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Background: This study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI). Methods: A total of 273 benign lesions (benign group) and 274 malignant lesions (malignant group) were collected and randomly divided into a training set (246 benign and 245 malignant lesions) and a testing set (28 benign and 28 malignant lesions) in a 9:1 ratio. An additional 53 lesions from 53 patients were designated as the validation set. Five models—VGG16, VGG19, DenseNet201, ResNet50, and MobileNetV2—were evaluated. Model performance was assessed using accuracy (Ac) in the training and testing sets, and precision (Pr), recall (Rc), F1 score (F1), and area under the receiver operating characteristic curve (AUC) in the validation set. Results: The accuracy of VGG19 on the test set (0.96) is higher than that of VGG16 (0.91), DenseNet201 (0.91), ResNet50 (0.67), and MobileNetV2 (0.88). For the validation set, VGG19 achieved higher performance metrics (Pr 0.75, Rc 0.76, F1 0.73, AUC 0.76) compared to the other models, specifically VGG16 (Pr 0.73, Rc 0.75, F1 0.70, AUC 0.73), DenseNet201 (Pr 0.71, Rc 0.74, F1 0.69, AUC 0.71), ResNet50 (Pr 0.65, Rc 0.68, F1 0.60, AUC 0.65), and MobileNetV2 (Pr 0.73, Rc 0.75, F1 0.71, AUC 0.73). S4 model achieved higher performance metrics (Pr 0.89, Rc 0.88, F1 0.87, AUC 0.89) compared to the other four fine-tuned models, specifically S1 (Pr 0.75, Rc 0.76, F1 0.74, AUC 0.75), S2 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77), S3 (Pr 0.76, Rc 0.76, F1 0.73, AUC 0.75), and S5 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77). Additionally, S4 model showed the lowest loss value in the testing set. Notably, the AUC of S4 for BI-RADS 3 was 0.90 and for BI-RADS 4 was 0.86, both significantly higher than the 0.65 AUC for BI-RADS 5. Conclusions: The S4 model we propose has demonstrated superior performance in predicting the likelihood of malignancy in DCE-BMRI, making it a promising candidate for clinical application in patients with breast diseases. However, further validation is essential, highlighting the need for additional data to confirm its efficacy. [ABSTRACT FROM AUTHOR]

Background: In clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unnecessary biopsies that residents need to perform for mammographic BI-RADS 4 lesions. Methods: A total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions collected and confirmed by clinical pathology or biopsy. The breast mammography images were randomly divided into three subsets, a training set, testing set, and validation set 1, at a ratio of 8:1:1. We constructed a DTL model for the classification of breast lesions based on Inception V3 and attempted to improve its performance with 11 fine-tuning strategies. The mammography images from 362 patients with pathologically confirmed BI-RADS 4 breast lesions were employed as validation set 2. Two images from each lesion were tested, and trials were categorized as correct if the judgement (≥ 1 image) was correct. We used precision (Pr), recall rate (Rc), F1 score (F1), and the area under the receiver operating characteristic curve (AUROC) as the performance metrics of the DTL model with validation set 2. Results: The S5 model achieved the best fit for the data. The Pr, Rc, F1 and AUROC of S5 were 0.90, 0.90, 0.90, and 0.86, respectively, for Category 4. The proportions of lesions downgraded by S5 were 90.73%, 84.76%, and 80.19% for categories 4 A, 4B, and 4 C, respectively. The overall proportion of BI-RADS 4 lesions downgraded by S5 was 85.91%. There was no significant difference between the classification results of the S5 model and pathological diagnosis (P = 0.110). Conclusion: The S5 model we proposed here can be used as an effective approach for reducing the number of unnecessary biopsies that residents need to conduct for mammographic BI-RADS 4 lesions and may have other important clinical uses. [ABSTRACT FROM AUTHOR]

