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Our study develops a computer-aided diagnosis (CAD) system for breast ultrasound by presenting an innovative frequency domain technique for extracting mass irregularity features, thereby significantly boosting tumor classification accuracy. The experimental data consists of 5252 ultrasound breast tumor images, including 2745 benign tumors and 2507 malignant tumors. A Support Vector Machine was employed to classify the tumor as either benign or malignant, and the effectiveness of the proposed features set in distinguishing malignant masses from benign ones was validated. For the constructed CAD system, the performance indices' accuracy, sensitivity, specificity, PPV, and NPV were 92.91%, 89.94%, 91.38%, 90.29%, and 91.45%, respectively, and the area index in the ROC analysis (AUC) was 0.924, demonstrating our method's superiority over traditional spatial gray level dependence (SGLD), the ratio of depth to width, the count of depressions, and orientation features. Therefore, the constructed CAD system with the proposed features will be able to provide a precise and quick distinction between benign and malignant breast tumors with minimal training time in clinical settings. [ABSTRACT FROM AUTHOR]

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. 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). 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. 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. [ABSTRACT FROM AUTHOR]

Breast cancer is a prevalent global health concern and the most commonly diagnosed cancer in women. Mammography, a well-established and widely used screening tool, has greatly contributed to early breast cancer detection. However, understanding mammographic breast composition is also crucial for refining the risk assessment of breast cancer beyond identifying lesions. In contrast to previous studies, we adopt an exploratory approach by using the Swin Transformer, a foundation model for image classification, to classify the four-category breast density. Leveraging this foundation, we fine-tune the model with a small set of mammograms for the purpose of making advancements in breast density classification. This study is experimentally validated to achieve an overall accuracy of 74.96% in the four-category breast density classification, which is a comparable performance to recent counterparts. [ABSTRACT FROM AUTHOR]

Our study develops a computer-aided diagnosis (CAD) system for breast ultrasound by presenting an innovative frequency domain technique for extracting mass irregularity features, thereby significantly boosting tumor classification accuracy. The experimental data consists of 5252 ultrasound breast tumor images, including 2745 benign tumors and 2507 malignant tumors. A Support Vector Machine was employed to classify the tumor as either benign or malignant, and the effectiveness of the proposed features set in distinguishing malignant masses from benign ones was validated. For the constructed CAD system, the performance indices’ accuracy, sensitivity, specificity, PPV, and NPV were 92.91%, 89.94%, 91.38%, 90.29%, and 91.45%, respectively, and the area index in the ROC analysis (AUC) was 0.924, demonstrating our method’s superiority over traditional spatial gray level dependence (SGLD), the ratio of depth to width, the count of depressions, and orientation features. Therefore, the constructed CAD system with the proposed features will be able to provide a precise and quick distinction between benign and malignant breast tumors with minimal training time in clinical settings.

Summary: Background: With the growing demand for breast screening in public health services and clinical care, ultrasound departments in China are facing tremendous challenges. Methods: A cross-sectional nationwide survey was conducted in 5,460 departments providing ultrasound diagnoses in mainland China from 2020 to 2021. The survey included general information about the ultrasound department, the characteristics of sonologists, the use of Breast Imaging Reporting and Data System (BI-RADS) templates, and the diagnostic accuracy rate of breast cancer ultrasound. Findings: There were on average 2.25 sonologists per 10,000 patients in mainland China per year. The average utilization rate of BI-RADS in Chinese hospitals was 87.02%. The GDP per capita of the province (P = 0.008), whether the hospital was specialized (P = 0.002) or a Tier 3 facility (P < 0.001), the percentage of doctors with master's and doctoral degrees (P < 0.001) and doctors ≤35 years (P = 0.005) were significantly and independently associated with the utilization rate of BI-RADS. The average diagnostic accuracy rate of breast cancer ultrasound in Chinese hospitals was 73.64%, and we observed significant positive associations between GDP per capita (P = 0.02), BI-RADS utilization rate (P = 0.019), and breast cancer ultrasound diagnostic accuracy. Interpretation: The utilization of BI-RADS templates effectively improved the diagnostic accuracy of ultrasound. Moreover, the survey summarized the current situation of departments and sonologists providing breast ultrasound diagnosis in mainland China, which helped monitor the development of the discipline and provide information for administrators to meet the growing demand. Funding: This work was supported by Natural Science Foundation of Beijing (7202156) and Foundation of ihecc (2019-C-0646-2).

Purpose: This study aims to determine the impact of prior examination comparison in the final Breast Imaging and Reporting Data System (BI-RADS®) assessment in screening mammography. We compared the incidence of BI-RADS® categories 0, 1, 2, 3, 4, and 5 before and after comparing to the previous exam, with a focus on category 0. We also compared the radiologists' final classification according to their clinical practice experience., Materials and Methods: This is a prospective observational study conducted from August 2020 to February 2021. Two radiologists independently interpreted 3,896 consecutive mammograms. The final BI-RADS® category was given for the mammography blinded to the previous examinations and after comparing the previous examination. We also compared the radiologists' final classification according to their clinical practice experience., Results: A total of 3,656 patients referred for breast mammography were evaluated, and 3,531 screening mammograms were included. The final BI-RADS® classification for mammograms blinded to previous examinations was as follows: 389 (11.02%), 3,017 (85.44%), 35 (0.99%), 87 (2.46%), and 3 (0.08%) for categories 0, 1, 2, 3, 4, and 5, respectively. After comparing the previous examinations, the results were as follows: 103 (2.92%), 3,311 (93.77%), 39 (1.10%), 75 (2.12%), and 3 (0.08%) for categories 0, 1, 2, 3, 4, and 5, respectively. Reader 1 read 1,439 mammograms, while reader 2 read 2,093 mammograms. When comparing the final category of each reader independently, reader 1 classified the mammograms as 91 (7.10%), 1,181 (92.19%), two (0.16%), and seven (0.55%) for blinded mammograms and 0, 1,261 (98.44%), six (0.47%), and 14 (1.09%) for mammograms after prior examination comparison for categories 0, 1 and 2, 3 and 4, respectively. Reader 2 classified the mammograms as 194 (9.27%), 1,181 (87.52%), 27 (1.29%), and 40 (1.91%) for blinded mammograms and one (0.05%), 2,044 (97.71%), 26 (1.24%), and 21 (1.00%) for mammograms after previous exams comparison for categories 0, 1 and 2, 3, and 4, respectively., Conclusion: Comparing previous mammograms of participants in breast cancer screening programs may reduce the number of BI-RADS® category 0 final classifications by 73.8% and the number of positive findings in the final classification, especially for the less experienced readers., Competing Interests: Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Brazil Platform issued approval 24043619.3.0000.0072. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, De Faria Castro Fleury et al.)

