Benign Breast Masses Research Articles

Pictorial Review of Common and Uncommon Pediatric Breast Lesions.

Breast masses in children and adolescents are uncommon, and the spectrum of pediatric breast masses is predominantly benign and different from that in adults. Knowledge of the clinical presentation and imaging features of the various stages of normal development and mass-forming lesions in the pediatric breast can guide a tailored imaging approach and help the radiologist make a definitive diagnosis. Breast development begins during fetal gestation along the embryologic milk lines and continues through puberty as the breast matures through the Tanner stages of development. Normal and developmental variants and benign neoplastic and nonneoplastic lesions in the pediatric breast are common causes of concern. Malignant breast masses in children are rare and are more often due to metastasis than primary breast cancer. When clinically warranted, US is the mainstay for imaging the pediatric breast and requires careful correlation of sonographic findings with patient age and history. Breast MRI can be used to further characterize lesions and evaluate the extent of disease. Biopsy should be considered only for suspicious findings and must be weighed against the risk of iatrogenic injury to the developing breast. Given that the majority of mass-forming lesions in the pediatric breast are benign, the diagnostic and management approach should emphasize "first do no harm." Knowledge of the imaging appearance of normal breast development and the spectrum of benign and malignant pediatric breast masses is necessary to make the correct diagnosis. © RSNA, 2022.

Diagnostics Role of Haematological Parameters in Benign and Malignant Breast Lesions: A Retrospective Observational Study from a Tertiary Healthcare Centre in Tamil Nadu, India

Introduction: Breast cancer is the most common malignancy diagnosed in women, and despite advancements in diagnosis and treatment, it still leads to significant morbidity and mortality. Distinguishing between benign and malignant breast disease is clinically challenging, and there is currently no serum biomarker available for early breast cancer detection. As part of the preoperative work-up for breast lesions, a Complete Blood Count (CBC) analysis is typically performed. Aim: This study aimed to evaluate the utility of CBC parameters in diagnosing malignant breast lesions and to analyse the diagnostic role of haematological parameters within benign and malignant breast lesions, as well as different histological breast cancer stages. Materials and Methods: This retrospective observational study was conducted for one year, from January to December 2021, at the Department of Pathology of PSG Institute of Medical Sciences and Research in Coimbatore, Tamil Nadu, India. The study included 60 female patients with both benign and malignant breast lesions. Histopathological examination confirmed the diagnoses of these lesions. CBC parameters, including haemoglobin, Total White Cell Count (TWBC), neutrophil count, lymphocyte count, monocyte count, platelet count, and Mean Platelet Volume (MPV), were collected from a total of 120 cases. Additionally, the Neutrophil-Lymphocyte Ratio (NLR), MonocyteLymphocyte Ratio (MLR), and Platelet-Lymphocyte Ratio (PLR) were calculated from the obtained CBC parameters for all cases. The values were expressed as mean and standard deviation, and an independent t-test was used to compare the two groups. A p-value less than 0.05 was considered significant. Results: Malignant breast lesions showed a significant increase in neutrophils (64.1±8.4%) and a significant decrease in lymphocytes (25.9±8%) and MPV (7.6±0.76 pg) compared to benign breast cases. The calculated ratios, such as NLR (2.9±1.7), MLR (0.32±0.2), and PLR (14.4±8), were also found to be increased in malignant cases. However, there were no significant variations in CBC parameters across the various stages of breast malignancy. Conclusion: Among the CBC parameters evaluated, neutrophil count, lymphocyte count, MPV, NLR, MLR, and PLR were significantly altered in breast malignancy compared to benign breast masses. Measuring CBC parameters and their derived ratios are fast, simple, inexpensive, and readily available method that can assist physicians in predicting breast malignancy.

Efficient System for Delimitation of Benign and Malignant Breast Masses.

