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The classification of the breast imaging-reporting and data system (BI-RADS) based on magnetic resonance imaging (MRI) refers to the classification of the degree of lesions according to the image signs of lesions, which is usually subjective. Moreover, the benign and malignant lesions of BI-RADS 3-5 are overlapping, which is prone to unnecessary invasive treatment due to high diagnostic categories in clinical diagnosis. To address these problems, this research applied radiomics for feature extraction and fusion of T1-weighted (T1W) and dynamic contrast-enhanced (DEC) MRI. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen out the optimal feature collection of each type of MR image. Support vector machine (SVM), random forest (RF), K-nearest neighbour (KNN) and logistic regression (LR) algorithms were applied for BI-RADS 3-5 classification, based on which the benign and malignant lesions were further classified. The results showed that the classification accuracy of breast BI-RADS 3-5 by four radiomics models based on feature fusion was 81.25%, 87.50%, 78.38%, and 81.25%, respectively. Their accuracy in distinguishing the benign and malignant breast lesions was 90.91%, 93.55%, 92.73%, and 94.55%, respectively. This indicates that the combination of radiomics and machine learning correlation algorithm has a good effect on breast MRI BI-RADS classification and benign and malignant differentiation, and feature fusion can further improve the accuracy of classification prediction.

To improve the accuracy of computer aided diagnosis (CAD) based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in the differentiation of benign and malignant breast lesions, this study proposed a convolutional neural network model (AC_Ulsam_CNN) that is based on multi-modal feature fusion and the combination of asymmetric convolution (AC) and ultra-lightweight subspace attention module (Ulsam). Firstly, the transfer learning method was used to pre-train the model to screen out the most effective DCE-MRI time phase scan for differentiating benign and malignant breast lesions. Then, a network model based on AC_Ulsam_CNN was constructed based on the optimal time phase scan images to enhance the feature expression ability and robustness of the classification model. Finally, multimodal information such as breast imaging reporting and data system (BI-RADS) classification, apparent diffusion coefficient (ADC) and time-signal intensity curve (TIC) type were incorporated for feature fusion, to further improve the distinguishing performance of benign and malignant breast lesions. The performance of the model was verified by 5-fold cross-validation method, and the accuracy (ACC) of the proposed method was 0.826 and the area under the curve (AUC) was 0.877. The experimental results show that the proposed algorithm performs well in the classification of benign and malignant breast lesions with small sample size, and the fusion model based on multimodal data further enriches the feature information, thus this study improves the detection accuracy of lesions, and provides a new method for automatic differential diagnosis of benign and malignant breast lesions.

Copyright of Imaging Science & Photochemistry is the property of Imaging Science & Photochemistry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective　To evaluate the diagnostic value of high-frequency ultrasound-guided guide wire localization of breast mass in patients with breast lesions. Methods　Ninety patients with suspected breast mass lesions in Yunnan Hospital of Traditional Chinese Medicine from May 2020 to August 2021 were selected as subjects by random sampling method. After admission，the patients received high-frequency ultrasound-guided guide wire localization diagnosis of breast mass， and the pathological tissue examination results were used as the "gold standard". The diagnostic value of high-frequency ultrasound-guided guide wire localization of breast mass in patients with breast lesions was calculated and analyzed. Results　All 90 patients with suspected breast mass lesions had complete pathological results. The results showed that 61 patients had benign lesions. 29 cases were malignant lesions. There was no significant difference in the detection rate of ultrasound-guided guide wire localization in fibroadenoma，breast adenosis，intraductal papilloma，invasive ductal carcinoma and invasive lobular carcinoma compared with pathological examination （ P > 0.05）. 57 cases of patients with benign breast lesions were diagnosed by ultrasonic-guided guide wire localization of benign breast masses，and the coincidence rate with gold standard diagnosis was 86.67%[（53+25） /90]，the difference was statistically significant （ P < 0.05）. The diagnostic sensitivity and specificity were 86.89% （53/61） and 86.21% （25/29）. Ultrasound guided guide wire localization of benign breast mass was confirmed in 27 cases of breast malignant lesions，and the coincidence rate with gold standard diagnosis was 88.89%[（57+23） /90] （ P < 0.05）. The diagnostic sensitivity and specificity were 79.31% （23/29） and 93.44% （57/61） respectively. Conclusion　High frequency ultrasound-guided guide wire localization of breast mass for patients with breast lesions can obtain a high diagnostic rate， high diagnostic sensitivity and specificity，which can guide clinical diagnosis and treatment，and is worthy of promotion and application. [ABSTRACT FROM AUTHOR]

