Search

This study evaluates the performance of the MaskFormer model for segmenting and classifying breast lesions using ultrasound images, addressing ultrasound's limitations. Ultrasound used for breast cancer detection faces challenges like low image contrast and difficulty in the detection of small or multiple lesions, further complicated by variability based on operator skill. Initial experiments with U-Net and other CNN-based models revealed constraints, such as early plateauing in model loss, which indicated suboptimal learning and performance. In contrast, MaskFormer demonstrated continuous improvement, achieving higher precision in breast lesion segmentation and significantly reducing both false positives and false negatives. Comparative analysis showed MaskFormer's superior performance, with the highest precision and recall rates for malignant lesions and an overall mean average precision (mAP) of 0.943. The model's ability to detect a diverse range of breast lesions, including those potentially missed by the human eye, especially by less experienced practitioners, underscores its potential. These findings suggest that integrating AI models like MaskFormer could greatly enhance ultrasound performance for breast cancer detection, providing reliable, operator-independent image analysis and potentially improving patient outcomes on a global scale. [ABSTRACT FROM AUTHOR]

Background: Previous studies have used different imaging sequences and different enhanced phases for breast lesion calsification in radiomics. The optimal sequence and contrast enhanced phase is unclear. Purpose: To identify the optimal magnetic resonance imaging (MRI) radiomics model for lesion clarification, and to simulate its incremental value for multiparametric MRI (mpMRI)‐guided biopsy. Study Type: Retrospective. Population: 329 female patients (138 malignant, 191 benign), divided into a training set (first site, n = 192) and an independent test set (second site, n = 137). Field Strength/Sequence: 3.0‐T, fast spoiled gradient‐echo and fast spin‐echo T1‐weighted imaging (T1WI), fast spin‐echo T2‐weighted imaging (T2WI), echo‐planar diffusion‐weighted imaging (DWI), and fast spoiled gradient‐echo contrast‐enhanced MRI (CE‐MRI). Assessment: Two breast radiologists with 3 and 10 years' experience developed radiomics model on CE‐MRI, CE‐MRI + DWI, CE‐MRI + DWI + T2WI, CE‐MRI + DWI + T2WI + T1WI at each individual phase (P) and for multiple combinations of phases. The optimal radiomics model (Rad‐score) was identified as having the highest area under the receiver operating characteristic curve (AUC) in the test set. Specificity was compared between a traditional mpMRI model and an integrated model (mpMRI + Rad‐score) at sensitivity >98%. Statistical Tests: Wilcoxon paired‐samples signed rank test, Delong test, McNemar test. Significance level was 0.05 and Bonferroni method was used for multiple comparisons (P = 0.007, 0.05/7). Results: For radiomics models, CE‐MRI/P3 + DWI + T2WI achieved the highest performance in the test set (AUC = 0.888, 95% confidence interval: 0.833–0.944). The integrated model had significantly higher specificity (55.3%) than the mpMRI model (31.6%) in the test set with a sensitivity of 98.4%. Data Conclusion: The CE‐MRI/P3 + DWI + T2WI model is the optimized choice for breast lesion classification in radiomics, and has potential to reduce benign biopsies (100%–specificity) from 68.4% to 44.7% while retaining sensitivity >98%. Level of Evidence: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to compare shear wave elastography (SWE) of benign and malignant breast lesions, evaluate the sensitivity and specificity of previously suggested thresholds for identifying malignant breast lesions, and compare SWE measures across histopathological type and grade. Methods: This single‐centre study included 303 patients, and 405 solid breast lesions were biopsied and evaluated by mammography, which may have included contrast‐enhanced mammography, conventional B‐mode ultrasound, and SWE. Following this, elastography mean (Emean), maximum (Emax), and ratio (Eratio) variables were calculated for elasticity in kilopascals (kPa) and speed in metres per second (m/s). Results: Malignant (n = 113) samples were significantly higher than benign (n = 267) across all SWE variables [median (interquartile range)]; Emean (kPa) [132.8 (34), vs. 41.9 (53.6), p <.001], Emean (m/s) [6.9 (1), vs. 3.7 (2.2), p <.001]. The highest combined sensitivity and specificity were for Emax (97%, 73%) at >80 kPa, and Emean (96%, 76%) at >5.2 m/s. Histopathological grade 3 (kPa), Emean [145.8 (22.5), p =.012], Emax [147.8 (19.4), p =.009], and Eratio [11.7 (6.6), p =.006] were significantly higher than grades 1 [129.2 (37.3), 133.2 (35.9), 7.53 (4.7)] and 2 [127.3 (36.4), 133.3 (34.2), 8.3 (6.1)]. Conclusion: Breast elastography is a sensitive complementary technique that can distinguish between malignant and benign lesions and help characterise histological profile and grade. [ABSTRACT FROM AUTHOR]

