-based MCQs (90%), followed by ChatGPT 4o (89%), outperforming all other LLMs and general radiologists (78% and 76%) (P < 0.05), except for the Claude 3 Opus models and the breast radiologist (82%) (P > 0.05). Lower-performing LLMs included Google Gemini 1.0 (61%) and ChatGPT 3.5 (60%). Performance across different categories of showed no significant variation among LLMs or radiologists (P > 0.05). For breast ultrasound images, Claude 3.5 Sonnet achieved 59% accuracy, significantly higher than other multimodal LLMs (P < 0.05). Management recommendations were evaluated using a 3-point Likert scale, with Claude 3.5 Sonnet scoring the highest (mean: 2.12 ± 0.97) (P < 0.05). Accuracy varied significantly across BI-RADS categories, except Claude 3 Opus (P < 0.05). Gemini 1.5 Pro failed to answer any BI-RADS 5 questions correctly. Similarly, ChatGPT 4V failed to answer any BI-RADS 1 questions correctly, making them the least accurate in these categories (P < 0.05). Although LLMs such as Claude 3.5 Sonnet and ChatGPT 4o show promise in text-based BI-RADS assessments, their limitations in visual diagnostics suggest they should be used cautiously and under radiologists' supervision to avoid misdiagnoses. This study demonstrates that while LLMs exhibit strong capabilities in text-based BI-RADS assessments, their visual diagnostic abilities are currently limited, necessitating further development and cautious application in clinical practice.

Diagnostic Accuracy and Incremental Value of Contrast-Enhanced Mammography Compared With Full Field Digital Mammography in a Tertiary Cancer Care Center.

To assess the diagnostic accuracy and incremental value of contrast-enhanced mammography (CEM) compared with full-field digital mammography (FFDM). A retrospective analysis was performed with 150 consecutive patients who underwent CEM at our institute between November 2020 and February 2021, fulfilling the inclusion criteria. The first round of analysis included a review of FFDM with an interpretation of findings as per the Breast Imaging Reporting and Data System (BIRADS) lexicon and the assignment of the BIRADS category to the detected abnormalities. After this documentation, a second round of analysis included a review of recombined subtracted images of CEM. The diagnostic accuracy of FFDM and CEM was calculated with histopathology as the gold standard. Among the 150 cases assessed, 202 lesions were detected with histopathological correlation, of which 42 were benign and 160 were malignant. The sensitivity of FFDM was 90.6% compared to 98.12% for CEM. The specificity of FFDM was 66.7% compared to 76.19% for CEM. The negative predictive value (NPV) of FFDM was low, at 65.12%; CEM showed a better NPV, at 91.43%. The positive predictive value (PPV) was almost the same, at 94.01% for CEM and 91.19% for FFDM. The area under the curve (AUC) was superior for CEM compared to that of FFDM, with a value of 0.87. FFDM had a low sensitivity, especially in dense breast parenchyma, at 88.79% and a specificity of 70%, whereas CEM showed a higher sensitivity, specificity, and NPV, measuring 99.14%, 76.67%, and 95.83%, respectively. Superior sensitivity and high NPV for CEM make it a preferable modality compared with FFDM, especially in dense breast parenchyma, where CEM overcomes the limitations of FFDM. We conclude that CEM is superior to FFDM in evaluating the extent of disease, additional satellite lesion detection, and ruling out ambiguous findings.

Background parenchymal enhancement on contrast-enhanced mammography: associations with breast density and patient's characteristics.

To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age. This bicentric retrospective analysis included CEM examinations performed for thework-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE). A total of 200 women (median age 56.8years, interquartile range 50.5-65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482-0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581-0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847-20.281, p = 0.003) and 2.416 (95% CI 1.095-5.332, p = 0.029) respectively. BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status.

Preliminary experiments on interpretable ChatGPT-assisted diagnosis for breast ultrasound radiologists.

