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

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

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

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

The utility of quantifying the orientation of breast masses in ultrasound imaging.

The aim of this study was to quantify the orientation of breast masses and determine whether it can enhance the utility of a not parallel orientation in predicting breast mass malignancy. A total of 15,746 subjects who underwent breast ultrasound examinations were initially enrolled in the study. Further evaluation was performed on subjects with solid breast masses (≤ 5 cm) intended for surgical resection and/or biopsy. The orientation angle, defined as the acute angle between the align of the maximal longitudinal diameter of the breast mass and the surface of the breast skin, was measured. Receiver operating characteristic (ROC) curve analysis was conducted, and various performance measures including sensitivity, specificity, positive and negative predictive values, accuracy, odds ratio, and the area under the ROC curve (AUC) were calculated. Multivariate analysis was performed to determine if the orientation angle was an independent predictor of breast malignancy. Decision curve analysis (DCA) was also conducted to assess the net benefit of adopting the orientation angle for predicting breast mass malignancy. The final analysis included 83 subjects with breast cancer and 135 subjects with benign masses. The intra-group correlation coefficient for the measurement of the orientation angle of breast masses was 0.986 (P = 0.001), indicating high reproducibility. The orientation angles of malignant and benign breast masses were 36.51 ± 14.90 (range: 10.7-88.6) degrees and 15.28 ± 8.40 (range: 0.0-58.7) degrees, respectively, and there was a significant difference between them (P < 0.001). The cutoff value for the orientation angle was determined to be 22.9°. The sensitivity, specificity, positive and negative predictive values, accuracy, odds ratio, and AUC for the prediction of breast malignancy using the orientation angle were 88.0%, 87.4%, 81.1%, 92.2%, 87.6%, 50.67%, and 0.925%, respectively. Multivariate analysis revealed that the orientation angle (> 22.9°), not circumscribed margin, and calcifications of the breast mass were independent factors predicting breast malignancy. The net benefit of adopting the orientation angle for predicting breast malignancy was 0.303. Based on these findings, it can be concluded that quantifying the orientation angle of breast masses is useful in predicting breast malignancy, as it demonstrates high sensitivity, specificity, AUC, and standardized net benefit. It optimizes the utility of the not parallel orientation in assessing breast mass malignancy.

Exploring the Relationship Between CD 166 Expression and Breast Imaging Reporting and Data System (BI-RADS) Scores in Breast Cancer Patients and Healthy Volunteers.

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.

Diagnosis of Benign and Malignant BI-RADS 4 Breast Masses by Contrastenhanced Ultrasound Combined with Shear Wave Elastography.

BACKGROUND: Breast cancer, one of the most prevalent malignant tumors in females, usually occurs in the breast epithelial tissues. OBJECTIVE: The study aimed to explore the diagnostic value of contrast-enhanced ultrasound (CEUS) combined with shear wave elastography (SWE) in the diagnosis of benign and malignant breast masses in BI-RADS (Breast Imaging Reporting and Data System) 4. METHODS: Examination outcomes and clinical information of 83 patients with BI-RADS 4 breast masses were analyzed retrospectively. These included patients who received CEUS, SWE, and pathological examinations. The difference of CEUS in determining the classification of BI-RADS 4 breast masses was evaluated using histopathological outcomes of breast masses as a reference standard. The diagnostic value of CEUS, SWE, and CEUS combined with SWE in the diagnosis of benign and malignant breast masses in BI-RADS 4 was also explored. RESULTS: Pathological biopsy results revealed 63 malignant masses and 20 benign masses among 83 BI-RADS 4 breast masses, with a 75.9% incidence of malignant masses. After the diagnosis of BI-RADS 4 breast masses with CEUS, SWE, and CEUS+SWE, the incidence of malignancy was 56.6%, 78.3%, and 73.5%, respectively. CEUS+SWE showed higher sensitivity (93.7% vs. 81% and 68.3%), specificity (90% vs. 30% and 80%), positive predictive value (96.7% vs. 78.5% and 91.5%), negative predictive value (81.8% vs. 33.3% and 44.4%), and diagnostic coincidence rate (92.8% vs. 68.7% and 71.1%) than SWE and CEUS alone in diagnosing pathological type of breast masses. Moreover, CEUS combined with SWE exhibited a larger area under the receiver operating characteristic (ROC) curve (0.918) than SWE (0.741, p = 0.028) and CEUS (0.555, p < 0.001) alone in the diagnosis of BI-RADS 4 breast masses. CONCLUSION: Overall, the diagnostic value of CEUS+SWE for the pathological type of BI-RADS is preferred over CEUS and SWE alone. CEUS+SWE showed higher values than CEUS and SWE alone in diagnosing BI-RADS 4 breast masses. Specifically, CEUS+SWE can correctly identify benign and malignant masses, reduce unnecessary trauma, and avoid misdiagnosis. In summary, CEUS combined with SWE can serve as an effective diagnostic method and avoid delaying the best treatment opportunity for some malignant lesions.

Quantitative evaluation of Kaiser score in diagnosing breast dynamic contrast-enhanced magnetic resonance imaging for patients with high-grade background parenchymal enhancement.

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.

Rosai-Dorfman disease of the breast: a clinicoradiologic and pathologic study.