In this study, a high-performing scheme is introduced to delimit benign and malignant masses in breast ultrasound images. The proposal is built upon by the Nonlocal Means filter for image quality improvement, an Intuitionistic Fuzzy C-Means local clustering algorithm for superpixel generation with high adherence to the edges, and the DBSCAN algorithm for the global clustering of those superpixels in order to delimit masses' regions. The empirical study was performed using two datasets, both with benign and malignant breast tumors. The quantitative results with respect to the BUSI dataset were JSC≥0.907, DM≥0.913, HD≥7.025, and MCR≤6.431 for benign masses and JSC≥0.897, DM≥0.900, HD≥8.666, and MCR≤8.016 for malignant ones, while the MID dataset resulted in JSC≥0.890, DM≥0.905, HD≥8.370, and MCR≤7.241 along with JSC≥0.881, DM≥0.898, HD≥8.865, and MCR≤7.808 for benign and malignant masses, respectively. These numerical results revealed that our proposal outperformed all the evaluated comparative state-of-the-art methods in mass delimitation. This is confirmed by the visual results since the segmented regions had a better edge delimitation.

Volumetric imaging and morphometric analysis of breast tumor angiogenesis using a new contrast-free ultrasound technique: a feasibility study

BackgroundThere is a strong correlation between the morphological features of new tumor vessels and malignancy. However, angiogenic heterogeneity necessitates 3D microvascular data of tumor microvessels for more reliable quantification. To provide more accurate information regarding vessel morphological features and improve breast lesion characterization, we introduced a quantitative 3D high-definition microvasculature imaging (q3D-HDMI) as a new easily applicable and robust tool to morphologically characterize microvasculature networks in breast tumors using a contrast-free ultrasound-based imaging approach.MethodsIn this prospective study, from January 2020 through December 2021, a newly developed q3D-HDMI technique was evaluated on participants with ultrasound-identified suspicious breast lesions recommended for core needle biopsy. The morphological features of breast tumor microvessels were extracted from the q3D-HDMI. Leave-one-out cross-validation (LOOCV) was applied to test the combined diagnostic performance of multiple morphological parameters of breast tumor microvessels. Receiver operating characteristic (ROC) curves were used to evaluate the prediction performance of the generated pooled model.ResultsNinety-three participants (mean age 52 ± 17 years, 91 women) with 93 breast lesions were studied. The area under the ROC curve (AUC) generated with q3D-HDMI was 95.8% (95% CI 0.901–1.000), yielding a sensitivity of 91.7% and a specificity of 98.2%, that was significantly higher than the AUC generated with the q2D-HDMI (p = 0.02). When compared to q2D-HDMI, the tumor microvessel morphological parameters obtained from q3D-HDMI provides distinctive information that increases accuracy in differentiating breast tumors.ConclusionsThe proposed quantitative volumetric imaging technique augments conventional breast ultrasound evaluation by increasing specificity in differentiating malignant from benign breast masses.

Biomedical Indicators of Patients with Non-Puerperal Mastitis: A Retrospective Study.

Non-puerperal mastitis (NPM), a recurrent chronic inflammation of non-lactating breast, often proves tremendous difficulty in treatment, and it may give rise to its complicated symptoms and unclear etiology. Furthermore, the clinical morbidity rate of NPM has been increasing in recent years. Overall, 284 patients diagnosed with NPM were consecutively recruited as cases in this study, and patients with benign breast disease (n = 1128) were enrolled as control. The clinical, biomedical, and pathological indicators were analyzed. Univariate and multivariate logistic analysis were used to distinguish risks between NPM and benign breast mass patients. Furthermore, according to the pathological characteristics, the patients of NPM were classified into two subgroups: mammary duct ectasia (MDE) and granulomatous lobular mastitis (GLM). The differences of biomedical indicators between MDE and GLM groups were also analyzed. Compared with benign breast mass group, the level of high-density lipoprotein (HDL-C) significantly decreased, while lipoprotein(a) (Lp(a)) and blood glucose (GLU) both increased in NPM group. According to univariate and multivariate logistic analysis, the onset age and HDL-C were generally decreased, while Lp(a) and GLU were increased in NPM group. The onset age, HDL-C, Lp(a), and GLU were modeled to distinguish NPM and benign breast mass. Significant differences were also observed between MDE and GLM patients in biomedical indicators, such as lipoprotein(a) (Lp(a)), lactate dehydrogenase (LDH), creatine kinase (CK), total cholesterol (TC), and so on. Our results indicated for the first time that biomarkers were associated with NPM. The biomedical indicators involved in lipid metabolism might be important factors in the development and treatment of NPM. In addition, MDE and GLM are two diseases with different inflammatory states of NPM. These findings would be helpful for a better understanding of NPM and give us some insights to develop new diagnostic and therapeutic strategies.