Background: Multiparametric magnetic resonance imaging (MP-MRI) has high sensitivity for diagnosing breast cancers but cannot always be used as a routine diagnostic tool. The present study aimed to evaluate whether the diagnostic performance of perfluorobutane (PFB) contrast-enhanced ultrasound (CEUS) is similar to that of MP-MRI in breast cancer and whether combining the two methods would enhance diagnostic efficiency. Patients and methods: This was a head-to-head, prospective, multicenter study. Patients with breast lesions diagnosed by US as Breast Imaging Reporting and Data System (BI-RADS) categories 3, 4, and 5 underwent both PFB-CEUS and MP-MRI scans. On-site operators and three reviewers categorized the BI-RADS of all lesions on two images. Logistic-bootstrap 1000-sample analysis and cross-validation were used to construct PFB-CEUS, MP-MRI, and hybrid (PFB-CEUS + MP-MRI) models to distinguish breast lesions. Results: In total, 179 women with 186 breast lesions were evaluated from 17 centers in China. The area under the receiver operating characteristic curve (AUC) for the PFB-CEUS model to diagnose breast cancer (0.89; 95% confidence interval [CI] 0.74, 0.97) was similar to that of the MP-MRI model (0.89; 95% CI 0.73, 0.97) (P = 0.85). The AUC of the hybrid model (0.92, 95% CI 0.77, 0.98) did not show a statistical advantage over the PFB-CEUS and MP-MRI models (P = 0.29 and 0.40, respectively). However, 90.3% false-positive and 66.7% false-negative results of PFB-CEUS radiologists and 90.5% false-positive and 42.8% false-negative results of MP-MRI radiologists could be corrected by the hybrid model. Three dynamic nomograms of PFB-CEUS, MP-MRI and hybrid models to diagnose breast cancer are freely available online. Conclusions: PFB-CEUS can be used in the differential diagnosis of breast cancer with comparable performance to MP-MRI and with less time consumption. Using PFB-CEUS and MP-MRI as joint diagnostics could further strengthen the diagnostic ability. Trial registration Clinicaltrials.gov; NCT04657328. Registered 26 September 2020. IRB number 2020-300 was approved in Chinese PLA General Hospital. Every patient signed a written informed consent form in each center. [ABSTRACT FROM AUTHOR]

Background: To evaluate multiple parameters in multiple b-value diffusion-weighted imaging (DWI) in characterizing breast lesions and predicting prognostic factors and molecular subtypes. Methods: In total, 504 patients who underwent 3-T magnetic resonance imaging (MRI) with T1-weighted dynamic contrast-enhanced (DCE) sequences, T2-weighted sequences and multiple b-value (7 values, from 0 to 3000 s/mm2) DWI were recruited. The average values of 13 parameters in 6 models were calculated and recorded. The pathological diagnosis of breast lesions was based on the latest World Health Organization (WHO) classification. Results: Twelve parameters exhibited statistical significance in differentiating benign and malignant lesions. alpha demonstrated the highest sensitivity (89.5%), while sigma demonstrated the highest specificity (77.7%). The stretched-exponential model (SEM) demonstrated the highest sensitivity (90.8%), while the biexponential model demonstrated the highest specificity (80.8%). The highest AUC (0.882, 95% CI, 0.852–0.912) was achieved when all 13 parameters were combined. Prognostic factors were correlated with different parameters, but the correlation was relatively weak. Among the 6 parameters with significant differences among molecular subtypes of breast cancer, the Luminal A group and Luminal B (HER2 negative) group had relatively low values, and the HER2-enriched group and TNBC group had relatively high values. Conclusions: All 13 parameters, independent or combined, provide valuable information in distinguishing malignant from benign breast lesions. These new parameters have limited meaning for predicting prognostic factors and molecular subtypes of malignant breast tumors. [ABSTRACT FROM AUTHOR]