Rosai-Dorfman disease (RDD) is an uncommon histiocytic disorder typically involving lymph nodes and less frequently extranodal tissues. RDD involving the breast is rare and may clinically and radiologically mimic neoplastic and non-neoplastic disorders. We report seven patients with breast RDD, describe their clinicoradiologic and pathologic features, and discuss the differential diagnosis. Patients, ranging from 15 to 74 years of age, presented with unilateral and unifocal (5/7) or bilateral and multifocal (2/7) masses. RDD was either confined to the breast (6/7) or concurrently involved a lymph node (1/7). Masses ranged from 8 to 31 mm, categorized as Breast Imaging-Reporting and Data System (BI-RADS) 4 (6/7) or 5 (1/7). All cases showed similar morphology with many large histiocytes displaying emperipolesis with associated fibrosis and dense lymphoplasmacytic infiltrate. The abnormal histiocytes co-expressed CD68/CD163, S100, OCT2, and Cyclin D1 (7/7), and were negative for CK AE1/AE3 (7/7), CD1a (7/7), and BRAF V600E (6/6). Flow cytometry (n = 3), kappa/lambda in situ hybridization (n = 5), and IgG4/IgG immunohistochemistry (n = 1) did not reveal lymphoma or IgG4-related disease. No mycobacterial or fungal organisms were identified on acid-fast bacillus (AFB) and Grocott methenamine silver (GMS) stains (n = 5). Three patients underwent complete excision and none recurred or progressed to systemic disease during follow-up (88-151 months). In summary, breast RDD should be included in the differential diagnosis of a mass-forming breast lesion. Histopathology with ancillary studies and clinicoradiologic correlation is essential for accurate diagnosis and optimal clinical management. Patients with RDD of the breast have an excellent prognosis after complete excision.

Vacuum-assisted biopsy system for breast lesions: a potential therapeutic approach.

Purpose: The primary objective is to optimize the population eligible for Mammotome Minimally Invasive Surgery (MIS) by refining selection criteria. This involves maximizing procedure benefits, minimizing malignancy risk, and reducing the rate of malignant outcomes. Patients and methods: A total of 1158 female patients who came to our hospital from November 2016 to August 2021 for the Mammotome MIS were analyzed retrospectively. Following χ2 tests to screen for risk variables, binary logistic regression analysis was used to determine the independent predictors of malignant lesions. In addition, the correlation between age and lesion diameter was investigated for BI-RADS ultrasound (US) category 4a lesions in order to better understand the relationship between these variables. Results: The malignancy rates of BI-RADS US category 3, category 4a and category 4b patients who underwent the Mammotome MIS were 0.6% (9/1562), 6.4% (37/578) and 8.3% (2/24) respectively. Malignant lesions were more common in patients over the age of 40, have visible blood supply, and BI-RADS category 4 of mammography. In BI-RADS US category 4a lesions, the diameter of malignant tumor was highly correlated with age, and this correlation was strengthened in patients over the age of 40 and with BI-RADS category 4 of mammography. Conclusion: The results of this study demonstrate that the clinical data and imaging results, particularly age, blood supply, and mammography classification, offer valuable insights to optimize patients' surgical options and decrease the incidence of malignant outcomes.

Gail model and fifth edition of ultrasound BI-RADS help predict axillary lymph node metastasis in breast cancer-A multicenter prospective study.

RATIONALE AND OBJECTIVES: We aim to assess the performance of the Gail model and the fifth edition of ultrasound BI-RADS (Breast Imaging Reporting and Data System) in breast cancer for predicting axillary lymph node metastasis (ALNM). MATERIALS AND METHODS: We prospectively studied 958 female patients with breast cancer between 2018 and 2019 from 35 hospitals in China. Based on B-mode, color Doppler, and elastography, radiologists classified the degree of suspicion based on the fifth edition of BI-RADS. Individual breast cancer risk was assessed with the Gail model. The association between the US BI-RADS category and the Gail model in terms of ALNM was analyzed. RESULTS: We found that US BI-RADS category was significantly and independently associated with ALNM (P < 0.001). The sensitivity, specificity, and accuracy of BI-RADS category 5 for predicting ALNM were 63.6%, 71.6%, and 68.6%, respectively. Combining the Gail model with the BI-RADS category showed a significantly higher sensitivity than using the BI-RADS category alone (67.8% vs. 63.6%, P < 0.001). The diagnostic accuracy of the BI-RADS category combined with the Gail model was better than that of the Gail model alone (area under the curve: 0.71 vs. 0.50, P < 0.001). CONCLUSION: Based on the conventional ultrasound and elastography, the fifth edition of ultrasound BI-RADS category could be used to predict the ALNM of breast cancer. ALNM was likely to occur in patients with BI-RADS category 5. The Gail model could improve the diagnostic sensitivity of the US BI-RADS category for predicting ALNM in breast cancer.

Analysis of the accuracy of ultrasound elastography and BI-RADS classification of breast masses located within the superficial fat layer of the glands.

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.

Breast ultrasound in Chinese hospitals: A cross-sectional study of the current status and influencing factors of BI-RADS utilization and diagnostic accuracy.

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).

Differential diagnosis of B-mode ultrasound Breast Imaging Reporting and Data System category 3-4a lesions in conjunction with shear-wave elastography using conservative and aggressive approaches.

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.

A simplified scoring protocol to improve diagnostic accuracy with the breast imaging reporting and data system in breast magnetic resonance imaging.

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.

A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography.

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.

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

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.

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

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

Gail Model Improves the Diagnostic Performance of the Fifth Edition of Ultrasound BI-RADS for Predicting Breast Cancer: A Multicenter Prospective Study.

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.

Breast MRI for Evaluating Residual Tumor Size Following Neoadjuvant Chemotherapy: Clinicopathologic Factors and MRI Imaging Features Affecting its Accuracy.

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.

Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study.

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.

Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study.

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.