An AI model of sonographer's evaluation+ S-Detect + elastography + clinical information improves the preoperative identification of benign and malignant breast masses.

The purpose of the study was to build an AI model with selected preoperative clinical features to further improve the accuracy of the assessment of benign and malignant breast nodules. Patients who underwent ultrasound, strain elastography, and S-Detect before ultrasound-guided biopsy or surgical excision were enrolled. The diagnosis model was built using a logistic regression model. The diagnostic performances of different models were evaluated and compared. A total of 179 lesions (101 benign and 78 malignant) were included. The whole dataset consisted of a training set (145 patients) and an independent test set (34 patients). The AI models constructed based on clinical features, ultrasound features, and strain elastography to predict and classify benign and malignant breast nodules had ROC AUCs of 0.87, 0.81, and 0.79 in the test set. The AUCs of the sonographer and S-Detect were 0.75 and 0.82, respectively, in the test set. The AUC of the combined AI model with the best performance was 0.89 in the test set. The combined AI model showed a better specificity of 0.92 than the other models. The sonographer's assessment showed better sensitivity (0.97 in the test set). The combined AI model could improve the preoperative identification of benign and malignant breast masses and may reduce unnecessary breast biopsies.

Ultrasound super-resolution imaging for differential diagnosis of breast masses.

Ultrasound imaging has been widely used in breast cancer screening. Recently, ultrasound super-resolution imaging (SRI) has shown the capability to break the diffraction limit to display microvasculature. However, the application of SRI on differential diagnosis of breast masses remains unknown. Therefore, this study aims to evaluate the feasibility and clinical value of SRI for visualizing microvasculature and differential diagnosis of breast masses. B mode, color-Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS) images of 46 patients were collected respectively. SRI were generated by localizations of each possible contrast signals. Micro-vessel density (MVD) and microvascular flow rate (MFR) were calculated from SRI and time to peak (TTP), peak intensity (PI) and area under the curve (AUC) were obtained by quantitative analysis of CEUS images respectively. Pathological results were considered as the gold standard. Independent chi-square test and multivariate logistic regression analysis were performed using these parameters to examine the correlation. The results showed that SRI technique could be successfully applied on breast masses and display microvasculature at a significantly higher resolution than the conventional CDFI and CEUS images. The results showed that the PI, AUC, MVD and MFR of malignant breast masses were significantly higher than those of benign breast masses, while TTP was significantly lower than that of benign breast masses. Among all five parameters, MVD showed the highest positive correlation with the malignancy of breast masses. SRI is able to successfully display the microvasculature of breast masses. Compared with CDFI and CEUS, SRI can provide additional morphological and functional information for breast masses. MVD has a great potential in assisting the differential diagnosis of breast masses as an important imaging marker.