Objective: This study is focused on ultrasound multimodality examination, which refers to the combined use of three ultrasound examination modalities, ultrasound (US), acoustic radiation force impulse (ARFI) imaging, and contrast-enhanced ultrasound (CEUS). The purpose of this study is to analyze the value of applying ultrasound multimodality examination in the differential diagnosis of benign and malignant breast non-mass-like lesions (NMLs)., Methods: Cases of breast NMLs were analyzed retrospectively, and the nature of all the lesions was verified by pathological examination. Based on the gray-scale ultrasound image characteristics, the cases were classified into types Ⅰ to Ⅴ, and type Ⅰ and type Ⅱ were further classified into 4 subtypes, Ⅰa, Ⅰb, Ⅱa, and Ⅱb, according to whether there was also calcification, and the proportion of malignant cases in each subtype was statistically analyzed. Logistic regression models of US, US+ARFI, US+CEUS, and US+ARFI+CEUS for the diagnosis of malignant cases were established, ROC curves were drawn, the area under the curve (AUC) was calculated, and comparisons were made accordingly. The detection rate of malignant NMLs without calcification (atypical malignant NMLs) by the combination examination of US, ARFI, and CEUS was analyzed., Results: A total of 407 cases were included in the study. All subjects were female, aged 22 to 81 years, with the average age being (47.0±11.0) years. There were 220 benign cases and 187 malignant cases. Ranked from the highest to the lowest, the malignancy proportion of the different types was Ⅰb>Ⅱb>Ⅲ>Ⅴ>Ⅰa>Ⅱa>Ⅳ. The malignant proportion of the low echo area with calcification was significantly higher than that of the lesions without calcification. The AUC (95% confidence interval [CI]) for diagnosing malignant cases with the logistic regression models of US, US+ARFI, US+CEUS, and US+ARFI+CEUS were 0.895 (0.862-0.927), 0.908 (0.878-0.937), 0.921 (0.893-0.948), and 0.927 (0.902-0.952), respectively. Comparison of the AUC of the 4 regression models showed significant differences ( P <0.001). The detection rate of US for NMLs without calcification was 80.7%. When US was used in combination with ARFI and CEUS, 86.4% of the malignant NMLs lesions without calcification could be detected if the lesion CEUS score was 4 or 5 points or if shear-wave velocity (SWV)≥4.28 m/s., Conclusion: Breast NMLs with calcification show high risks of malignancy, and a pathological examination is always recommended for a conclusive diagnosis. Ultrasound multimodality examination can improve the diagnostic accuracy of breast NML without calcification., Competing Interests: 利益冲突　所有作者均声明不存在利益冲突, (© 2024《四川大学学报（医学版）》编辑部 版权所有Copyright ©2024 Editorial Office of Journal of Sichuan University (Medical Sciences).)

Objective: To investigate the diagnostic value of digital breast tomosynthesis (DBT) combined with breast ultrasonography (BUS) in dense breast lesions. Methods: The imaging data of 149 patients with dense breast lesions with complete pathological findings who were admitted to our hospital from June 2018 to April 2019 were retrospectively analyzed, and the detection rates of DBT and BUS of two inspection methods were compared. The pathological results were taken as the gold standard, the diagnostic efficiency of DBT, BUS and DBT combined BUS were analyzed. Results: The detection rate of BUS in benign lesions (97.87%) was higher than DBT (89.36%), the difference was statistically significant (x²=5.697, P＜0.05). The detection rates of DBT and BUS for malignant lesions were 98.44% and 95.31%, respectively, and the differences were not statistically significant (x²=1.032, P>0.05). The sensitivity of DBT in diagnosis of dense breast lesions was 90.61%, the specificity was 93.55%, the accuracy rate was 91.77%. The sensitivity of BUS in diagnosis of dense breast lesions was 78.13%, the specificity was 89.36%, the accuracy rate was 84.81%. The sensitivity of DBT combined with BUS in diagnosis of dense breast lesions was 95.31%, the specificity was 95.74%, the accuracy rate was 95.57%. The area under the receiver operating characteristic curve (ROC) of DBT combined with BUS for diagnosis of dense breast lesions was 0.977, which was slightly higher than that of DBT (0.951), and significantly higher than that of BUS (0.885). Conclusion: The detection rate of BUS for benign lesions is significantly higher than that of DBT, and the diagnosis efficiency of DBT for dense breast lesions is higher than that of BUS, the combined application of the two methods can improve the detection rate and diagnostic efficiency of lesions. [ABSTRACT FROM AUTHOR]