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

Rationale and Objectives: The sonographic appearance of benign and malignant breast nodules overlaps to some extent, and we aimed to assess the performance of the Gail model as an adjunctive tool to ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) for predicting the malignancy of nodules.Materials and Methods: From 2018 to 2019, 2607 patients were prospectively enrolled by 35 health care facilities. An individual breast cancer risk was assessed by the Gail model. Based on B-mode US, color Doppler, and elastography, all nodules were evaluated according to the fifth edition of BI-RADS, and these nodules were all confirmed later by pathology.Results: We demonstrated that the Gail model, age, tumor size, tumor shape, growth orientation, margin, contour, acoustic shadowing, microcalcification, presence of duct ectasia, presence of architectural distortion, color Doppler flow, BI-RADS, and elastography score were significantly related to breast cancer (all p < 0.001). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve (AUC) for combining the Gail model with the BI-RADS category were 95.6%, 91.3%, 85.0%, 97.6%, 92.8%, and 0.98, respectively. Combining the Gail model with the BI-RADS showed better diagnostic efficiency than the BI-RADS and Gail model alone (AUC 0.98 vs 0.80, p < 0.001; AUC 0.98 vs 0.55, p < 0.001) and demonstrated a higher specificity than the BI-RADS (91.3% vs 59.4%, p < 0.001).Conclusion: The Gail model could be used to differentiate malignant and benign breast lesions. Combined with the BI-RADS category, the Gail model was adjunctive to US for predicting breast lesions for malignancy. For the diagnosis of malignancy, more attention should be paid to high-risk patients with breast lesions. [ABSTRACT FROM AUTHOR]

Abstract Background Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically evaluate breast tumors from ultrasound images into five categories based on convolutional neural networks (CNNs). Methods This new developed automatic grading system was consisted of two stages, including the tumor identification and the tumor grading. The constructed network for tumor identification, denoted as ROI-CNN, can identify the region contained the tumor from the original breast ultrasound images. The following tumor categorization network, denoted as G-CNN, can generate effective features for differentiating the identified regions of interest (ROIs) into five categories: Category “3”, Category “4A”, Category “4B”, Category “4C”, and Category “5”. Particularly, to promote the predictions identified by the ROI-CNN better tailor to the tumor, refinement procedure based on Level-set was leveraged as a joint between the stage and grading stage. Results We tested the proposed two-stage grading system against 2238 cases with breast tumors in ultrasound images. With the accuracy as an indicator, our automatic computerized evaluation for grading breast tumors exhibited a performance comparable to that of subjective categories determined by physicians. Experimental results show that our two-stage framework can achieve the accuracy of 0.998 on Category “3”, 0.940 on Category “4A”, 0.734 on Category “4B”, 0.922 on Category “4C”, and 0.876 on Category “5”. Conclusion The proposed scheme can extract effective features from the breast ultrasound images for the final classification of breast tumors by decoupling the identification features and classification features with different CNNs. Besides, the proposed scheme can extend the diagnosing of breast tumors in ultrasound images to five sub-categories according to BI-RADS rather than merely distinguishing the breast tumor malignant from benign.

The differential value of ultrasound breast imaging reporting and data system (BI-RADS) combined with computed tomography (CT) in the diagnosis of ≤2 cm benign and malignant breast lesions was studied. 184 patients with ≤2 cm breast lesions were selected as the subjects, and all patients were given ultrasound and CT examination. The pathological results as control, the differential value of ultrasound BI-RADS, CT and their combination in the diagnosis of ≤2 cm benign and malignant breast lesions was analyzed. The results of the pathological examination showed that there were 58 cases (31. 52%) of breast cancer and 126 cases of benign breast lesions (68. 48%). The detection rate of ultrasound was lower than that of CT, but the difference was not statistically significant (P>0.05). The sensitivity, specificity and accuracy of the ultrasound BI-RADS combined with CT in the diagnosis of ≤2 cm benign and malignant breast lesions was 96. 55%, 95. 24%, 95. 65%, it was significantly higher than the single of ultrasound BI-RADS or CT (P<0.05). This study confirms that ultrasound BI-RADS and CT have good differential value for ≤2 cm benign and malignant breast lesions, and the combination of them is more valuable. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To analyze the correlation between ultrasound breast imaging reporting and data system (BI-RADS) category and fluorodeoxyglucose [18F] (18F-FDG) positron emission tomography/computed tomography (PET/CT) findings and their value in breast lesion diagnosis.Materials and Methods: Cases involving hypermetabolic lesions identified by 18F-FDG PET/CT and ultrasound were retrospectively analyzed. The correlation between the maximum standardized uptake values (SUVmax) of the lesions and the BI-RADS grades was calculated. Histologic diagnosis or evidence at the end of a 2-year follow-up as the standard of truth were analyzed to determine the sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) of the diagnostic methods. Area under the curve (AUC) of BI-RADS, SUVmax, and BI-RADS/SUVmax combined were obtained using receiver-operating characteristic curve (ROC) analysis.Results: Of 206 cases, 92 were benign and 114 were malignant. The difference between the SUVmax and the BI-RADS grades was statistically significant (p < 0.001). The critical value of the optimal SUVmax was 2.325, and the accuracy, sensitivity, specificity, PPV, and NPV were 84.5%, 91.2%, 76.1%, 82.5%, and 87.5%, respectively. For diagnosis using BI-RADS, these values were 85.9%, 98.2%, 70.7%, 80.6%, and 97.0%, respectively. ROC analysis of 206 breast lesions for distinguishing benign from malignant lesions yielded AUCs of 0.948, 0.896, and 0.977 for BI-RADS, SUVmax, and BI-RADS/SUVmax combined, respectively. The critical value of the optimal SUVmax in grade 3 and 4 lesions (as determined using BI-RADS) was 2.705, and the accuracy, sensitivity, specificity, PPV, and NPV were 82.6%, 77.8%, 85.7%, 77.8%, and 85.7%, respectively. For diagnosis using BI-RADS in cases with grade 3 and 4 lesions, these values were 68.5%, 94.4%, 51.8%, 55.7%, and 93.5%, respectively. In ROC analysis for distinguishing benign from malignant for BI-RADS grade 3-4 lesions, the AUC of BI-RADS, SUVmax, and BI-RADS/SUVmax combined were 0.731, 0.859, and 0.882, respectively.Conclusion: Both 18F-FDG PET/CT and ultrasound-dependent BI-RADS grading are effective for diagnosing breast lesions. However, in cases of BI-RADS grades 3 and 4, 18F-FDG PET/CT has better specificity and may be useful for further differential diagnosis. [ABSTRACT FROM AUTHOR]

Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.

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)., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2122/coif). The authors have no conflicts of interest to declare., (2024 Translational Cancer Research. All rights reserved.)