Background: Several image-based diagnostic methods have been developed to examine the features of breast lesions among women, while the value of combining palpation imaging and ultrasound by a bimodal breast examination system is still unknown. Methods: A real-world study was conducted among 424 patients who visited Fujian Maternal and Child Health Hospital and Fujian Obstetrics and Gynecology Hospital, and used the Bimodal Breast Exam (BBE) systems which combines palpation imaging and ultrasound imaging. Among them, 97 patients had additional ultrasound, mammogram, or pathological examination. These patients were used to evaluate the consistency and efficacy of the BBE in interpreting the features of breast lesions as compared to results of ultrasound, mammogram, and pathological examinations. Results: The BBE system detected 1517 lesions with palpation imaging, 1126 lesions with ultrasound examination (950 solid lesions and 176 cysts), and 391 non mass lesions. Among them, 404 patients were diagnosed as benign and 20 were diagnosed as malignant tumor. However, 12, 9 and 4 cases were diagnosed as malignant tumors by ultrasound, mammogram and pathological examination, respectively. Compared with the integrative results of ultrasound, mammogram and pathology, the sensitivity of BBE is 55.6%, and the specificity is 90.9%, with a kappa coefficient of 0.387 (0.110, 0.665), indicating moderate consistency. Conclusions: In clinical practice, BBE can be used to evaluate features of breast lesions with a high specificity. The diagnostic efficacy is comparable to the integrative results of ultrasound, mammography, and pathological examination. [ABSTRACT FROM AUTHOR]

Purpose: Contrast-enhanced mammography (CEM) is an innovative imaging tool for breast cancer detection, involving intravenous injection of a contrast medium and the assessment of lesion enhancement in two phases: early and delayed. The aim of the study was to analyze the topographic concordance of lesions detected in the early- versus delayed phase acquisitions. Materials and methods: Approved by the Ethics Committee (No. 118/20), this prospective study included 100 women with histopathological confirmed breast neoplasia (B6) at the Radiodiagnostics Department of the Maggiore della Carità Hospital of Novara, Italy from May 1, 2021, to October 17, 2022. Participants underwent CEM examinations using a complete protocol, encompassing both early- and delayed image acquisitions. Three experienced radiologists blindly analyzed the CEM images for contrast enhancement to determine the topographic concordance of the identified lesions. Two readers assessed the complete study (protocol A), while one reader assessed the protocol without the delayed phase (protocol B). The average glandular dose (AGD) of the entire procedure was also evaluated. Results: The analysis demonstrated high concordance among the three readers in the topographical identification of lesions within individual quadrants of both breasts, with a Cohen's κ > 0.75, except for the lower inner quadrant of the right breast and the retro-areolar region of the left breast. The mean whole AGD was 29.2 mGy. The mean AGD due to CEM amounted to 73% of the whole AGD (21.2 mGy). The AGD attributable to the delayed phase of CEM contributed to 36% of the whole AGD (10.5 mGy). Conclusions: As we found no significant discrepancy between the readings of the two protocols, we conclude that delayed-phase image acquisition in CEM does not provide essential diagnostic benefits for effective disease management. Instead, it contributes to unnecessary radiation exposure. [ABSTRACT FROM AUTHOR]

Abstract Background To compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue. Methods This prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model. Results Almost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P

Background: To compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue. Methods: This prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model. Results: Almost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F1, C1C2, and C2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively). Conclusions: Quantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of the MaskFormer model for segmenting and classifying breast lesions using ultrasound images, addressing ultrasound’s limitations. Ultrasound used for breast cancer detection faces challenges like low image contrast and difficulty in the detection of small or multiple lesions, further complicated by variability based on operator skill. Initial experiments with U-Net and other CNN-based models revealed constraints, such as early plateauing in model loss, which indicated suboptimal learning and performance. In contrast, MaskFormer demonstrated continuous improvement, achieving higher precision in breast lesion segmentation and significantly reducing both false positives and false negatives. Comparative analysis showed MaskFormer’s superior performance, with the highest precision and recall rates for malignant lesions and an overall mean average precision (mAP) of 0.943. The model’s ability to detect a diverse range of breast lesions, including those potentially missed by the human eye, especially by less experienced practitioners, underscores its potential. These findings suggest that integrating AI models like MaskFormer could greatly enhance ultrasound performance for breast cancer detection, providing reliable, operator-independent image analysis and potentially improving patient outcomes on a global scale.