Ultrasound is essential for detecting breast lesions. The American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) classification system is widely used, but its subjectivity can lead to inconsistency in diagnostic outcomes. Artificial intelligence (AI) models, such as ChatGPT-3.5, may potentially enhance diagnostic accuracy and efficiency in medical settings. This study aimed to assess the utility of the ChatGPT-3.5 model in generating BI-RADS classifications for breast ultrasound reports and its ability to replicate the "chain of thought" (CoT) in clinical decision-making to improve model interpretability. Breast ultrasound reports were collected, and ChatGPT-3.5 was used to generate diagnoses and treatment plans. We evaluated GPT-4's performance by comparing its generated reports to those from doctors with various levels of experience. We also conducted a Turing test and a consistency analysis. To enhance the interpretability of the model, we applied the CoT method to deconstruct the decision-making chain of the GPT model. A total of 131 patients were evaluated, with 57 doctors participating in the experiment. ChatGPT-3.5 showed promising performance in structure and organization (S&O), professional terminology and expression (PTE), treatment recommendations (TR), and clarity and comprehensibility (C&C). However, improvements are needed in BI-RADS classification, malignancy diagnosis (MD), likelihood of being written by a physician (LWBP), and ultrasound doctor artificial intelligence acceptance (UDAIA). Turing test results indicated that AI-generated reports convincingly resembled human-authored reports. Reproducibility experiments displayed consistent performance. Erroneous report analysis revealed issues related to incorrect diagnosis, inconsistencies, and overdiagnosis. The CoT investigation supports the potential of ChatGPT to replicate the clinical decision-making process and offers insights into AI interpretability. The ChatGPT-3.5 model holds potential as a valuable tool for assisting in the efficient determination of BI-RADS classifications and enhancing diagnostic performance.

Downgrading Breast Imaging Reporting and Data System categories in ultrasound using strain elastography and computer-aided diagnosis system: a multicenter, prospective study.

To determine whether adding elastography strain ratio (SR) and a deep learning based computer-aided diagnosis (CAD) system to breast ultrasound (US) can help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3 and 4a-c categories and avoid unnecessary biopsies. This prospective, multicentre study included 1049 masses (691 benign, 358 malignant) with assigned BI-RADS 3 and 4a-c between 2020 and 2022. CAD results was dichotomized possibly malignant vs. benign. All patients underwent SR and CAD examinations and histopathological findings were the standard of reference. Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after reclassified (new BI-RADS 3) with SR and CAD were the outcome measures. Following the routine conventional breast US assessment, 48.6% (336 of 691 masses) underwent unnecessary biopsies. After reclassifying BI-RADS 4a masses (SR cut-off <2.90, CAD dichotomized possibly benign), 25.62% (177 of 691 masses) underwent an unnecessary biopsies corresponding to a 50.14% (177 vs. 355) reduction of unnecessary biopsies. After reclassification, only 1.72% (9 of 523 masses) malignancies were missed in the new BI-RADS 3 group. Adding SR and CAD to clinical practice may show an optimal performance in reclassifying BI-RADS 4a to 3 categories, and 50.14% masses would be benefit by keeping the rate of undetected malignancies with an acceptable value of 1.72%. Leveraging the potential of SR in conjunction with CAD holds immense promise in substantially reducing the biopsy frequency associated with BI-RADS 3 and 4A lesions, thereby conferring substantial advantages upon patients encompassed within this cohort.

Comparison of Full-Field Digital Mammography with Synthesized Mammography from Tomosynthesis in a Diagnostic population: Prospective Study

Abstract Background Synthesized mammography (SM) refers to two-dimensional (2D) images derived from the digital breast tomosynthesis (DBT) data. It can reduce the radiation dose and scan duration when compared with conventional full-field digital mammography (FFDM) plus tomosynthesis. Purpose To compare the diagnostic performance of 2D FFDM with synthetic mammograms obtained from DBT in a diagnostic population. Materials and Methods A total of 1,468 mammograms with both FFDM and SM + DBT images were obtained and analyzed over 2 years, after obtaining approval from the institute ethics committee. The images were reported and compared as per the 2013 American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon in terms of breast density, morphological features of mass, calcifications, and presence of asymmetry or architectural distortion followed by the BI-RADS category. The agreement between the two modalities was studied using the Kappa value, and the radiation exposure dose was recorded in both groups. Results FFDM and SM + DBT showed strong agreement for breast density, mass characteristics, and detection of calcifications (kappa &gt; 0.8). Downstaging of breast density and mass density were seen by SM + DBT without any statistically significant difference. The nipple–areola complex visualization was poor in SM (50.34 vs. 76.29% in FFDM), and there were SM-specific artifacts mainly related to the reconstruction algorithm. The radiation dose was higher with SM. Conclusion FFDM has comparable performance to SM + DBT in diagnostic setup. The latter may be particularly helpful in patients with dense breasts.

Age‑integrated breast imaging reporting and data system assessment model to improve the accuracy of breast cancer diagnosis.