Objective · To explore the value of the 2013 version of the ultrasound breast imaging reporting and data system (BI-RADS) classification diagnostic criteria combined with ultrasound shear wave elastography (SWE) to identify benign and malignant breast lesions. Methods · A total of 175 solid breast masses in 155 women were examined with ultrasound, and were judged to be benign or malignant by BI-RADS classification criteria. Then all the masses were examined with shear wave elastography (SWE) , to obtain shear wave quantitative parameters of benign and malignant breast lesions,the pathological results were used as the gold standard to construct the receiver operating characteristic (ROC) curve of the subjects, which were used to compare the diagnostic value of the two methods alone and in combination. Results · The area under curve (AUC) of the BI-RADS classification diagnostic criteria, the Emax value, and the combination of the two methods to differential diagnosis of benign and malignant breast nodules were 0913, 0.884 and 0.957, respectively. Through pairwise comparison, there was significant difference in AUC between the two methods alone and their combination (BI-RADS classification vs. combination: Z=2.883, P=0.002; SWE vs. combination: Z=4.081, P=0.000) . Conclusion · The combination of BI-RADS classification and SWE technology can improve the diagnostic accuracy of breast lesions. [ABSTRACT FROM AUTHOR]

Purpose The aim of this study was to evaluate the positive predictive value (PPV) and the diagnostic performance of the ultrasonographic descriptors in the fifth edition of BI-RADS, comparing with the fourth edition using video clips. Methods From September 2013 to July 2014, 80 breast masses in 74 women (mean age, 47.5±10.7 years) from five institutions of the Korean Society of Breast Imaging were included. Two radiologists individually reviewed the static and video images and analyzed the images according to the fourth and fifth edition of BI-RADS. The PPV of each descriptor was calculated and diagnostic performances between the fourth and fifth editions were compared. Results Of the 80 breast masses, 51 (63.8%) were benign and 29 (36.2%) were malignant. Suspicious ultrasonographic features such as irregular shape, non-parallel orientation, angular or spiculated margins, and combined posterior features showed higher PPV in both editions (all P0.05). The area under the receiver operating characteristics curve was higher in the fourth edition (0.708 to 0.690), without significance (P=0.416). Conclusion The fifth edition of the BI-RADS ultrasound lexicon showed comparable performance to the fourth edition and can be useful in the differential diagnosis of breast masses using ultrasonography.

Background: Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically evaluate breast tumors from ultrasound images into five categories based on convolutional neural networks (CNNs).Methods: This new developed automatic grading system was consisted of two stages, including the tumor identification and the tumor grading. The constructed network for tumor identification, denoted as ROI-CNN, can identify the region contained the tumor from the original breast ultrasound images. The following tumor categorization network, denoted as G-CNN, can generate effective features for differentiating the identified regions of interest (ROIs) into five categories: Category "3", Category "4A", Category "4B", Category "4C", and Category "5". Particularly, to promote the predictions identified by the ROI-CNN better tailor to the tumor, refinement procedure based on Level-set was leveraged as a joint between the stage and grading stage.Results: We tested the proposed two-stage grading system against 2238 cases with breast tumors in ultrasound images. With the accuracy as an indicator, our automatic computerized evaluation for grading breast tumors exhibited a performance comparable to that of subjective categories determined by physicians. Experimental results show that our two-stage framework can achieve the accuracy of 0.998 on Category "3", 0.940 on Category "4A", 0.734 on Category "4B", 0.922 on Category "4C", and 0.876 on Category "5".Conclusion: The proposed scheme can extract effective features from the breast ultrasound images for the final classification of breast tumors by decoupling the identification features and classification features with different CNNs. Besides, the proposed scheme can extend the diagnosing of breast tumors in ultrasound images to five sub-categories according to BI-RADS rather than merely distinguishing the breast tumor malignant from benign. [ABSTRACT FROM AUTHOR]

Abstract: Ultrasound elastography (SW & Strain) is a medical imaging method that maps the elastic properties of soft tissues and provides additional characterization information for recognizing benign and malignant breast tumors over conventional sonography like clinical palpation examination but these mode shows color elasticity maps with quantitative values in real time. Doppler imaging can also help differentiate benign from malignant masses. SMI (superb microvascular imaging), new ultrasound technique for evaluating micro-vessels and is more sensitive in blood flow detection than conventional Doppler. Objective: To compare elastography and superb-microvascular imaging (SMI) with grey scale ultrasound and Doppler respectively in benign and malignant breast lesions. Method: A comparative cross sectional study with convenient sampling technique was used. The study was conducted at Diagnostic Radiology Department of Institute of nuclear medicine and oncology Lahore (INMOL Hospital) from July 2016 to March 2017. A sample of 105 women with symptoms and screen detected breast masses prior to surgery, biopsy or fine-needle aspirations were selected. Results: Comparison of ultrasound elastography, using elasticity score versus B-mode ultrasonography using breast imaging reporting and data system (BI-RADS). Tumors were considered malignant if they have elasticity score 3 or higher and elasticity values (>70 Kpa) along with the high vascularity detection on SMI. The sensitivity and specificity of BI-RADS with elastography was 98.07% and 85.71% respectively. Conclusion: Ultrasound elastography is a noninvasive and reliable method to distinguish low-risk lesions and decreasing pointless interventional diagnostic methods. If we combined B-mode ultrasound with strain, SWE and SMI on the lesions, the diagnostic performance would be improved. [ABSTRACT FROM AUTHOR]

The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities. The objective of this study is to develop a natural language processing (NLP) system for automated BI-RADS categories extraction from breast radiology reports. We evaluated an existing rule-based NLP algorithm, and then we developed and evaluated our own method using a supervised machine learning approach. We divided the BI-RADS category extraction task into two specific tasks: (1) annotation of all BI-RADS category values within a report, (2) classification of the laterality of each BI-RADS category value. We used one algorithm for task 1 and evaluated three algorithms for task 2. Across all evaluations and model training, we used a total of 2159 radiology reports from 18 hospitals, from 2003 to 2015. Performance with the existing rule-based algorithm was not satisfactory. Conditional random fields showed a high performance for task 1 with an F-1 measure of 0.95. Rules from partial decision trees (PART) algorithm showed the best performance across classes for task 2 with a weighted F-1 measure of 0.91 for BIRADS 0-6, and 0.93 for BIRADS 3-5. Classification performance by class showed that performance improved for all classes from Naïve Bayes to Support Vector Machine (SVM), and also from SVM to PART. Our system is able to annotate and classify all BI-RADS mentions present in a single radiology report and can serve as the foundation for future studies that will leverage automated BI-RADS annotation, to provide feedback to radiologists as part of a learning health system loop. [ABSTRACT FROM AUTHOR]

Background: This research embarked on a crucial endeavor to clarify the connection between levels of CD166 expression and the established Breast Imaging Reporting and Data System (BI-RADS) grading system. Through a comprehensive exploration of this correlation, the objective was to ascertain if CD166 could function as an additional biomarker, enhancing the predictive effectiveness of the BI-RADS classification., Method: This prospective observational study involved 81 women with histopathologically confirmed early breast tumors and 81 radiologically confirmed healthy breast volunteers. The BI-RADS scores of all the participants included in the study were recorded. Before starting treatment, serum, saliva, and urine samples were collected. The CD166 levels were quantified using an enzyme-linked immunosorbent assay., Results: The study involved the analysis and comparison of the mean and standard deviations of CD166 expression in serum, saliva, and urine across various BI-RADS categories. Notably, statistically significant differentiation was found (p=0.00) across all samples spanning the spectrum of BI-RADS categories., Conclusion: A progressive rise in CD166 concentration coincides with the increasing gradient of the BI-RADS category, implying a possible link between CD166 and breast cancer progression and severity., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Hemalatha et al.)