Evaluation of radiomic features as imaging markers to differentiate benign and malignant breast masses on MRI

Category: Breast imaging

Prospective analysis of breast masses using the combined score for quantitative ultrasonography parameters

To investigate the diagnostic value of combined SWE, SMI, and B-mode US scores for distinguishing between benign and malignant masses. A total of 450 breast masses that underwent US-guided core needle biopsies were prospectively enrolled. The breast masses were assessed based on the BI-RADS and quantitative SWE and SMI parameters. The SWEmax, SWEratio, and SMIVI cutoff value were determined using Youden’s index by comparison to the pathological results. The BI-RADS categories were scored on a scale from 1 to 5, and SWEmax, SWEratio, and SMIVI were dichotomized based on each cutoff values (0 or 1). The combined scores (1 to 8) were calculated as the sum of the BI-RADS score and the quantitative scores and compared to the pathologic results using AUROC analysis. The cutoff values were 52.25 kPa for SWEmax, 5.03 for SWEratio, and 2.15% for SMIVI. In AUROC, the combined scores showed significantly better diagnostic performance compared to BI-RADS alone (p < 0.001). The combined score showed significantly increased than BI-RADS alone in specificity (p < 0.001) and accuracy (p < 0.001), but a sensitivity decreased without significance (p = 0.082). When a combined score cutoff value of 4 was used, the false negative rate was 2.7%. Using the combined score, 76.4% of the C4a lesions were considered benign also pathologically diagnosed as benign. The combined scores showed improved diagnostic performance in differentiating between benign and malignant breast masses, which could be helpful for determining a breast biopsy eligibility.

Value of MRI in Differentiation between Benign and Malignant Breast Mass

Objectives: This study aimed to evaluate the role of MRI in differentiation between benign and malignant well-circumscribed breast masses.&#x0D; Methods: A prospective cross-sectional study conducted on selected female patients with Breast mass who their age older than 35 years. All patients underwent examination using 1.5 Tesla magnetic resonance unit (MAGNETOM AERA, SIMENS).&#x0D; Results: 30 female patients were selected (mean age of 44.2±9.2 years, range between 35-69). Final findings of MRI revealed that 6 (20%) women had malignant breast mass while 24 (80%) women had benign breast mass. The histopathology revealed that 4 (13.3%) women were having malignancy while 26 (86.6%) were having benign breast tumor. There was a highly significant association between malignancy and hyperdense mammography findings of women (p&lt;0.001), multiple lymph nodes for women (p&lt;0.001), increased size of mass by MRI (p&lt;0.001), circumscribed macrolabular lesion on MRI, rapid early kinetic phase of enhanced MRI (p&lt;0.001), washout late kinetic phase enhanced MRI (p&lt;0.001), and MRI kinetic curve type 3(p&lt;0.001). There was a highly significant association between homogenous enhanced MRI findings of women and benign lesions (p&lt;0.001).&#x0D; Conclusion: The validity results of MRI in differentiation between benign and malignant circumscribed breast masses are high, except for positive predictive value, and application of kinetic curve dynamic contrast enhancement increases magnetic resonance in differentiation between benign and malignant circumscribed breast masses.

Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals

Objective. Prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer is important for patient outcomes. In this work, we propose a deep learning based approach to NAC response prediction in ultrasound (US) imaging. Approach. We develop recurrent neural networks that can process serial US imaging data to predict chemotherapy outcomes. We present models that can process either raw radio-frequency (RF) US data or regular US images. The proposed approach is evaluated based on 204 sequences of US data from 51 breast cancers. Each sequence included US data collected before the chemotherapy and after each subsequent dose, up to the 4th course. We investigate three pre-trained convolutional neural networks (CNNs) as back-bone feature extractors for the recurrent network. The CNNs were pre-trained using raw US RF data, US b-mode images and RGB images from the ImageNet dataset. The first two networks were developed using US data collected from malignant and benign breast masses. Main results. For the pre-treatment data, the better performing network, with back-bone CNN pre-trained on US images, achieved area under the receiver operating curve (AUC) of 0.81 (±0.04). Performance of the recurrent networks improved with each course of the chemotherapy. For the 4th course, the better performing model, based on the CNN pre-trained with RGB images, achieved AUC value of 0.93 (±0.03). Statistical analysis based on the DeLong test presented that there were no significant differences in AUC values between the pre-trained networks at each stage of the chemotherapy (p-values > 0.05). Significance. Our study demonstrates the feasibility of using recurrent neural networks for the NAC response prediction in breast cancer US.