BACKGROUND: Early breast cancer diagnosis is of great clinical importance for selecting treatment options, improving prognosis, and enhancing the quality of patients' survival. OBJECTIVE: We investigated the value of virtual touch tissue imaging average grayscale values (VAGV) helper Breast Imaging Reporting and Data System (BI-RADS) in diagnosing breast malignancy. METHODS: We retrospectively analyzed 141 breast tumors in 134 patients. All breast lesions were diagnosed pathologically by biopsy or surgical excision. All patients first underwent conventional ultrasound (US) followed by virtual touch tissue imaging (VTI). The measurement of the VAGV of the lesion was performed by Image J software. BI-RADS classification was performed for each lesion according to the US. We performed a two-by-two comparison of the diagnostic values of VAGV, BI-RADS, and BI-RADS + VAGV. RESULTS: VAGV was lower in malignant tumors than in benign ones (35.82 ± 13.39 versus 73.58 ± 42.69, P < 0.001). The area under the receiver operating characteristic curve (AUC) value, sensitivity, and specificity of VAGV was 0.834, 84.09%, and 69.07%, respectively. Among BI-RADS, VAGV, and BI-RADS + VAGV, BI-RADS + VAGV had the highest AUC (0.926 versus 0.882, P = 0.0066; 0.926 versus 0.834, P = 0.0012). There was perfect agreement between the two radiologists using VAGV (ICC = 0.9796) and substantial agreement using BI-RADS (Kappa = 0.725). CONCLUSION: Our study shows that VAGV can accurately diagnose breast cancer. VAGV effectively improves the diagnostic performance of BI-RADS. [ABSTRACT FROM AUTHOR]

Objective Carney complex (CNC) is a rare genetic syndrome, mostly due to germline loss-of-function pathogenic variants in PRKAR1A. Carney complex includes pigmented skin lesions, cardiac myxomas, primary pigmented nodular adrenocortical dysplasia, and various breast benign tumors. Design The present study was designed to describe the characteristics of breast lesions in CNC patients and their association with other manifestations of CNC and PRKAR1A genotype. Methods A 3-year follow-up multicenter French prospective study of CNC patients included 50 women who were analyzed for CNC manifestations and particularly breast lesions, with breast imaging, genotyping, and hormonal settings. Results Among the 38 women with breast imaging, 14 (39%) had breast lesions, half of them bilateral. Ten women (26%) presented with benign lesions and six with breast carcinomas (16%): one had ductal carcinoma in situ at 54, and five had invasive cancer before 50 years old, whom one with contralateral breast cancer during follow-up. The occurrence of breast cancer was more frequent in women with PRKAR1A pathogenic variant odds ratio = 6.34 (1.63-17.91) than in general population of same age. The mean age at breast cancer diagnosis was 44.7 years old: 17 years younger than in the general population. Breast cancer patients had good prognosis factors. All breast carcinomas occurred in individuals with familial CNC and PRKAR1A pathogenic variants. Loss of heterozygosity at the PRKAR1A locus in the 2 invasive breast carcinomas analyzed suggested a driver role of this tumor suppressor gene. Conclusions As CNC could predispose to breast carcinoma, an adequate screening strategy and follow-up should be discussed in affected women. Clinical Trial Registration ClinicalTrial.gov NCT00668291. [ABSTRACT FROM AUTHOR]

Background: The occurrence of breast cancer is increasing worldwide with peak incidence occurring above the age of 50 years in developed countries, whereas in india, it is above the age of 40. Fine-needle aspiration cytology (FNAC) is a quick, easy, and cost-effective diagnostic tool for breast disease, and also helpful to differentiate between various benign and malignant lesions of breast. Aims and Objectives: (1) to assess the usefulness of Modified Masood’s Scoring Index (MMSI) in breast cytology. (2) To study cytohistopathological correlation in breast lesions. After the FNAC of breast lesion, classify the breast lesion based on MMSI and histopathology. Materials and Methods: A hospital-based prospective study was conducted between March 2021 and September 2022 on 183 patients who were referred for FNAC from the surgery department to the cytology section of the Pathology Department at Netaji Subhash Chandra Bose Medical College Hospital in Jabalpur, Madhya Pradesh. Results: In our study, the largest group of cases (45.90%) was in the 20–39 age range, with a mean age of 37±14.65. The majority of cases (60.6%) belonged to category II (proliferative breast disease [PBD] without atypia) with a mean MMSI score of 11.35±5.55. The cytological findings showed a correlation of 98.20% with MMSI in category II and 88.68% in category IV, while the histopathological findings showed a correlation of 89.52% with MMSI category II and 98.15% with MMSI category IV. Fibroadenoma (FA) was the most common finding on histopathological examination, accounting for 120 cases (65.6%). Conclusion: The MMSI is an effective tool to complement cytomorphological diagnosis in breast lesions, including PBD with or without atypia and carcinomas. It is particularly valuable in the early management and prognosis of patients, as treatment options can vary based on the MMSI score.