Background: High-grade background parenchymal enhancement (BPE), including moderate and marked, poses a considerable challenge for the diagnosis of breast disease due to its tendency to increase the rate of false positives and false negatives. The purpose of our study was to explore whether the Kaiser score can be used for more accurate assessment of benign and malignant lesions in high-grade BPE compared with the Breast Imaging Reporting and Data System (BI-RADS)., Methods: A retrospective review was conducted on consecutive breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans from 2 medical centers. Included were patients who underwent DCE-MRI demonstrating high-grade BPE and who had a pathology-confirmed diagnosis. Excluded were patients who had received neoadjuvant chemotherapy or who had undergone biopsy prior to MRI examination. Two physicians with more than 7 years of experience specializing in breast imaging diagnosis jointly reviewed breast magnetic resonance (MR) images. The Kaiser score was used to determine the sensitivity, specificity, and positive predictive value (PPV), and negative predictive value (NPV) of the BI-RADS from different BPE groups and different enhancement types. The performance of the Kaiser score and BI-RADS were compared according to diagnostic accuracy., Results: A total of 126 cases of high-grade BPE from 2 medical centers were included in this study. The Kaiser score had a higher specificity and PPV than did the BI-RADS (87.5% vs . 46.3%) as well as a higher PPV (94.3% vs . 79.8%). The value of diagnostic accuracy and 95% confidence interval (CI) for the Kaiser score (accuracy 0.928; 95% CI: 0.883-0.973) was larger than that for BI-RADS (accuracy 0.810; 95% CI: 0.741-0.879). Moreover, the Kaiser score had a significantly higher value of diagnostic accuracy for both mass and non-mass enhancement, especially mass lesions (Kaiser score: accuracy 0.947, 95% CI: 0.902-0.992; BI-RADS: accuracy 0.821, 95% CI: 0.782-0.860), with a P value of 0.006., Conclusions: The Kaiser score is a useful diagnostic tool for the evaluation of high-grade BPE lesions, with a higher specificity, PPV, and diagnostic accuracy as compared to the BI-RADS., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-113/coif). SJW was supported by grants from the National Natural Science Foundation of China (Nos. 82022034 and 81871420) and the Jiangsu Province Natural Science Foundation of China (No. BK20200032). The other authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Objectives: To evaluate the diagnostic performance of a new method of combined two-dimensional shear wave elastography (i.e. virtual touch imaging quantification, VTIQ) and ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) in the differential diagnosis of breast lesions.Materials and Methods: From September 2014 to December 2014, 276 patients with 296 pathologically proven breast lesions were enrolled in this study. The conventional US images were interpreted by two independent readers. The diagnosis performances of BI-RADS and combined BI-RADS and VTIQ were evaluated, including the area under the receiver operating characteristic curve (AUROC), sensitivity and specificity. Observer consistency was also evaluated.Results: Pathologically, 212 breast lesions were benign and 84 were malignant. Compared with BI-RADS alone, the AUROCs and specificities of the combined method for both readers increased significantly (AUROC: 0.862 vs. 0.693 in reader 1, 0.861 vs. 0.730 in reader 2; specificity: 91.5 % vs. 38.7 % in reader 1, 94.8 % vs. 47.2 % in reader 2; all P < .05). The Kappa value between the two readers for BI-RADS assessment was 0.614, and 0.796 for the combined method.Conclusion: The combined VTIQ and BI-RADS had a better diagnostic performance in the diagnosis of breast lesions in comparison with BI-RADS alone.Key Points: • Combination of conventional ultrasound and elastography distinguishes breast cancers more effectively. • Combination of conventional ultrasound and elastography increases observer consistency. • BI-RADS weights more than the 2D-SWE with an increase in malignancy probability. [ABSTRACT FROM AUTHOR]

Objective: To evaluate a system we developed that connects natural language processing (NLP) for information extraction from narrative text mammography reports with a Bayesian network for decision-support about breast cancer diagnosis. The ultimate goal of this system is to provide decision support as part of the workflow of producing the radiology report.Materials and Methods: We built a system that uses an NLP information extraction system (which extract BI-RADS descriptors and clinical information from mammography reports) to provide the necessary inputs to a Bayesian network (BN) decision support system (DSS) that estimates lesion malignancy from BI-RADS descriptors. We used this integrated system to predict diagnosis of breast cancer from radiology text reports and evaluated it with a reference standard of 300 mammography reports. We collected two different outputs from the DSS: (1) the probability of malignancy and (2) the BI-RADS final assessment category. Since NLP may produce imperfect inputs to the DSS, we compared the difference between using perfect ("reference standard") structured inputs to the DSS ("RS-DSS") vs NLP-derived inputs ("NLP-DSS") on the output of the DSS using the concordance correlation coefficient. We measured the classification accuracy of the BI-RADS final assessment category when using NLP-DSS, compared with the ground truth category established by the radiologist.Results: The NLP-DSS and RS-DSS had closely matched probabilities, with a mean paired difference of 0.004±0.025. The concordance correlation of these paired measures was 0.95. The accuracy of the NLP-DSS to predict the correct BI-RADS final assessment category was 97.58%.Conclusion: The accuracy of the information extracted from mammography reports using the NLP system was sufficient to provide accurate DSS results. We believe our system could ultimately reduce the variation in practice in mammography related to assessment of malignant lesions and improve management decisions. [ABSTRACT FROM AUTHOR]