Fibroepithelial lesions of the breast: improving the accuracy of imaging diagnosis and reducing unnecessary biopsy.

Fibroadenomas and phyllodes tumours are fibroepithelial lesions of the breast. Fibroadenomas are common benign breast masses encountered both symptomatically and incidentally. Phyllodes tumours are uncommon and usually present symptomatically. Management of fibroadenomas focuses on reducing biopsies without missing cancers while radiological management of phyllodes tumours should focus on predicting the need for surgical excision with or without margins.

Application of S-detect combined with virtual touch imaging quantification in ultrasound for diagnosis of breast mass.

Ultrasound is a safe and timely diagnosis method commonly used for breast lesion, however it depends on the operator to a certain degree. As an emerging technology, artificial intelligence can be combined with ultrasound in depth to improve the intelligence and precision of ultrasound diagnosis and avoid diagnostic errors caused by subjectivity of radiologists. This paper aims to investigate the value of artificial intelligence S-detect system combined with virtual touch imaging quantification (VTIQ) technique in the differential diagnosis of benign and malignant breast masses by conventional ultrasound (CUS). respectively, and AUCs for them were 0.74, 0.86, 0.79, and 0.94, respectively. The diagnostic accuracy of CUS+S-detect was higher than that of CUS (P<0.05). The diagnostic accuracy of CUS+S-detect was higher than that of CUS (P<0.05). The diagnostic specificity of CUS+VTIQ was higher than that of CUS (P<0.05). The diagnostic accuracy and AUC of CUS+S-detect+VTIQ were higher than those of S-detect or VTIQ applied to CUS alone (P<0.05). The sensitivities of CUS for senior radiologists, CUS for junior radiologists, CUS+S-detect+VTIQ for senior radiologists, and CUS+S-detect+VTIQ for junior radiologists were 60.00%, 80.00%, 72.73%, and 90.00%, respectively, when diagnosing benign and malignant breast masses in 50 randomly selected cases. The specificities for them was 66.67%, 76.67%, 80.00%, and 81.25%, respectively. The accuracies for them was 64.00%, 78.00%, 80.00%, and 88.00%, respectively. The AUCs for them were 0.63, 0.78, 0.88, and 0.80, respectively. Compared with the CUS for junior radiologists, the CUS+S-detect+VTIQ for junior radiologists had higher sensitivity, specificity, and accuracy (all P<0.05). The consistency of the combined application of S-detect and VTIQ for diagnosing breast masses at 2 different times was good among junior radiologists (Kappa=0.800). CUS, S-detects, and VTIQ were used to differentially diagnose benign and malignant breast masses in 108 cases, and the final pathological results were referred to as the gold standard for classifying breast masses. The diagnostic efficacy were evaluated and compared, among the 3 methods and among S-detect applied to CUS (CUS+S-detect), VTIQ applied to CUS (CUS+VTIQ), and S-detect combined with VTIQ applied to CUS (CUS+S-detect+VTIQ). Fifty cases were acquired randomly from the collected breast masses, and 2 radiologists with different years of experience (2 and 8 years) used S-detect combined with VTIQ for the ultrasonic differential diagnosis of benign and malignant breast masses. The differences in sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) of the 3 diagnostic methods of CUS, S-detect, and VTIQ were not statistically significant (all P>0.05). The sensitivities of CUS, CUS+Sdetect, CUS+VTIQ, and CUS+S-detect+VTIQ were 78.57%, 92.86%, 69.05%, and 95.24%, respectively, the specificities for them were 69.70%, 78.79%, 87.88%, and 92.42%, respectively, the accuracies for them were 73.15%, 84.26%, 80.56%, and 93.52%. S-detect combined with VTIQ when applied to CUS can overcome the shortcomings of separate applications and complement each other, especially for junior radiologists, and can more effectively improve the diagnostic efficacy of ultrasound for benign and malignant breast masses.