Abstract Background In recent years, contrast-enhanced ultrasonography (CEUS) has been used for various applications in breast diagnosis. The superiority of CEUS over conventional B-mode imaging in the ultrasound diagnosis of the breast lesions in clinical practice has been widely confirmed. On the other hand, there have been many proposals for computer-aided diagnosis of breast lesions on B-mode ultrasound images, but few for CEUS. We propose a semi-automatic classification method based on machine learning in CEUS of breast lesions. Methods The proposed method extracts spatial and temporal features from CEUS videos and breast tumors are classified as benign or malignant using linear support vector machines (SVM) with combination of selected optimal features. In the proposed method, tumor regions are extracted using the guidance information specified by the examiners, then morphological and texture features of tumor regions obtained from B-mode and CEUS images and TIC features obtained from CEUS video are extracted. Then, our method uses SVM classifiers to classify breast tumors as benign or malignant. During SVM training, many features are prepared, and useful features are selected. We name our proposed method "Ceucia-Breast" (Contrast Enhanced UltraSound Image Analysis for BREAST lesions). Results The experimental results on 119 subjects show that the area under the receiver operating curve, accuracy, precision, and recall are 0.893, 0.816, 0.841 and 0.920, respectively. The classification performance is improved by our method over conventional methods using only B-mode images. In addition, we confirm that the selected features are consistent with the CEUS guidelines for breast tumor diagnosis. Furthermore, we conduct an experiment on the operator dependency of specifying guidance information and find that the intra-operator and inter-operator kappa coefficients are 1.0 and 0.798, respectively. Conclusion The experimental results show a significant improvement in classification performance compared to conventional classification methods using only B-mode images. We also confirm that the selected features are related to the findings that are considered important in clinical practice. Furthermore, we verify the intra- and inter-examiner correlation in the guidance input for region extraction and confirm that both correlations are in strong agreement.

Abstract Background Systemic lupus erythematosus is an autoimmune disease that can have cutaneous and systemic manifestations. Lupus panniculitis, also known as lupus mastitis, is a subset of chronic cutaneous lupus erythematosus that involves inflammation of the subcutaneous fat. The pathogenesis of lupus mastitis is not fully understood. Diagnosis involves a combination of skin manifestations, imaging, and pathologic confirmation. Treatment typically includes steroids and antimalarials, with more severe disease requiring additional immunosuppressive medications. This report highlights a case of lupus mastitis treated with rituximab and a possible relationship between this disease process and thrombotic disease. Case presentation A 48-year-old African American female with systemic lupus erythematosus and antiphospholipid syndrome presented with new breast lesion. Mammography revealed calcifications and increased density with coarse trabecular pattern. Breast biopsy showed features of cutaneous lupus and occlusive vasculopathy. The patient was diagnosed with lupus mastitis and treated with anticoagulation, rituximab, mycophenolate mofetil, and quinacrine with resolution of her symptoms. Conclusion This patient experienced improvement in her breast symptoms with combination therapy including rituximab. There are only two other cases reported in literature of patients with lupus mastitis responding to rituximab, highlighting the possible role of B cell depleting therapy for those who have contraindications to standard treatments for lupus mastitis. While the pathophysiology of lupus mastitis is thought to be immune driven, some literature suggests that associated thrombosis commonly seen may be due to a physiologic overlap similar to antiphospholipid syndrome. The possible relationship between antiphospholipid syndrome and lupus mastitis and the use of antiplatelet and anticoagulation therapy is discussed and may warrant further investigation.

To establish a good predictive model using a deep-learning (DL)-based three-dimensional (3D) super-resolution ultrasound images for the diagnosis of benign and malignant breast lesions.Methods: This retrospective study included 333 patients with histopathologically confirmed breast lesions, randomly split into training (N=266) and testing (N=67) datasets. Eight models, including four deep learning models (ORResNet101, ORMobileNet_v2, SRResNet101, SRMobileNet_v2) and four machine learning models (OR_LR, OR_SVM, SR_LR, SR_SVM), were developed based on original and super-resolution images. The best performing model was SRMobileNet_v2, which was used to construct a nomogram integrating clinical factors. The performance of nomogram was evaluated using receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and calibration curves.Results: SRMobileNet_v2, MobileNet_V2 based on super-resolution ultrasound images, had the best predictive performance in four traditional machine learning models and four deep learning models, with AUC improvements of 0.089 and 0.031 in the training and testing sets, relative to the ORMobileNet_v2 model based on original ultrasound images. The deep-learning nomogram was constructed using the SRMobileNet_v2 model score, tumor size, and patient age, resulting in superior predictive efficacy compared to the nomogram without the SRMobileNet_v2 model score. Furthermore, it demonstrated favorable calibration, discrimination, and clinical utility in both cohorts.Conclusion: The diagnostic prediction model utilizing super-resolution reconstructed ultrasound images outperforms the model based on original images in distinguishing between benign and malignant breast lesions. The nomogram based on super-resolution ultrasound images has the potential to serve as a reliable auxiliary diagnostic tool for clinicians, exhibiting superior predictive performance in distinguishing between benign and malignant breast lesions. [ABSTRACT FROM AUTHOR]