Purpose To evaluate the characteristics of low suspicion lesions (BI-RADS 0) at blinded and non-blinded double reading of screening mammograms and to determine the potential effect of arbitration of discrepant BI-RADS 0 recalls by a third reader on screening outcome. Methods We included a series of 84,927 consecutive digital screening mammograms, double read in a blinded (43,184 screens) or non-blinded (41,743 screens) fashion, between July 2009 and July 2011. Discrepant readings were routinely recalled for further evaluation. During 2 years of follow-up, radiology, surgical and pathology reports were collected of all recalled women. Arbitration of discrepant BI-RADS 0 recalls (only one radiologist assigning a BI-RADS 0 score) was retrospectively performed by a third screening radiologist. Results At blinded and non-blinded double reading, 32.0% and 32.5% of recalls were assigned BI-RADS 0 with a positive predictive value (PPV) of 7.2% and 6.8%, respectively. Compared to non-blinded double reading, BI-RADS 0 recalls at blinded double reading showed a higher discrepancy rate (9.0 versus 4.3 per 1000 screens, p < 0.001) and false positive recall rate (10.1 versus 8.4 per 1000 screens, p = 0.012). Arbitration of discrepant BI-RADS 0 recalls would have significantly lowered recall rate (from 3.4% to 2.8% at blinded double reading, p < 0.001, and from 2.8% to 2.5% at non-blinded double reading, p = 0.008), without a decrease in cancer detection rate (from 7.5‰ to 7.3‰, p = 0.751, and from 6.6‰ to 6.5‰, p = 0.832, respectively) and program sensitivity (from 83.2% to 81.2%, p = 0.453, and from 76.0% to 74.6%, p = 0.667, respectively). Arbitration would have significantly increased the PPV at blinded double reading (from 22.3% to 26.3%, p = 0.015). Conclusion We advise arbitration of discrepant BI-RADS 0 recalls, at (non-)blinded double reading of screening mammograms, to reduce recall rates and improve the PPV of recall at blinded double reading. [ABSTRACT FROM AUTHOR]

Abstract: Aim of the work: To evaluate the value of real-time ultrasound elastography (RTE) in differentiating benign from malignant breast masses. Materials and methods: This study included, whether palpable or non-palpable, 145 sonographically proven breast masses in 121 patients, imaged by conventional B-mode US, color-flow Doppler US and RTE with histopathological analysis considered as the golden standard reference. Results: Lesions were differentiated into benign and malignant by conventional B-mode US (79; 45.5% and 66; 54.5%, respectively), RTE (80; 55.2% and 65; 44.8% respectively), and histopathology (82; 56.6% and 63; 43.4%, respectively). The mean difference in the mass size was significant between B-mode US and RTE in malignant masses (P =0.002), while not significant among benign masses (P =0.153). The B-mode US depicted sensitivity of 92.06%, specificity of 90.24%, PPV of 87.88%, NPV of 93.67% and accuracy of 91.03%, while the RTE showed sensitivity of 98.41%, specificity of 96.34%, PPV of 95.38%, NPV of 98.75% and accuracy of 97.24%. Conclusion: Combined use of RTE can complement conventional B-mode US with improving its diagnostic performance in differentiating breast lesions with subsequent reduction in the rate of unnecessary biopsies in benign lesions. [Copyright &y& Elsevier]

An unsolved challenge in biomedical natural language processing (NLP) is detecting ambiguities in the reports that can help physicians to improve report clarity. Our goal was to develop NLP methods to tackle the challenges of identifying ambiguous descriptions of the laterality of BI-RADS Final Assessment Categories in mammography radiology reports. We developed a text processing system that uses a BI-RADS ontology we built as a knowledge source for automatic annotation of the entities in mammography reports relevant to this problem. We used the GATE NLP toolkit and developed customized processing resources for report segmentation, named entity recognition, and detection of mismatches between BI-RADS Final Assessment Categories and mammogram laterality. Our system detected 55 mismatched cases in 190 reports and the accuracy rate was 81%. We conclude that such NLP techniques can detect ambiguities in mammography reports and may reduce discrepancy and variability in reporting. [ABSTRACT FROM AUTHOR]

The objective of this study is to evaluate a natural language processing (NLP) algorithm that determines American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) final assessment categories from radiology reports. This HIPAA-compliant study was granted institutional review board approval with waiver of informed consent. This cross-sectional study involved 1,165 breast imaging reports in the electronic medical record (EMR) from a tertiary care academic breast imaging center from 2009. Reports included screening mammography, diagnostic mammography, breast ultrasound, combined diagnostic mammography and breast ultrasound, and breast magnetic resonance imaging studies. Over 220 reports were included from each study type. The recall (sensitivity) and precision (positive predictive value) of a NLP algorithm to collect BI-RADS final assessment categories stated in the report final text was evaluated against a manual human review standard reference. For all breast imaging reports, the NLP algorithm demonstrated a recall of 100.0 % (95 % confidence interval (CI), 99.7, 100.0 %) and a precision of 96.6 % (95 % CI, 95.4, 97.5 %) for correct identification of BI-RADS final assessment categories. The NLP algorithm demonstrated high recall and precision for extraction of BI-RADS final assessment categories from the free text of breast imaging reports. NLP may provide an accurate, scalable data extraction mechanism from reports within EMRs to create databases to track breast imaging performance measures and facilitate optimal breast cancer population management strategies. [ABSTRACT FROM AUTHOR]

Background: The surrounding tissue of lesions located in the mammary fat layer is mainly fat, not mammary glands. Are the currently used Breast Imaging Reporting and Data System (BI-RADS) classifications and ultrasound elasticity appropriate for such lesions? In the present study, we discuss the value of elastography and BI-RADS classification in the evaluation of masses in the superficial fat layer of the breast., Methods: Seventy-five breast masses within the fat layer that met inclusion criteria were included in the study. Using histopathology as the gold standard, we retrospectively analyzed whether the ultrasound elastography and BI-RADS classification results were consistent with the pathological results., Results: Histological analysis showed that 73 tumors were benign and 2 were malignant. According to the BI-RADS classification and treatment principle, 60% (45/75) of the masses were classified into category 4 and require breast biopsy. But only 4.4% (2/45) of these masses were malignant, and 95.6% (43/45) were overtreated. If we consider the masses with well-defined margins and within the fat layer on the surface of the breast glands as likely benign (BI-RADS category 3), the probability of malignancy is 1.4%. This is consistent with the BI-RADS classification probability of malignancy. According to this BI-RADS classification, only 1.3% (1/75) of patients required biopsy., Conclusions: The findings of this study suggest that breast masses located in the fat layer are prone to be classified into category 4 by BI-RADS and thus be subjected to unnecessary biopsies. Ultrasound elastography can easily misdiagnose benign masses as malignant. It is suggested that ultrasound elastography can downgrade the BI-RADS classification, but not upgrade it. It is more reasonable for these breast masses to be classified as BI-RADS 3 for follow-up observation when the boundary is clear., Competing Interests: Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-22-503/coif). The authors have no conflicts of interest to declare., (2022 Gland Surgery. All rights reserved.)