Diagnostic accuracy of S-Detect to breast cancer on ultrasonography: A meta-analysis (PRISMA).

Background:Computer-aided diagnosis (CAD) systems have shown great potential as an effective auxiliary diagnostic tool in breast imaging. Previous studies have shown that S-Detect technology has a high accuracy in the differential diagnosis of breast masses. However, the application of S-Detect in clinical practice remains controversial, and the results vary among different clinical trials. This meta-analysis aimed to determine the diagnostic accuracy of S-Detect for distinguishing between benign and malignant breast masses.Methods:We searched PubMed, Cochrane Library, and CBM databases from inception to April 1, 2021. Meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 softwares. We calculated the summary statistics for sensitivity (Sen), specificity (Spe), positive, and negative likelihood ratio (LR+/LR−), diagnostic odds ratio(DOR), and summary receiver operating characteristic (SROC) curves. Cochran Q-statistic and I2 test were used to evaluate the potential heterogeneity between studies. Sensitivity analysis was performed to evaluate the influence of single studies on the overall estimate. We also performed meta-regression analyses to investigate potential sources of heterogeneity.Results:Eleven studies that met all the inclusion criteria were included in the meta-analysis. A total of 951 malignant and 1866 benign breast masses were assessed. All breast masses were histologically confirmed using S-Detect. The pooled Sen was 0.82 (95% confidence interval(CI) = 0.74–0.88); the pooled Spe was 0.83 (95%CI = 0.78–0.88). The pooled LR + was 4.91 (95%CI = 3.75–6.41); the pooled negative LR − was 0.21 (95%CI = 0.15–0.31). The pooled DOR of S-Detect in the diagnosis of breast nodules was 23.12 (95% CI = 14.53–36.77). The area under the SROC curve was 0.90 (SE = 0.0166). No evidence of publication bias was found (t = 0.54, P = .61).Conclusions:Our meta-analysis indicates that S-Detect may have high diagnostic accuracy in distinguishing benign and malignant breast masses.

Value of Artificial Neural Network Ultrasound in Improving Breast Cancer Diagnosis.

Ultrasound-guided needle biopsy based on artificial neural network, as a safe, effective, and simple preoperative pathological diagnosis technique, has been widely used in clinical practice. Ultrasound-guided needle biopsy based on artificial neural networks for suspicious breast lesions found in conventional ultrasound examinations is an effective method for preoperative diagnosis. The purpose of this article is to study the value of artificial neural network ultrasound in improving breast cancer diagnosis. This article summarizes the neuron model of PCNN by observing and studying its impulse synchronization phenomenon. Aiming at gray-scale images disturbed by mixed noise (impulse noise and the Gaussian noise), a comprehensive filtering algorithm based on the simplified PCNN model is proposed. In this paper, the benign and malignant breast masses were evaluated based on the two-dimensional and three-dimensional ultrasound imaging signs of the mass, and compared with the postoperative pathological results, a logistic regression model was established to analyze the shape, boundary, microcalcification, and posterior echo attenuation of the mass, values for keratinization or burrs, convergent signs, and blood flow classification in the differential diagnosis of benign and malignant. In this paper, a color ultrasound diagnostic device is used, Sonobi is used as a contrast medium, and the injection volume is 2.4 ml/dose. During the imaging process, the sound image performance of the lesion is dynamically observed, the original dynamic data are stored throughout the whole process, and the playback analysis is performed after the imaging is completed. Studies have shown that CDUS elastography (UE) combined with MRI can increase the sensitivity of breast cancer diagnosis, with a diagnostic accuracy rate of 92.4%.