Early diagnosis is an effective strategy for decreasing breast cancer mortality. Ultrasonography is one of the most predominant imaging modalities for breast cancer owing to its convenience and non-invasiveness. The present study aimed to develop a model that integrates age with Breast Imaging Reporting and Data System (BI-RADS) lexicon to improve diagnostic accuracy of ultrasonography in breast cancer. This retrospective study comprised two cohorts: A training cohort with 975 female patients from Renmin Hospital of Wuhan University (Wuhan, China) and a validation cohort with 500 female patients from Maternal and Child Health Hospital of Hubei Province (Wuhan, China). Logistic regression was used to construct a model combining BI-RADS score with age and to determine the age-based prevalence of breast cancer to predict a cut-off age. The model that integrated age with BI-RADS scores demonstrated the best performance compared with models based solely on age or BI-RADS scores, with an area under the curve (AUC) of 0.872 (95% CI: 0.850-0.894, P<0.001). Furthermore, among participants aged <30 years, the prevalence of breast cancer was lower than the lower limit of the reference range (2%) for BI-RADS subcategory 4A lesions but within the reference range for BI-RADS category 3 lesions, as indicated by linear regression analysis. Therefore, it is recommended that management for this subset of participants are categorized as BI-RADS category 3, meaning that biopsies typically indicated could be replaced with short-term follow-up. In conclusion, the integrated assessment model based on age and BI-RADS may enhance accuracy of ultrasonography in diagnosing breast lesions and young patients with BI-RADS subcategory 4A lesions may be exempted from biopsy.

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

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. 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. 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. 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.

Advancing Breast Cancer Diagnosis: The Impact of Elastography Integration Into Breast Imaging Reporting and Data System (BIRADS) Categorization.

This study evaluates the impact of integrating elastography into the Breast Imaging Reporting and Data System (BIRADS) categorization on breast cancer diagnostics in an African population. It explores the association and agreement between traditional BIRADS and those modified by elastography, as well as between quantitative and qualitative elastography methods. A total of 200 participants who underwent breast imaging as part of their diagnostic evaluation for breast lesions were included in the study. Participant characteristics, including age distribution and indicators for breast cancer diagnoses, were analyzed. Brightness mode (B-mode) findings without elastography were assessed using the BIRADS classification. Elastography was integrated into the BIRADS categorization to evaluate its impact on breast cancer diagnostics.The association and agreement between BIRADS with and without elastography were analyzed. Participants predominantly aged 40-49 showed significant staging differences with the integration of elastography. Traditional B-mode staging identified 29 (49%) of participants in BIRADS stage IV and 14 (23%) in stage V, whereas elastography adjusted these figures significantly, enhancing diagnostic refinement. There was a fair agreement between BIRADS with and without elastography (kappa = 0.322), while a substantial agreement was found between quantitative and qualitative elastography (kappa = 0.674). The results of the study provide evidence that the integration of elastography into BIRADS categorization can significantly improve the accuracy of breast cancer diagnosis in African women. Elastography enhanced lesion characterization, supporting more personalized and precise clinical management. Continued research is needed to fully integrate elastography into routine diagnostic workflows and understand its broader clinical implications in Africa.

Detection and diagnosis of automated breast ultrasound in patients with BI-RADS category 4 microcalcifications: a retrospective study