Introduction: To present the use of digital spot mammography (DSM) in a diagnostic practice. Methods and Patients: Digital spot images of 779 women requiring a spot compression or a spot magnification view were collected. The digital images were acquired on a digital spot upright unit using a 61 mm x 61 mm field of view. Lesions reported included masses, calcifications, and areas of distortions. Results: 1065 lesions required additional views with DSM. Lesions reported included masses (n = 113), masses and microcalcifications (n = 53), spiculated masses (n = 34), architectural distortions (n = 16), and microcalcifications (n = 849). DSMs were considered to be adequate in 97.7% of patients. Unsatisfactory exams resulted from difficulties encountered in targeting the area of interest at the beginning of our experience. Conclusion: DSM, most commonly used to perform interventional procedures, can also be used in a diagnostic practice taking advantage of post-processing of images not available with conventional spot compression and magnification. [ABSTRACT FROM AUTHOR]

Background: 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., Methods: 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., Results: 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., Conclusions: 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., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-21-916/coif). All authors report that this work was supported by The National Natural Science Foundation of China (No. 81801701 to WZ; No. 81901749 to HZ; No. 81901703 to CY; No. 81830058 to CC). The authors have no other conflicts of interest to declare., (2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Background: The breast imaging reporting and data system (BI-RADS) lexicon provides a standardized terminology for describing leision characteristics but does not provide defined rules for converting specific imaging features into diagnostic categories. The inter-reader agreement of the BI-RADS is moderate. In this study, we explored the use of a simplified protocol and scoring system for BI-RADS categorization which integrates the morphologic features (MF), kinetic time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values with equal weights, with a view to providing a convenient and practical method for breast magnetic resonance imaging (MRI) and improving the inter-reader agreement and diagnostic performance of BI-RADS., Methods: This cross-sectional, retrospective, single-center study included 879 patients with 898 histopathologically verified lesions who underwent an MRI scan on a 3.0 Tesla GE Discovery 750 MRI scanner between January 1, 2017, and June 30, 2020. The BI-RADS categorization of the studied lesions was assessed according to the sum of the assigned scores (the presence of malignant MF, lower ADC, and suspicious TIC each warranted a score of +1). Total scores of +2 and +3 were classified as category 5, scores of +1 were classified as category 4, and scores of +0 but with other lesions of interest were classified as category 3. The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity, and accuracy of this categorization were investigated to assess its efficacy and its consistency with pathology., Results: There were 472 malignant, 104 risk, and 322 benign lesions. Our simplified scoring protocol had high diagnostic accuracy, with an area under curve (AUC) value of 0.896. In terms of the borderline effect of pathological risk and category 4 lesions, our results showed that when risk lesions were classified together with malignant ones, the AUC value improved (0.876 vs. 0.844 and 0.909 vs. 0.900). When category 4 and 5 lesions were classified as malignant, the specificity, accuracy, and AUC value decreased (82.3% vs. 93.2%, 89.3% vs. 90.2%, and 0.876 vs. 0.909, respectively). Therefore, to improve the diagnostic accuracy of the protocol for BI-RADS categorization, only category 5 lesions should be considered to be malignant., Conclusions: Our simplified scoring protocol that integrates MF, TIC, and ADC values with equal weights for BI-RADS categorization could improve both the diagnostic performance of the protocol for BI-RADS categorization in clinical practice and the understanding of the benign-risk-malignant breast diseases., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-21-1036/coif). The authors have no conflicts of interest to declare., (2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women. [ABSTRACT FROM AUTHOR]