BACKGROUND: FNAC of breast lumps is an accepted and established method for determine the natures of breast lumps with a high degree of accuracy and it may play an important role when it is difficult to determine the nature of breast lump by clinical examination. MATERIAL AND METHOD: The present study is hospital based cross sectional study was carry out from March 2021 to September 2022 on 183 patients in cytology section of pathology department, Netaji Subhash Chandra Bose Medical College & Hospital Jabalpur (M.P). RESULT: In our study, the maximum number of cases was in the 20-39 years age group, with 84 cases (45.90%). The mean age was 37 ± 14.65. The majority of breast lesions presented as a lump, with 157 cases (85.8%). The size was <2 cm in 76 cases (41.53%), unilateral in 164 cases (89.62%), located in the upper outer quadrant in 117 cases (63.93%), firm in consistency in 122 cases (66.67%), with regular margins in 128 cases (69.95%), mobile in 126 cases (68.85%), and the aspirate material was whitish in color in 121 cases (66.12%). In the present study, the most common pattern on cytology was fibroadenoma (FA), accounting for 105 cases (57.4%) CONCLUSION: We concluded that FNAC is simple rapid, minimally invasive cost effective, reliable outpatient procedure highly sensitive in making early and accurate diagnosis. [ABSTRACT FROM AUTHOR]

Background: The occurrence of breast cancer is increasing worldwide with peak incidence occurring above the age of 50 years in developed countries, whereas in india, it is above the age of 40. Fine-needle aspiration cytology (FNAC) is a quick, easy, and cost-effective diagnostic tool for breast disease, and also helpful to differentiate between various benign and malignant lesions of breast. Aims and Objectives: (1) to assess the usefulness of Modified Masood's Scoring Index (MMSI) in breast cytology. (2) To study cytohistopathological correlation in breast lesions. After the FNAC of breast lesion, classify the breast lesion based on MMSI and histopathology. Materials and Methods: A hospital-based prospective study was conducted between March 2021 and September 2022 on 183 patients who were referred for FNAC from the surgery department to the cytology section of the Pathology Department at Netaji Subhash Chandra Bose Medical College Hospital in Jabalpur, Madhya Pradesh. Results: In our study, the largest group of cases (45.90%) was in the 20-39 age range, with a mean age of 37±14.65. The majority of cases (60.6%) belonged to category II (proliferative breast disease [PBD] without atypia) with a mean MMSI score of 11.35±5.55. The cytological findings showed a correlation of 98.20% with MMSI in category II and 88.68% in category IV, while the histopathological findings showed a correlation of 89.52% with MMSI category II and 98.15% with MMSI category IV. Fibroadenoma (FA) was the most common finding on histopathological examination, accounting for 120 cases (65.6%). Conclusion: The MMSI is an effective tool to complement cytomorphological diagnosis in breast lesions, including PBD with or without atypia and carcinomas. It is particularly valuable in the early management and prognosis of patients, as treatment options can vary based on the MMSI score. [ABSTRACT FROM AUTHOR]

Son yıllarda bilgisayar donanımları ile paralel olarak gelişim gösteren yapay zeka çalışmaları klinikte uzmanların erken teşhis ile olası metastazın önüne geçerek hasta sağ kalımını artırmaktadır. Literatürde klinikte kanser teşhisini gerçekleştiren çokça çalışma mevcuttur. Bu çalışmalarda, kanser sınıflandırmasının yapılması için makine öğrenmesi ve derin öğrenme uygulamaları sıklıkla uygulanmaktadır. Benzer şekilde çalışmada termal meme görüntüleri üzerinden derin öğrenme yöntemleri ile meme kanseri teşhisi ele alınmıştır. Çalışmada kullanılan görüntüler açık erişim olarak sunulan DMR-IR veri setinden alınmıştır. Veri setinden alınan görüntüler üzerinde bazı önişlemler yapılmış, ardından meme bölgelerinin bölütlenmesi için manuel ve otomatik olmak üzere iki farklı bölütleme metodu uygulanmıştır. Manuel bölütleme işleminde, VIA ile lokalizasyon bilgisi kaydedilen meme bölgelerinin maskesi oluşturup orijinal görüntüden çıkarılarak bölütleme gerçekleştirilmiştir. Otomatik bölütleme işleminde ise Mask R-CNN ve U-NET ile bölütleme yapılmıştır. Bu iki metot için bölütleme performans analizi yapılmış ve 0.9896 doğruluk, 0.9413 Dice ve 0.8900 Jaccard değerini gerçekleştiren Mask R-CNN ile sınıflandırma işlemleri çalışılmıştır. Manuel ve Mask-RCNN metodu ile bölütlenen görüntülerden oluşan termogramlar ile ön eğitimli yedi farklı (InceptionV3, MobileNet, MobileNetV2, ResNet50, VGG16, VGG19 ve Xception) mimari kullanılarak meme kanseri sınıflandırması gerçekleştirilmiştir. Sonuç olarak test verilerinde %100 sınıflandırma başarısını doğruluk, kesinlik, duyarlılık ve F1 Skoru ile MobileNet ve InceptionV3 mimarileri sağlamıştır.