BackgroundAutomated Breast Ultrasound (AB US) has shown good application value and prospects in breast disease screening and diagnosis. The aim of the study was to explore the ability of AB US to detect and diagnose mammographically Breast Imaging Reporting and Data System (BI-RADS) category 4 microcalcifications.Methods575 pathologically confirmed mammographically BI-RADS category 4 microcalcifications from January 2017 to June 2021 were included. All patients also completed AB US examinations. Based on the final pathological results, analyzed and summarized the AB US image features, and compared the evaluation results with mammography, to explore the detection and diagnostic ability of AB US for these suspicious microcalcifications.Results250 were finally confirmed as malignant and 325 were benign. Mammographic findings including microcalcifications morphology (61/80 with amorphous, coarse heterogeneous and fine pleomorphic, 13/14 with fine-linear or branching), calcification distribution (189/346 with grouped, 40/67 with linear and segmental), associated features (70/96 with asymmetric shadow), higher BI-RADS category with 4B (88/120) and 4 C (73/38) showed higher incidence in malignant lesions, and were the independent factors associated with malignant microcalcifications. 477 (477/575, 83.0%) microcalcifications were detected by AB US, including 223 malignant and 254 benign, with a significantly higher detection rate for malignant lesions (x2 = 12.20, P < 0.001). Logistic regression analysis showed microcalcifications with architectural distortion (odds ratio [OR] = 0.30, P = 0.014), with amorphous, coarse heterogeneous and fine pleomorphic morphology (OR = 3.15, P = 0.037), grouped (OR = 1.90, P = 0.017), liner and segmental distribution (OR = 8.93, P = 0.004) were the independent factors which could affect the detectability of AB US for microcalcifications. In AB US, malignant calcification was more frequent in a mass (104/154) or intraductal (20/32), and with ductal changes (30/41) or architectural distortion (58/68), especially with the both (12/12). BI-RADS category results also showed that AB US had higher sensitivity to malignant calcification than mammography (64.8% vs. 46.8%).ConclusionsAB US has good detectability for mammographically BI-RADS category 4 microcalcifications, especially for malignant lesions. Malignant calcification is more common in a mass and intraductal in AB US, and tend to associated with architectural distortion or duct changes. Also, AB US has higher sensitivity than mammography to malignant microcalcification, which is expected to become an effective supplementary examination method for breast microcalcifications, especially in dense breasts.

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

ObjectiveThe 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. MethodsThis 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). ResultsAmong 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. ConclusionOur 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.

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.

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. 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. 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. 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.

Abstract PO2-29-01: POSITIVE PREDICTIVE VALUE FOR THE MALIGNANCY OF MAMMOGRAPHIC ABNORMALITIS IN A ONE-STOP CLINIC MODEL FOR BREAST CANCER IN COLOMBI

Abstract Breast cancer (BC) is the most prevalent malignancy among women in Colombia, with the greatest incidence rate among malignancies. According to available data through the year 2020, the Colombian female population has officially documented 52,025 cases of breast cancer. This variety of cancer has emerged as the leading cause of cancer-related mortality among Colombian women. Currently, it is essential to emphasize the significance of the BI-RADS (Breast Imaging Reporting and Data System) classification as a means of categorizing patients’ risk levels based on criteria for subsequent breast pathology evaluation and diagnosis. We aimed to assess the positive predictive value of BIRADS IV and V in predicting the risk of breast cancer in the Colombian population. From September 2018 to December 2022, consecutive patients referred for breast mammography and suspected of having breast cancer underwent triple assessment of breast tumor and were included in this retrospective study. 27.937 patients were included with breast imaging report and BIRADS classification system 4 and 5. Patients with a previously diagnosed case of breast cancer and a BIRADS score of 1,2 or 3 were excluded. The positive predictive value of categories 4 and 5 was determined using histopathologic results of ultrasound- or stereotactically-guided biopsies as the gold standard. 46% of 21,442 patients had BACAF biopsies, 39% TRUCUT, and 15% stereotactic. Category 4A was found in 12,865 patients (60%) with a 7% positivity rate, 4B in 8,362 (25%) with 20%, and 4C and 5 in 3,216 (15%) with 75%. BIRADS 4 and 5 had a 20% positive predictive value, with 4,371 positive participants (31% under 49 and 69% over 50). Categories 4 - 5 were found to be beneficial for predicting the likelihood of malignancy, with categories 4C and 5 yielding the highest rates of positive results. Comparable to previous research, the PPV for malignancy in the present investigation was comparable. Citation Format: Kelly Corredor, Sara Gil, Carlos Agudelo, Clara Piedrahita, Melissa Naranjo. POSITIVE PREDICTIVE VALUE FOR THE MALIGNANCY OF MAMMOGRAPHIC ABNORMALITIS IN A ONE-STOP CLINIC MODEL FOR BREAST CANCER IN COLOMBI [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-29-01.