Background: This study aimed to explore whether collagen fiber features and collagen type I alpha 1 (COL1A1) are related to the stiffness of breast lesions and whether COL1A1 can predict axillary lymph node metastasis (LNM).Methods: Ninety-four patients with breast lesions were consecutively enrolled in the study. Amongst the 94 lesions, 30 were benign, and 64 were malignant (25 were accompanied by axillary lymph node metastasis). Ultrasound (US) and shear wave elastography (SWE) were performed for each breast lesion before surgery. Sirius red and immunohistochemical staining were used to examine the shape and arrangement of collagen fibers and COL1A1 expression in the included tissue samples. We analyzed the correlation between the staining results and SWE parameters and investigated the effectiveness of COL1A1 expression levels in predicting axillary LNM.Results: The optimal cut-off values for Emax, Emean, and Eratio for diagnosing the benign and malignant groups, were 58.70 kPa, 52.50 kPa, and 3.05, respectively. The optimal cutoff for predicting axillary LNM were 107.5 kPa, 85.15 kPa, and 3.90, respectively. Herein, the collagen fiber shape and arrangement features in breast lesions were classified into three categories. One-way analysis of variance (ANOVA) showed that Emax, Emean, and Eratio differed between categories 0, 1, and 2 (P < 0.05). Meanwhile, elasticity parameters were positively correlated with collagen categories and COL1A1 expression. The COL1A1 expression level > 0.145 was considered the cut-off value, and its efficacy in benign and malignant breast lesions was 0.808, with a sensitivity of 66% and a specificity of 90%. Furthermore, when the COL1A1 expression level > 0.150 was considered the cut-off, its efficacy in predicting axillary LNM was 0.796, with sensitivity and specificity of 96% and 59%, respectively.Conclusions: The collagen fiber features and expression levels of COL1A1 positively correlated with the elastic parameters of breast lesions. The expression of COL1A1 may help diagnose benign and malignant breast lesions and predict axillary LNM. [ABSTRACT FROM AUTHOR]

Background: Shear wave elastography can evaluate tissue stiffness. Previous studies showed that the elasticity characteristics of breast lesions were related to the components of extracellular matrix which was regulated by transforming growth factor beta 1(TGF-β1) directly or indirectly. However, the correlation of the expression level of TGF-β1, its signal molecules and elasticity characteristics of breast lesions have rarely been reported. The purpose of this study was to investigate the correlation between the expression level of TGF-β1, its signal molecules, and the elasticity characteristics of breast lesions.Methods: 135 breast lesions in 130 patients were included. Elasticity parameters, including elasticity modulus, the elasticity ratio, the "stiff rim sign", were recorded before biopsy and surgical excision. The expression levels of TGF-β1 and its signal molecules, including Smad2/3, Erk1/2, p38 mitogen-activated protein kinase (MAPK), c-Jun N-terminal kinase 2 (JNK2), phosphoinositide 3-kinase (PI3K), and protein kinase B (PKB/AKT) were detected by immunohistochemistry. The diagnostic performance of the expression level of those molecules and their correlation with the elasticity characteristics were analyzed.Results: Elasticity parameters and the expression levels of TGF- β1 and its signal molecules of benign lesions were lower than those of malignant lesions (P<0.0001). The expression levels of TGF- β1 and its signal molecules were correlated with elasticity parameters. The expression levels of TGF- β1 and its signal molecules in lesions with "stiff rim sign" were higher than those without "stiff rim sign" (P<0.05). And the expression levels of Smad2/3, Erk1/2, p38 MAPK, JNK2, PI3K and AKT were correlated with that of TGF- β1. The area under the curve for receiver operator characteristic curve of TGF-β1 and its signal molecules in the differentiation of malignant and benign breast lesions ranged from 0.920-0.960.Conclusions: The expression levels of TGF-β1, its signal molecules of breast lesions showed good diagnostic performance and were correlated with the elasticity parameters. The expression levels of signal molecules were correlated with that of TGF- β1, which speculated that TGF- β1 might play an important role in the regulation of breast lesion elasticity parameters and multiple signal molecule expressions. [ABSTRACT FROM AUTHOR]