Background: Ultrasound is commonly used in breast cancer screening but lacks quantification ability and diagnostic power due to its low specificity, which can lead to overdiagnosis and unnecessary biopsies. This study evaluated the diagnostic efficacy and clinical utility of adding shear-wave elastography (SWE) to the screening of the Breast Imaging Reporting and Data System (BI-RADS) category 4 breast cancer., Methods: A machine learning-based diagnostic model was constructed using data retrospectively collected from 3 independent cohorts with features selected using lasso regression and support vector machine-recursive feature elimination algorithms. Propensity score matching (PSM) was used to preclude confounding baseline characteristics between malignant and benign lesions. A decision curve analysis (DCA) was used to evaluate the clinical benefit of the diagnostic model in identifying high-risk tumor patients for intervention while simultaneously avoiding overtreatment of low-risk patients with integrative evaluation using a net benefit value and treatment reduction rate., Results: In our training center, a total of 122 patients were enrolled, and 577 breast tumors were collected. The comparison between malignant and benign lesions revealed significant differences in patient age, tumor size, resistance index (RI), and elasticity values. The maximum elasticity value (Emax) was identified as an independent diagnostic feature and was included in the diagnostic model. The combination of Emax with BI-RADS category 4 demonstrated a significantly better diagnostic efficacy than the BI-RADS category alone [BI-RADS+Emax: AUC =0.908, 95% confidence interval (CI): 0.842-0.974; BI-RADS: AUC =0.862, 95% CI: 0.784-0.94; P=0.024] and significantly increased the clinical benefit for patients and policy makers by effectively reducing overdiagnosis and biopsy rates. In the BI-RADS category 4A subgroup, adding Emax to breast cancer screening benefited patients and showed a greater absolute benefit than did the BI-RADS category alone when used for patients with a higher probability of cancer (>0.403), demonstrating a 50% overtreatment reduction., Conclusions: Adding Emax to BI-RADS category 4 breast cancer screening using SWE significantly reduced overdiagnosis and biopsy rates compared with the BI-RADS category alone, especially for BI-RADS 4A patients., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-21-341). The authors have no conflicts of interest to declare., (2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Objective: The aim of the study was to investigate the accuracy of breast Magnetic Resonance Imaging (MRI) for evaluating residual tumor size following Neoadjuvant Chemotherapy (NAC) and to identify clinicopathologic and MRI features affecting its accuracy., Materials and Methods: We retrospectively assessed 109 women who underwent preoperative Dynamic Contrast-Enhanced (DCE) MRI following NAC and subsequent surgery between April 2016 and August 2020. Preoperative MRI features, including Breast Imaging Reporting and Data System lexicon characteristics, size of residual enhancing lesion, tumor shrinkage pattern, and clinicopathologic features, were investigated, and MRI and pathology findings were compared., Results: Residual tumor size on MRI showed high agreement with residual invasive tumor size on pathologic examination (ICC, 0.808, p<0.001). The residual tumor size measured by MRI and final pathologic size were concordant in 63/109 cases (57.8%), while MRI overestimated the size in 35/109 cases (32.1%). For estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative tumors, MRI tended to underestimate the residual tumor size compared with HER2-positive cancers (p=0.002) and triple-negative cancers (p=0.12). On MRI, tumors with concentric shrinkage patterns after NAC showed less size discrepancy with final pathologic tumor size than those with non-concentric patterns (p=0.026)., Conclusion: In ER-positive/HER2-negative cancers, MRI tends to underestimate the residual tumor size, compared to in other subtypes. Tumors with concentric shrinkage patterns after NAC showed less MRI/pathology size discrepancy., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Contrast-enhanced mammography (CEM) is a promising breast imaging technique. A limited number of studies have focused on the radiomics analysis of CEM. We intended to explore whether a model constructed with both clinical and radiomics features of CEM can better classify benign and malignant breast lesions., Methods: This retrospective, double-center study included women who underwent CEM between August 2017 and February 2020. The data from Center 1 were used as training set and the data from Center 2 were used as external testing set (training: testing =2:1). Models were constructed with the clinical, radiomics, and clinical + radiomics features of CEM. The clinical features included patient age and clinical image features interpreted by the radiologists. The radiomics features were extracted from high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images of CEM. The Mann-Whitney U test, Pearson correlation and Boruta's approach were used to select the radiomics features. Random Forest (RF) and logistic regression were used to establish the models. For the testing set, the areas under the curve (AUCs) and 95% confidence intervals (CIs) were employed to evaluate the performance of the models. For the training set, the mean AUCs were obtained by performing internal validation for 100 iterations and then compared by the Kruskal-Wallis and Mann-Whitney U tests., Results: A total of 226 women (mean age: 47.4±10.1 years) with 226 pathologically proven breast lesions (101 benign; 125 malignant) were included. For the external testing set, the AUCs were 0.964 (95% CI: 0.918-1.000) for the combined model, 0.947 (95% CI: 0.891-0.997) for the radiomics model, and 0.882 (95% CI: 0.803-0.962) for the clinical model. In the internal validation process, the combined model achieved a mean AUC of 0.934±0.030, which was significantly higher than those of the radiomics (mean AUC =0.921±0.031, adjusted P<0.050) and clinical models (mean AUC =0.907±0.036; adjusted P<0.050)., Conclusions: Incorporating both clinical and radiomics features of CEM may achieve better classification results for breast lesions., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-21-103). SD is an employee of General Electric (GE) Healthcare (Shanghai, China). The other authors have no conflicts of interest to declare., (2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Background: Computer-aided diagnosis (CAD) is a tool that can help radiologists diagnose breast lesions by ultrasonography. Previous studies have demonstrated that CAD can help reduce the incidence of missed diagnoses by radiologists. However, the optimal method to apply CAD to breast lesions using diagnostic planes has not been assessed., Objective: The aim of this study was to compare the performance of radiologists with different levels of experience when using CAD with the quadri-planes method to detect breast tumors., Methods: From November 2018 to October 2019, we enrolled patients in the study who had a breast mass as their most prominent symptom. We assigned 2 ultrasound radiologists (with 1 and 5 years of experience, respectively) to read breast ultrasonography images without CAD and then to perform a second reading while applying CAD with the quadri-planes method. We then compared the diagnostic performance of the readers for the 2 readings (without and with CAD). The McNemar test for paired data was used for statistical analysis., Results: A total of 331 patients were included in this study (mean age 43.88 years, range 17-70, SD 12.10), including 512 lesions (mean diameter 1.85 centimeters, SD 1.19; range 0.26-9.5); 200/512 (39.1%) were malignant, and 312/512 (60.9%) were benign. For CAD, the area under the receiver operating characteristic curve (AUC) improved significantly from 0.76 (95% CI 0.71-0.79) with the cross-planes method to 0.84 (95% CI 0.80-0.88; P<.001) with the quadri-planes method. For the novice reader, the AUC significantly improved from 0.73 (95% CI 0.69-0.78) for the without-CAD mode to 0.83 (95% CI 0.80-0.87; P<.001) for the combined-CAD mode with the quadri-planes method. For the experienced reader, the AUC improved from 0.85 (95% CI 0.81-0.88) to 0.87 (95% CI 0.84-0.91; P=.15). The kappa indicating consistency between the experienced reader and the novice reader for the combined-CAD mode was 0.63. For the novice reader, the sensitivity significantly improved from 60.0% for the without-CAD mode to 79.0% for the combined-CAD mode (P=.004). The specificity, negative predictive value, positive predictive value, and accuracy improved from 84.9% to 87.8% (P=.53), 76.8% to 86.7% (P=.07), 71.9% to 80.6% (P=.13), and 75.2% to 84.4% (P=.12), respectively. For the experienced reader, the sensitivity improved significantly from 76.0% for the without-CAD mode to 87.0% for the combined-CAD mode (P=.045). The NPV and accuracy moderately improved from 85.8% and 86.3% to 91.0% (P=.27) and 87.0% (P=.84), respectively. The specificity and positive predictive value decreased from 87.4% to 81.3% (P=.25) and from 87.2% to 93.0% (P=.16), respectively., Conclusions: S-Detect is a feasible diagnostic tool that can improve the sensitivity, accuracy, and AUC of the quadri-planes method for both novice and experienced readers while also improving the specificity for the novice reader. It demonstrates important application value in the clinical diagnosis of breast cancer., Trial Registration: ChiCTR.org.cn 1800019649; http://www.chictr.org.cn/showproj.aspx?proj=33094., (©Liang Yongping, Zhang Juan, Ping Zhou, Zhao Yongfeng, Wengang Liu, Yifan Shi. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.05.2020.)