Type 2 dynamic curves: A diagnostic dilemma.

BACKGROUNDMagnetic resonance imaging (MRI) with multiparametric dynamic contrast plays a critical role in the assessment of breast lesions. Dynamic curves are a critical parameter in determining the benign or malignant nature of lesions. Dynamic curves of type 1 are known to represent benign masses, while dynamic curves of type 3 are known to identify malignant masses. Type 2 dynamic curves have a sensitivity of 42.6% and specificity of 75% for malignancy detection. AIMTo investigate the pathological diagnosis of lesions with type 2 dynamic curves.METHODSWe evaluated breast MRI examinations performed between 2020 and 2021 retrospectively and included lesions with type 2 dynamic curves. We included 38 lesions from 33 patients. The lesions were evaluated for their pathological diagnosis and morphological characteristics. RESULTSTwenty-six lesions were malignant, while twelve were benign. The most frequently encountered benign lesion (7/12, 58.3%) was sclerosing adenosis, while the most frequently encountered malignant diagnosis was invasive ductal cancer. The presence of a type 2 dynamic curve had a sensitivity of 40.2% and specificity of 73.4% for predicting malignancy. By combining type 2 curves and morphological features, the sensitivity and specificity were increased.CONCLUSIONThe high rates of malignancy detected histopathologically among patients with type 2 dynamic curves in our study are remarkable. Type 2 dynamic curves can be detected in benign breast masses, especially in sclerosing adenosis cases. Considering morphological features can increase the diagnostic accuracy in cases with type 2 dynamic curves.

Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network.

The efficient biological signal processing method can effectively improve the efficiency of researchers to explore the work of life mechanism, so as to better reveal the relationship between physiological structure and function, thus promoting the generation of major biological discoveries; high-precision medical signal analysis strategy can, to a certain extent, share the pressure of doctors' clinical diagnosis and assist them to formulate more favorable plans for disease prevention and treatment, so as to alleviate patients' physical and mental pain and improve the overall health level of the society. This article in biomedical signal is very representative of the two types of signals: mammary gland molybdenum target X-ray image (mammography) and the EEG signal as the research object, combined with the deep learning field of CNN; the most representative model is two kinds of biomedical signal classification, and reconstruction methods conducted a series of research: (1) a new classification method of breast masses based on multi-layer CNN is proposed. The method includes a CNN feature representation network for breast masses and a feature decision mechanism that simulates the physician's diagnosis process. By comparing with the objective classification accuracy of other methods for the identification of benign and malignant breast masses, the method achieved the highest classification accuracy of 97.0% under different values of c and gamma, which further verified the effectiveness of the proposed method in the identification of breast masses based on molybdenum target X-ray images. (2) An EEG signal classification method based on spatiotemporal fusion CNN is proposed. This method includes a multi-channel input classification network focusing on spatial information of EEG signals, a single-channel input classification network focusing on temporal information of EEG signals, and a spatial-temporal fusion strategy. Through comparative experiments on EEG signal classification tasks, the effectiveness of the proposed method was verified from the aspects of objective classification accuracy, number of model parameters, and subjective evaluation of CNN feature representation validity. It can be seen that the method proposed in this paper not only has high accuracy, but also can be well applied to the classification and reconstruction of biomedical signals.