Objective: To develop a deep learning (DL) model for predicting axillary lymph node (ALN) metastasis using dynamic ultrasound (US) videos in breast cancer patients. Methods: A total of 271 US videos from 271 early breast cancer patients collected from Xiang'an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultrasonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models. Results: Among the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P<0.05). The Grad-CAM technology confirmed the heatmap of the model, which highlighted important subregions of the keyframe for ultra-sonographers' review. Conclusion: A feasible and improved DL model to predict ALN metastasis from breast cancer US video images was developed. The DL model in this study with reliable interpretability would provide an early diagnostic strategy for the appropriate management of axillary in the early breast cancer patients. [ABSTRACT FROM AUTHOR]

Medical Imaging with Deep Learning has recently become the most prominent topic in the scientific world. Significant results have been obtained in the classification of medical images using deep learning methods, and there has been an increase in studies on malignant types. The main reason for choosing breast cancer is that breast cancer is one of the critical malignant types that increase the death rate in women. In this study, 1236 ultrasound images were collected from Elazig Fethi Sekin City Hospital, and three different ResNet CNN architectures were used for feature extraction. Data were trained with an SVM classifier. In addition, the three ResNet architectures were combined, and novel fused ResNet architecture was used in this study. In addition, these features were used with three different feature selection techniques, MR‐MR, NCA, and Relieff. These results are 89.3% obtained from ALL‐ResNet architecture and the feature selected with NCA in normal and lesion classification. Normal, malignant, and benign classification best accuracy is 84.9% with ALL‐ResNet NCA. Experimental studies show that MR‐MR, NCA, and Relieff feature selection algorithms reduce features and give more results that are successful. This indicates that the proposed method is more successful than classical deep learning methods. [ABSTRACT FROM AUTHOR]

Breast lesions arising from IgG4-related diseases are rare and can be difficult to differentiate from breast cancer based on only imaging findings. A 46 year-old female was referred to our hospital complaining of swelling in her bilateral submandibular gland. Contrast-enhanced CT displayed the bilateral swelling of the submandibular gland, a pancreas mass, and a right breast mass. A needle biopsy of the breast showed a dense infiltration of lymphocytes and plasma cells with accompanying stromal fibrosis, as well as positive immunohistochemical reactions for IgG4 and increased IgG4 serum levels. These findings resulted in a diagnosis of IgG4-related disease, and oral steroid treatment was initiated. The breast tumor disappeared after 6 months. [ABSTRACT FROM AUTHOR]

Copyright of Türk Radyoloji Seminerleri is the property of Galenos Yayinevi Tic. LTD. STI 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.)

Background: In recent years, contrast-enhanced ultrasonography (CEUS) has been used for various applications in breast diagnosis. The superiority of CEUS over conventional B-mode imaging in the ultrasound diagnosis of the breast lesions in clinical practice has been widely confirmed. On the other hand, there have been many proposals for computer-aided diagnosis of breast lesions on B-mode ultrasound images, but few for CEUS. We propose a semi-automatic classification method based on machine learning in CEUS of breast lesions. Methods: The proposed method extracts spatial and temporal features from CEUS videos and breast tumors are classified as benign or malignant using linear support vector machines (SVM) with combination of selected optimal features. In the proposed method, tumor regions are extracted using the guidance information specified by the examiners, then morphological and texture features of tumor regions obtained from B-mode and CEUS images and TIC features obtained from CEUS video are extracted. Then, our method uses SVM classifiers to classify breast tumors as benign or malignant. During SVM training, many features are prepared, and useful features are selected. We name our proposed method "Ceucia-Breast" (Contrast Enhanced UltraSound Image Analysis for BREAST lesions). Results: The experimental results on 119 subjects show that the area under the receiver operating curve, accuracy, precision, and recall are 0.893, 0.816, 0.841 and 0.920, respectively. The classification performance is improved by our method over conventional methods using only B-mode images. In addition, we confirm that the selected features are consistent with the CEUS guidelines for breast tumor diagnosis. Furthermore, we conduct an experiment on the operator dependency of specifying guidance information and find that the intra-operator and inter-operator kappa coefficients are 1.0 and 0.798, respectively. Conclusion: The experimental results show a significant improvement in classification performance compared to conventional classification methods using only B-mode images. We also confirm that the selected features are related to the findings that are considered important in clinical practice. Furthermore, we verify the intra- and inter-examiner correlation in the guidance input for region extraction and confirm that both correlations are in strong agreement. [ABSTRACT FROM AUTHOR]