Rationale and Objectives: To analyze the correlation between ultrasound breast imaging reporting and data system (BI-RADS) category and fluorodeoxyglucose [ 18 F] ( 18 F-FDG) positron emission tomography/computed tomography (PET/CT) findings and their value in breast lesion diagnosis., Materials and Methods: Cases involving hypermetabolic lesions identified by 18 F-FDG PET/CT and ultrasound were retrospectively analyzed. The correlation between the maximum standardized uptake values (SUV max ) of the lesions and the BI-RADS grades was calculated. Histologic diagnosis or evidence at the end of a 2-year follow-up as the standard of truth were analyzed to determine the sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) of the diagnostic methods. Area under the curve (AUC) of BI-RADS, SUV max , and BI-RADS/SUV max combined were obtained using receiver-operating characteristic curve (ROC) analysis., Results: Of 206 cases, 92 were benign and 114 were malignant. The difference between the SUV max and the BI-RADS grades was statistically significant (p < 0.001). The critical value of the optimal SUV max was 2.325, and the accuracy, sensitivity, specificity, PPV, and NPV were 84.5%, 91.2%, 76.1%, 82.5%, and 87.5%, respectively. For diagnosis using BI-RADS, these values were 85.9%, 98.2%, 70.7%, 80.6%, and 97.0%, respectively. ROC analysis of 206 breast lesions for distinguishing benign from malignant lesions yielded AUCs of 0.948, 0.896, and 0.977 for BI-RADS, SUV max , and BI-RADS/SUV max combined, respectively. The critical value of the optimal SUV max in grade 3 and 4 lesions (as determined using BI-RADS) was 2.705, and the accuracy, sensitivity, specificity, PPV, and NPV were 82.6%, 77.8%, 85.7%, 77.8%, and 85.7%, respectively. For diagnosis using BI-RADS in cases with grade 3 and 4 lesions, these values were 68.5%, 94.4%, 51.8%, 55.7%, and 93.5%, respectively. In ROC analysis for distinguishing benign from malignant for BI-RADS grade 3-4 lesions, the AUC of BI-RADS, SUV max , and BI-RADS/SUV max combined were 0.731, 0.859, and 0.882, respectively., Conclusion: Both 18 F-FDG PET/CT and ultrasound-dependent BI-RADS grading are effective for diagnosing breast lesions. However, in cases of BI-RADS grades 3 and 4, 18 F-FDG PET/CT has better specificity and may be useful for further differential diagnosis., (Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Background: Computer-aided diagnosis (CAD) is used as an aid tool by radiologists on breast lesion diagnosis in ultrasonography. Previous studies demonstrated that CAD can improve the diagnosis performance of radiologists. However, the optimal use of CAD on breast lesions according to size (below or above 2 cm) has not been assessed., Objective: The aim of this study was to compare the performance of different radiologists using CAD to detect breast tumors less and more than 2 cm in size., Methods: We prospectively enrolled 261 consecutive patients (mean age 43 years; age range 17-70 years), including 398 lesions (148 lesions>2 cm, 79 malignant and 69 benign; 250 lesions≤2 cm, 71 malignant and 179 benign) with breast mass as the prominent symptom. One novice radiologist with 1 year of ultrasonography experience and one experienced radiologist with 5 years of ultrasonography experience were each assigned to read the ultrasonography images without CAD, and then again at a second reading while applying the CAD S-Detect. We then compared the diagnostic performance of the readers in the two readings (without and combined with CAD) with breast imaging. The McNemar test for paired data was used for statistical analysis., Results: For the novice reader, the area under the receiver operating characteristic curve (AUC) improved from 0.74 (95% CI 0.67-0.82) from the without-CAD mode to 0.88 (95% CI 0.83-0.93; P<.001) at the combined-CAD mode in lesions≤2 cm. For the experienced reader, the AUC improved from 0.84 (95% CI 0.77-0.90) to 0.90 (95% CI 0.86-0.94; P=.002). In lesions>2 cm, the AUC moderately decreased from 0.81 to 0.80 (novice reader) and from 0.90 to 0.82 (experienced reader). The sensitivity of the novice and experienced reader in lesions≤2 cm improved from 61.97% and 73.23% at the without-CAD mode to 90.14% and 97.18% (both P<.001) at the combined-CAD mode, respectively., Conclusions: S-Detect is a feasible diagnostic tool that can improve the sensitivity for both novice and experienced readers, while also improving the negative predictive value and AUC for lesions≤2 cm, demonstrating important application value in the clinical diagnosis of breast cancer., Trial Registration: Chinese Clinical Trial Registry ChiCTR1800019649; http://www.chictr.org.cn/showprojen.aspx?proj=33094., (©Liang Yongping, Ping Zhou, Zhang Juan, Zhao Yongfeng, Wengang Liu, Yifan Shi. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 02.03.2020.)

Background: The malignant probability of Breast Imaging Reporting and Data System (BI-RADS) 4 breast lesions is 3-94%, which is a very large span, and thus leads to a high rate of unnecessary biopsy. Therefore, the differential diagnosis of benign and malignant BI-RADS 4 breast lesions has become extremely important. Thus, in this paper, we investigated the diagnostic value of conventional ultrasonography (US), contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) for BI-RADS 4 breast lesions, and tried to figure out a multi-mode ultrasonic method for them., Methods: From March 2016 to May 2017, 118 breast lesions that were categorized as BI-RADS 4 lesions by US were studied with CEUS and SWE. All the lesions were confirmed by pathology via surgery or vacuum-assisted biopsy. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of US, CEUS and SWE were analyzed. Then the diagnostic efficacies of US, CEUS, SWE and the combination of these modalities were compared. Logistic regression analysis was performed to identify the independent risk factors. A multi-mode method to evaluate BI-RADS 4 lesions based on the logistic regression was developed., Results: Of the 118 BI-RADS 4 lesions, 74 lesions (62.7%) were benign and 44 lesions (37.3%) were malignant. The diagnostic sensitivity and specificity for US, US + CEUS, US + SWE, US + CEUS + SWE were 88.6% and 75.7%, 86.4% and 94.6%, 88.6% and 90.5%, 97.7% and 93.2%, respectively. The area under the ROC curve (AUC) of US + SWE + CEUS was significantly higher than that of US (P<0.0001), US + CEUS (P=0.020), but there was no significant difference between the AUC of US + SWE + CEUS and the AUC of US + SWE., Conclusions: US + CEUS + SWE and US + SWE could significantly improve the diagnostic efficiency and accuracy of US in the diagnosis of BI-RADS 4 breast lesions., Competing Interests: Conflicts of Interest: The authors have no conflicts of interest to declare.

Background Radiology reports are usually narrative, unstructured text, a format which hinders the ability to input report contents into decision support systems. In addition, reports often describe multiple lesions, and it is challenging to automatically extract information on each lesion and its relationships to characteristics, anatomic locations, and other information that describes it. The goal of our work is to develop natural language processing (NLP) methods to recognize each lesion in free-text mammography reports and to extract its corresponding relationships, producing a complete information frame for each lesion. Materials and methods We built an NLP information extraction pipeline in the General Architecture for Text Engineering (GATE) NLP toolkit. Sequential processing modules are executed, producing an output information frame required for a mammography decision support system. Each lesion described in the report is identified by linking it with its anatomic location in the breast. In order to evaluate our system, we selected 300 mammography reports from a hospital report database. Results The gold standard contained 797 lesions, and our system detected 815 lesions (780 true positives, 35 false positives, and 17 false negatives). The precision of detecting all the imaging observations with their modifiers was 94.9, recall was 90.9, and the F measure was 92.8. Conclusions Our NLP system extracts each imaging observation and its characteristics from mammography reports. Although our application focuses on the domain of mammography, we believe our approach can generalize to other domains and may narrow the gap between unstructured clinical report text and structured information extraction needed for data mining and decision support. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to develop a quantitative approach for combining individual American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) sonographic features of breast masses for assessing the overall probability of malignancy., Methods: Sonograms of solid breast masses were analyzed by 2 observers blinded to patient age, mammographic features, and lesion pathologic findings. BI-RADS sonographic features were determined by using American College of Radiology criteria. A naïve Bayes model was used to determine the probability of malignancy of all the sonographic features together and with age and BI-RADS mammographic features. The diagnostic performance for various combinations was evaluated by using the area under the receiver operating curve (Az)., Results: Sonographic features had high positive and negative predictive values. The Az values for BI-RADS sonographic features for the 2 observers ranged from 0.772 to 0.884, which increased to 0.866 to 0.924 when used with patient age and BI-RADS mammographic features. The benefit of adding age and mammographic information was more marked for the observer with lower initial diagnostic performance. Age-specific analysis showed that diagnostic performance varied with age, with higher performance for patients aged 45 years and younger and patients older than 60 years compared to those aged 46 to 60 years. In 85% of cases, the diagnosis of the observers matched. When the consensus between the observers was used for diagnostic decisions, a high level of diagnostic performance (Az, 0.954) was achieved., Conclusions: A naïve Bayes model provides a systematic approach for combining sonographic features and other patient characteristics for assessing the probability of malignancy to differentiate malignant and benign breast masses.