Objectives: IL17 is a critical pro-inflammatory cytokine that is involved in inflammation, multidrug resistance and growth persistence pathways in cancer. This study is aiming at studying the expression of IL17 and hormonal receptors expression in benign and malignant breast lesions using immunohistochemical staining methods. Results: A total of 137 cases of breast lesions were studied, 97 (70.8%) were malignant and 40 (29.2%) were benign cases. Age range for malignant and benign cases were between 26 and 80 years [mean age 50 ± 2 years], and 20 to 70 years [mean age 41 ± 4 years], respectively, Odds ratio = 2.3 [1.78–1.99, 95% CI]. The majority of the histopathological diagnosis of the benign and malignant lesions were 21 (15.3%) fibro-adenomas and 87 (63.5%) invasive ductal carcinoma, respectively. Expression of IL17 and age were insignificantly negatively correlated for both groups; benign cases [r = − 0.054, P value 0.742] and malignant cases [r = − 0.080, P value 0.444]. IL17 expression was showing insignificant association with age group, P value 0.065. IL17 expression showed a statistical significance based on the different histopathological diagnosis, P value 0.035. Expression levels of estrogen, progesterone, and human epidermal receptors were showing insignificant difference among IL17 expression categories, P values 0.678, 0.623, and 0.361, respectively. [ABSTRACT FROM AUTHOR]

Background: Fibroadenoma is the most prevalent benign breast lesion that generally affects middle-aged women; it is rare in adolescents and younger children. The transformation into malignancy is not common. However, multiple recurrences of rapidly enlarging fibroadenomas suggest a high possibility of transforming into phyllodes tumors, which are uncommon fibroepithelial lesions that account for 0.3-0.5% of female breast tumors and typically present in premenopausal women.Case Presentation: We report a case of a 17-year-old Syrian girl who previously had three episodes of recurrence of multiple rapidly enlarging fibroadenomas in her left breast and underwent three operations for complete resection of the lesions. However, a few months later, she was readmitted with multiple large masses in the same breast, and pathological findings confirmed a surprising combination of multiple fibroadenomas for the fourth time with a malignant phyllodes tumor (cystosarcoma phyllodes). The patient underwent lumpectomies followed by adjuvant radiotherapy. Long-term follow-up was recommended.Conclusion: Our patient had an extraordinary number of episodes of recurrence at a young age and a rare combination of malignant and benign lesions in the same breast with multiple recurrences. We present her unique, very challenging case with the aim of highlighting the importance of clinical correlation, detailed diagnosis, and careful follow-up. [ABSTRACT FROM AUTHOR]

Background: Diffusion-weighted magnetic resonance imaging (DW-MRI) is different from conventional diagnostic methods and has the potential to delineate the microscopic anatomy of a target tissue or organ. The purpose of our study was to evaluate the value of DW-MRI in the diagnosis of benign and malignant breast masses, which would help the clinical surgeon to decide the scope and pattern of operation. Methods: A total of 52 female patients with palpable solid breast masses received breast MRI scans using routine sequences, dynamic contrast-enhanced imaging, and diffusion-weighted echo-planar imaging at b values of 400, 600, and 800 s/mm2, respectively. Two regions of interest (ROIs) were plotted, with a smaller ROI for the highest signal and a larger ROI for the overall lesion. Apparent diffusion coefficient (ADC) values were calculated at three different b values for all detectable lesions and from two different ROIs. The sensitivity, specificity, positive predictive value, and positive likelihood ratio of DW-MRI were determined for comparison with histological results. Results: A total of 49 (49/52, 94.2%) lesions were detected using DW-MRI, including 20 benign lesions (two lesions detected in the same patient) and 29 malignant lesions. Benign lesion had a higher mean ADC value than their malignant counterparts, regardless of b value. According to the receiver operating characteristic (ROC) curve, the smaller-range ROI was more effective in differentiation between benign and malignant lesions. The area under the ROC curve was the largest at a b value of 800 s/mm2. With a threshold ADC value at 1.23 × 10-3 mm2/s, DW-MRI achieved a sensitivity of 82.8%, specificity of 90.0%, positive predictive value of 92.3%, and positive likelihood ratio of 8.3 for differentiating benign and malignant lesions. Conclusions: DW-MRI is an accurate diagnostic tool for differentiation between benign and malignant breast lesions, with an optimal b value of 800 s/mm2. A smaller-range ROI focusing on the highest signal has a better differential value. [ABSTRACT FROM AUTHOR]