Value of ultrasonic S-Detect technique in diagnosis of breast masses

To evaluate the value of ultrasound S-Detect in the diagnosis of breast masses. A total of 85 breast masses in 62 female patients were diagnosed by S-Detect technique and conventional ultrasound. The diagnostic efficacy of conventional ultrasound and S-Detect technique was analyzed and compared with postoperative pathological results as the gold standard. When operated by junior physicians, the diagnostic efficacy of conventional ultrasound was significantly lower than that of S-Detect technique (P < 0.05), but this difference was not observed in moderately experienced and senior physicians (P>0.05). S-Detect technique was positively correlated with the diagnostic results of senior physicians (r=0.97). Using S-Detect technique, the diagnostic efficacy did not differ significantly between the long axis section and its vertical section (P>0.05). Routine ultrasound showed a better diagnostic efficacy than S-Detect for breast masses with a diameter below 20 mm (P < 0.05), but for larger breast masses, its diagnostic efficacy was significantly lower than that of SDetect (P < 0.05). S-Detect can be used in differential diagnosis of benign and malignant breast masses, and its diagnostic efficiency can be comparable with that of BI-RADS classification for moderately experienced and senior physicians, but its diagnostic efficacy can be low for breast masses less than 20 mm in diameter.

Evaluation of serum lipid profile in breast cancer patients: a case control study

Objective: This study was conducted to investigate the relationship between serum lipid profile and breast cancer disease.Patients and methods: One hundred breast cancer women and 75 women with benign breast mass as a control group were included in the study. The control and the patient groups were aged matched. The age range of the control group was between 25 to 70 years (mean ± SD: 50.4 ± 15.1 years). The age range of the patient group was between 25 to 71 yeas (mean ± SD: 49.1 ± 14.6 years). The patients were undergone radical modified mastectomy, then treated with chemotherapy and radiotherapy after the operation. Five mL of blood samples from the patient and control groups were taken, and the patient samples were taken before the operation. The blood samples were analyzed for serum lipid profile. The patients and the control groups were advised to fast for 14 hours before taking the blood samples. Body mass index was measured for the patients and controls. The stage, lymph involvement, and metastasis of the cancer were also included.Results: Body mass index (BMI) in patients was significantly higher than controls. However, serum total cholesterol (TC), triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C) and atherogenic index (AI), in the patients were significantly lower than controls. High-density lipoprotein cholesterol (HLD-C) was higher in the patients compared with the controls but not significant.Conclusion: a significant decrease in TC, TGs, and LDL-C with no significant increase for HDL-C was noticed in breast cancer women. Serum lipid profile may not be considered as a risk factor for breast cancer.

Distinction of Breast Masses from Benign to Malignant using Magnetic Resonance Imaging and Dynamic Contrast-Enhanced in Tertiary Care Hospitals of Taif, Saudi Arabia: A Retrospective Study

Background: Breast cancer is the most frequent cancer among women throughout the world. A range of noninvasive techniques have been employed for early prevention. In health-care practice, however, quality and sensitivity remained critical. Objective: The aim of this study is to see how well Breast Magnetic Resonance Imaging (MRIs) and Dynamic Contrast-Enhanced MRI (DCE-MRI) techniques can detect breast cancer and distinguish between malignant and benign lesions. Methods: A retrospective study was conducted at the Taif Hospitals, Saudi Arabia. The Picture Archiving and Communication System was used to acquire medical records and data from 50 individuals with probable breast cancer, and breast MRI pictures were analyzed. Breast Imaging Reporting and Data System (BI-RADS) radiologist reports and DCE-MRI kinetic curves were evaluated. Excel was also used to test the sensitivity and specificity of breast MRI. Results: According to the BI-RADS results, biopsies, and breast MRI data, 52 percent of 50 patients were categorized as benign, 24 percent as malignant, and 24 percent had no abnormalities. Biopsy revealed that 61.5 percent of the malignant lesions were benign, whereas 38.5 percent were appropriately identified as cancerous. The majority of malignant tumors were discovered in patients over the age of 50. The washout curve correctly identified 60% of the malignant lesions and 40% of the benign lesions. Our data demonstrated the usefulness of MRI in detecting breast cancers by analyzing BI-RADS and utilizing DCE-MRI. False-positive, on the other hand, can lead to unnecessary biopsies. Conclusion: Breast cancer is more common among women of their fifties and beyond. Biopsies, breast MRIs, and kinetic curve analysis can all be utilized to differentiate between benign and malignant breast masses with high sensitivity and specificity.