Objective: To observe the typical performance of shear wave elastography (SWE) in benign and malignant breast lesions, and to explore the value of SWE in differential diagnosis of benign and malignant breast lesions. Method: 162 patients with breast masses who underwent SWE examination in our hospital from June 2017 to June 2019 were selected. 105 cases of benign masses (benign group) and 57 cases of malignant masses (malignant group) were confirmed by biopsy or pathology. The differences of SWE typical performance, SWE parameters [Emax, Emin, Emean, standard deviation (SD), SWE-Ratio] between benign and malignant breast lesions were compared, and the value of SWE in differentiating benign and malignant breast lesions was analyzed. Result: The detection rate of "stiff rim sign", elastic image of type III-type V, Emax, Emean, SD and SWE-Ratio of breast masses in malignant group were higher than those in benign group (P<0.05), and Emin was lower than that in benign group (P<0.05). Logistic regression analysis showed that "stiff rim sign", Emax, Emean and SWE-Ratio were independently correlated with the pathological diagnosis of breast mass (P<0.05). The results of receiver operator characteristics curve (ROC) curve analysis show that the area under curve (AUC) of "stiff rim sign", Emax, Emean and SWE-Ratio in diagnosing benign and malignant breast lesions were 0.923, 0.686, 0.873 and 0.879, respectively. Conclusion: SWE is an effective imaging method in the diagnosis of benign and malignant breast lesions. SWE "stiff rim sign", SWE-Ratio and Emean have high value in differentiating benign and malignant breast lesions. [ABSTRACT FROM AUTHOR]

Copyright of Imaging Science & Photochemistry is the property of Imaging Science & Photochemistry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Objective To compare contrast -enhanced ultrasound (CEUS) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions. Methods 86 lesions were underwent by ultrasonic CEUS and DCE-MRI. Using the pathologic findings as the gold standard, the diagnostic sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 86 lesions on CEUS and DCE-MRI were calculated. Results In 65 malignant and 21 benign lesions, the 89.23% sensitivity, 80.95% specificity, 93.55% PPV and 70.83% NPV of CEUS were not significantly different from that of DCE-MRI (92.31%, 85.71%, 95,24%, 78.26%). Conclusion CEUS is valuable in the differential diagnosis of BI-RADS 4 breast lesions and has a good agreement with DCE-MRI. [ABSTRACT FROM AUTHOR]

Objective: To investigate the value of quantitative synthetic magnetic resonance imaging (SyMRI) in distinguishing between benign and malignant breast lesions., Methods: We retrospectively collected data of preoperative conventional MRI and multi-dynamic multi-echo sequences from 95 patients with breast lesions showing mass-type enhancement on DCE-MRI, including 27 patients with benign lesions and 68 with malignant lesions. The MRI features of the lesions (shape, margin, internal enhancement pattern, time-signal intensity curve, and T2WI signal) were analyzed, and for each lesion, SyMRI-generated quantitative parameters including T1 and T2 relaxation time and proton density (PD) were measured before and after enhancement and recorded as T1p, T2p, PDp and T1e, T2e, and PDe, respectively. The relative change rate of each parameter was calculated. Logistic regression and all-subset regression analyses were performed for variable selection to construct diagnostic models of the breast lesions, and receiver-operating characteristic (ROC) analysis was used to assess the performance of each model for differentiation of benign and malignant lesions., Results: There were significant differences in the MRI features between benign and malignant lesions ( P < 0.05). All the SyMRI-generated quantitative parameters, with the exception of T2e and Pdp, showed significant differences between benign and malignant lesions ( P < 0.05). Among the constructed diagnostic models, the model based on all the DCE-MRI features combined with SyMRI parameters T2p and T1e (DCE-MRI+T2p+T1e) showed the best performance in the differential diagnosis malignant breast masses with an AUC of 0.995 (95% CI : 0.983-1.000)., Conclusion: Quantitative SyMRI can be used for differential diagnosis of benign and malignant breast lesions.