Background: Lesions of the breast that are classified BI-RADS®-US 3 by ultrasound are probably benign and observation is recommended, although malignancy may occasionally occur. In our study, we focus exclusively on BI-RADS®-US 3 lesions and hypothesize that sonoelastography as an adjunct to conventional ultrasound can identify a high-risk-group and a low-risk-group within these patients. Methods: A group of 177 breast lesions that were classified BI-RADS®-US 3 were additionally examined with real-time sonoelastography. Elastograms were evaluated according to the Tsukuba Elasticity Score. Pretest and posttest probability of disease (POD), sensitivity (SE), specificity (SP), positive (PPV) and negative predictive values (NPV) and likelihood-ratios (LR) were calculated. Furthermore, we analyzed the false-negative and false-positive cases and performed a model calculation to determine how elastography could affect the proceedings in population screening. Results: In our collection of BI-RADS®-US 3 cases there were 169 benign and eight malignant lesions. The pretest POD was 4.5% (95% confidence interval (CI): 2.1-9.0). In patients with a suspicious elastogram (high-risk group), the posttest POD was significantly higher (13.2%, p = 0.041) and the positive LR was 3.2 (95% CI: 1.7-5.9). With a benign elastogram (low-risk group), the posttest POD decreased to 2.2%. SE, SP, PPV and NPV for sonoelastography in BI-RADS®-US 3 lesions were 62.5% (95% CI: 25.9-89.8), 80.5% (95% CI: 73.5-86.0), 13.2% (95% CI: 5.0-28.9) and 97.8% (95% CI: 93.3-99.4), respectively. Conclusions: Sonoelastography yields additional diagnostic information in the evaluation of BI-RADS®-US 3 lesions of the breast. The examiner can identify a low-risk group that can be vigilantly observed and a high-risk group that should receive immediate biopsy due to an elevated breast cancer risk [ABSTRACT FROM AUTHOR]

Introduction: Vibro-acoustography (VA) is a recently developed imaging modality that is sensitive to the dynamic characteristics of tissue. It detects low-frequency harmonic vibrations in tissue that are induced by the radiation force of ultrasound. Here, we have investigated applications of VA for in vivo breast imaging. Methods: A recently developed combined mammography-VA system for in vivo breast imaging was tested on female volunteers, aged 25 years or older, with suspected breast lesions on their clinical examination. After mammography, a set of VA scans was acquired by the experimental device. In a masked assessment, VA images were evaluated independently by 3 reviewers who identified mass lesions and calcifications. The diagnostic accuracy of this imaging method was determined by comparing the reviewers' responses with clinical data. Results: We collected images from 57 participants: 7 were used for training and 48 for evaluation of diagnostic accuracy (images from 2 participants were excluded because of unexpected imaging artifacts). In total, 16 malignant and 32 benign lesions were examined. Specificity for diagnostic accuracy was 94% or higher for all 3 reviewers, but sensitivity varied (69% to 100%). All reviewers were able to detect 97% of masses, but sensitivity for detection of calcification was lower (. 72% for all reviewers). Conclusions: VA can be used to detect various breast abnormalities, including calcifications and benign and malignant masses, with relatively high specificity. VA technology may lead to a new clinical tool for breast imaging applications. [ABSTRACT FROM AUTHOR]