Background: Systemic lupus erythematosus is an autoimmune disease that can have cutaneous and systemic manifestations. Lupus panniculitis, also known as lupus mastitis, is a subset of chronic cutaneous lupus erythematosus that involves inflammation of the subcutaneous fat. The pathogenesis of lupus mastitis is not fully understood. Diagnosis involves a combination of skin manifestations, imaging, and pathologic confirmation. Treatment typically includes steroids and antimalarials, with more severe disease requiring additional immunosuppressive medications. This report highlights a case of lupus mastitis treated with rituximab and a possible relationship between this disease process and thrombotic disease. Case presentation: A 48-year-old African American female with systemic lupus erythematosus and antiphospholipid syndrome presented with new breast lesion. Mammography revealed calcifications and increased density with coarse trabecular pattern. Breast biopsy showed features of cutaneous lupus and occlusive vasculopathy. The patient was diagnosed with lupus mastitis and treated with anticoagulation, rituximab, mycophenolate mofetil, and quinacrine with resolution of her symptoms. Conclusion: This patient experienced improvement in her breast symptoms with combination therapy including rituximab. There are only two other cases reported in literature of patients with lupus mastitis responding to rituximab, highlighting the possible role of B cell depleting therapy for those who have contraindications to standard treatments for lupus mastitis. While the pathophysiology of lupus mastitis is thought to be immune driven, some literature suggests that associated thrombosis commonly seen may be due to a physiologic overlap similar to antiphospholipid syndrome. The possible relationship between antiphospholipid syndrome and lupus mastitis and the use of antiplatelet and anticoagulation therapy is discussed and may warrant further investigation. [ABSTRACT FROM AUTHOR]

Breast cancer is among the most common cancers diagnosed in women globally. To help the breast cancer diagnosis, an important step is to accurately segment the breast lesion. To support clinicians in this important step, we analyze the performance of a semi-automated segmentation method based on the Local Graph Cut technique in the Image Segmenter application. Local graph cuts algorithm has the ability to segment more complicated shape by converting the image into a graph representation. It employs seed points set by the user and a cost function. The user identifies certain pixels as foreground and background. The region properties are identified from these pixels and they allow to specify the probability of a pixel belonging to the background or foreground. The graph cut formulation assigns each pixel to a node in the graph and incorrectly segmented pixels are re-assigned until the desired segmentation is completed. To evaluate the segmentation results, the Dice similarity coefficient and Fréchet distance were calculated between the ground truth images and the segmented images. Results show a Dice score of 0.7754 for malignant lesions and 0.8842 for benign lesions. The average Fréchet distance values were 303.28 for malignant and 290.80 for benign lesions, respectively. The experimental results show that the method achieves the best performance and gets the higher Dice score and Fréchet distance for breast benign lesions against malignant lesions. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence studies, which have developed in parallel with computer hardware, have helped increase patient survival by preventing possible metastasis with early diagnosis of clinicians. There are many studies in literature that carry out diagnosis of cancer in the clinic. In these studies, machine learning and deep learning applications are frequently applied for cancer classification. Similarly, in this study, diagnosis of breast cancer over thermal images with the help of deep learning methods was discussed. The images used in the study were taken open access made available in the DMR-IR dataset. Some preprocessing was done on the images before manual and automatic segmentation methods were applied to segment the breast regions on images. In the manual segmentation process, the mask of the breast regions whose localization information was recorded with VIA was created and the segmentation was performed by subtracting the mask from the original image. In the automatic segmentation process, segmentation was done using Mask R-CNN and U-NET techniques. Segmentation performance analysis was performed for these two methods and classification operations were done with Mask R-CNN, which realized 0.9896 accuracy, 0.9413 Dice and 0.8900 Jaccard. Breast cancer classification was carried out using seven pre-trained architectures (InceptionV3, MobileNet, MobileNetV2, ResNet50, VGG16, VGG19 and Xception) with thermograms consisting of images segmented by manual and Mask-RCNN methods. As a result, MobileNet and InceptionV3 architectures provided the 100% classification success in test data accuracy, precision, sensitivity and F1 Score. [ABSTRACT FROM AUTHOR]