Abstract PO3-07-11: Concordance between abbreviated and complete magnetic resonance protocols for screening woman at moderate and high risk for breast cancer

Abstract Introduction: Patients at high risk for developing breast cancer are potential beneficiaries of personalized screening strategies. Breast magnetic resonance imaging (MRI) has shown high sensitivity, but its broader use remains constrained by issues such as high cost and long examination time. The abbreviated magnetic resonance protocol is presented as an alternative to overcome these challenges. Our primary outcome was to evaluate the concordance of the final report between full and abbreviated protocols. Secondary outcomes included the acquisition and reading time of images from the abbreviated protocol. Methods: A cross-sectional study was conducted on asymptomatic women at moderate and high risk for breast cancer who underwent the full MRI screening protocol in private health institutions in the city of Belo Horizonte, Brazil. The abbreviated protocol was derived from the full protocol by the computer through selection of specific sequences. Each image was independently blind read, and assigned a category from the Breast Imaging - Reporting and Data System (BI-RADS) by at least two examiners. The results were then compared using the Cohen's kappa and Intraclass Correlation Coefficients. Results: The study included 170 women, and a concordance rate of 96,9% was found for the reported BI-RADS category between protocols in this population. A 21-minute reduction was observed in examination time for the abbreviated protocol. Conclusion: The significant concordance between protocols indicates that despite the reduction in acquisition time, number of acquired images, and method's complexity, the abbreviated MRI protocol meets most screening demands. It is, therefore, a viable alternative for cost reduction and improving patients tolerability for the studied population. Table 1. Clinical characteristics of the study population Absolute and percentage data about age, personal risk for breast cancer, family history, and previous genetic testing in the study population. Table 2. Concordance analysis between abbreviated and complete MRI protocols based on the ACR-BIRADS classification - Coen Kappa and Intraclass Correlation Coefficient The Kappa value of 0.9687 (95% CI 0.9426 - 0.9949, p &amp;lt; 0.01) indicates almost perfect concordance between the two protocols. The intraclass correlation coefficient of 0.982 (95% CI 0.97 - 0.987, p &amp;lt; 0.01), also indicates excellent concordance using the two-way random effect models criteria. Citation Format: Mariana Mitraud Ottoni Guedes, Agnaldo Lopes da Silva Filho, Thais Paiva Moraes, Adriene Moraes Campos, Ivie Braga de Paula, Felipe Marcondes de Oliveira Coelho, Barbara Silveira Santana, Bertha Andrade Coelho, Maria Fernanda Sperotto Valadares Gontijo, Leticia Martins De Araujo Campos Linhares, Anna Dias Salvador, Waldeir José de Almeida Junior, José Tadeu Campos de Avelar. Concordance between abbreviated and complete magnetic resonance protocols for screening woman at moderate and high risk for breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-11.

BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study.

Background The performance of publicly available large language models (LLMs) remains unclear for complex clinical tasks. Purpose To evaluate the agreement between human readers and LLMs for Breast Imaging Reporting and Data System (BI-RADS) categories assigned based on breast imaging reports written in three languages and to assess the impact of discordant category assignments on clinical management. Materials and Methods This retrospective study included reports for women who underwent MRI, mammography, and/or US for breast cancer screening or diagnostic purposes at three referral centers. Reports with findings categorized as BI-RADS 1-5 and written in Italian, English, or Dutch were collected between January 2000 and October 2023. Board-certified breast radiologists and the LLMs GPT-3.5 and GPT-4 (OpenAI) and Bard, now called Gemini (Google), assigned BI-RADS categories using only the findings described by the original radiologists. Agreement between human readers and LLMs for BI-RADS categories was assessed using the Gwet agreement coefficient (AC1 value). Frequencies were calculated for changes in BI-RADS category assignments that would affect clinical management (ie, BI-RADS 0 vs BI-RADS 1 or 2 vs BI-RADS 3 vs BI-RADS 4 or 5) and compared using the McNemar test. Results Across 2400 reports, agreement between the original and reviewing radiologists was almost perfect (AC1 = 0.91), while agreement between the original radiologists and GPT-4, GPT-3.5, and Bard was moderate (AC1 = 0.52, 0.48, and 0.42, respectively). Across human readers and LLMs, differences were observed in the frequency of BI-RADS category upgrades or downgrades that would result in changed clinical management (118 of 2400 [4.9%] for human readers, 611 of 2400 [25.5%] for Bard, 573 of 2400 [23.9%] for GPT-3.5, and 435 of 2400 [18.1%] for GPT-4; P < .001) and that would negatively impact clinical management (37 of 2400 [1.5%] for human readers, 435 of 2400 [18.1%] for Bard, 344 of 2400 [14.3%] for GPT-3.5, and 255 of 2400 [10.6%] for GPT-4; P < .001). Conclusion LLMs achieved moderate agreement with human reader-assigned BI-RADS categories across reports written in three languages but also yielded a high percentage of discordant BI-RADS categories that would negatively impact clinical management. © RSNA, 2024 Supplemental material is available for this article.