Objective: We herein compared the diagnostic accuracy of the BI‐RADS, ABVS, SWE, and combined techniques for the classification of breast lesions. Methods: Breast lesions were appraised using the BI‐RADS classification system as well as the combinations of BI‐RADS plus ABVS (BI‐RADS + ABVS) and BI‐RADS plus SWE (BI‐RADS + SWE), and both methods (BI‐RADS + ABVS + SWE) by two specialties Medical Ultrasound physician. The Fisher's exact and χ2 tests were performed to compare the degree of malignancy for the various methods with a pathology ground truth. Receiver operating characteristic curves (ROC) were generated and the corresponding area under the curve (AUC) values were determined to test the diagnostic efficacy of the various methods and identify the optimal SWE cut‐off indicative of malignancy. Results: The incidence of the retraction phenomenon on ABVS images of the malignant group was significantly higher (P <.001) than that of the benign group. The specificity, sensitivity, and positive and negative predictive values of the BI‐RADS classification were 88.72, 79.38, 83.70, and 85.50%, respectively. BI‐RADS plus SWE‐Max exhibited enhanced specificity, sensitivity, and positive and negative predictive values of 88.72, 92.78, 85.70, and 94.40%, respectively. Similarly, when BI‐RADS + ABVS was utilized, the sensitivity and negative predictive value increased to 95.88 and 96.40%, respectively. BI‐RADS + ABVS + SWE possessed the highest overall sensitivity (96.91%), specificity (94.74%), and positive (93.10%) and negative (97.70%) predictive values from all four indices. Conclusion: ABVS and SWE can reduce the subjectivity of BI‐RADS. As a result, BI‐RADS + ABVS + SWE resulted in the best diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Pectoralis major muscle sarcomas are extremely rare and can mimic breast lesion clinically. We report a case of poorly differentiated sarcoma of the pectoralis major muscle in a 63-year-old woman of south east Asian ethnicity presenting with a progressively increasing right breast lump. Mammography, ultrasonography (US), contrast-enhanced computed tomography, and biopsy were done to make the final diagnosis. Complete surgical excision was planned but deferred due to pulmonary metastasis, and the patient was treated with palliative chemotherapy. Clinical examination may be confusing but radiological and pathological investigations provide detailed information about the location and the extent of the disease and a definitive tissue diagnosis can only be made on histopathology which will be helpful in preoperative planning and further treatment of the patient.

The aim of this study was to explore whether intravoxel incoherent motion (IVIM) combined with a dynamic contrast–enhanced magnetic resonance imaging (DCE–MRI) quantitative model can improve the ability to distinguish between benign and malignant BI-RADS 4 breast lesions. We enrolled 100 patients who underwent breast MRI at our institution and extracted the quantitative parameters of lesions with a post-processing workstation. Statistical differences in these parameters between benign and malignant BI-RADS 4 lesions were assessed using a two independent samples t-test or a Mann–Whitney U test. Binary logistic regression analysis was performed to establish five diagnostic models (model_ADC, model_IVIM, model_DCE, model_DCE+ADC, and model_DCE+IVIM). Receiver operating characteristic (ROC) curves, leave-one-out cross-validation, and the Delong test were used to assess and compare the diagnostic performance of these models. The model_DCE+IVIM showed the highest area under the curve (AUC) of 0.903 (95% confidence interval (CI): 0.828–0.953, sensitivity: 87.50%, specificity: 85.00%), which was significantly higher than that of model_ADC (p = 0.014) and model_IVIM (p = 0.033). The model_ADC had the lowest diagnostic performance (AUC = 0.768, 95%CI: 0.672–0.846) but was not significantly different from model_IVIM (p = 0.168). The united quantitative model with DCE–MRI and IVIM could improve the ability to evaluate the malignancy in BI-RADS 4 lesions, and unnecessary breast biopsies may be obviated.

Introduction: To compare the accuracy of Artificial Intelligent Breast Ultrasound (AIBUS) with hand-held breast ultrasound (HHUS) in asymptomatic women and to offer recommendations for screening in regions with limited medical resources. Methods: 852 participants who underwent both HHUS and AIBUS were enrolled between December 2020 and June 2021. Two radiologists, who were unaware of the HHUS results, reviewed the AIBUS data and scored the image quality on a separate workstation. Breast imaging reporting and data system (BI-RADS) final recall assessment, breast density category, quantified lesion features, and examination time were evaluated for both devices. The statistical analysis included McNemar's test, paired t-test, and Wilcoxon test. The kappa coefficient and consistency rate were calculated in different subgroups. Results: Subjective satisfaction with AIBUS image quality reached 70%. Moderate agreements were found between AIBUS with good quality images and HHUS for the BI-RADS final recall assessment (k = 0.47, consistency rate = 73.9%) and breast density category (k = 0.50, consistency rate = 74.8%). The lesions measured by AIBUS were statistically smaller and deeper than those measured by HHUS (P < 0.001), though they were not significant in clinical diagnosis (all < 3 mm). The total time required for the AIBUS examination and image interpretation was 1.03 (95% CI (0.57, 1.50)) minutes shorter than that of HHUS per case. Conclusion: Moderate agreement was obtained for the description of the BIRADS final recall assessment and breast density category. With image quality comparable to that of HHUS, AIBUS was superior for the efficiency of primary screening. [ABSTRACT FROM AUTHOR]

Key Clinical Message: SAN should be considered in the setting of nipple discharge or morphology changes with typical histological findings. There are limited published cases of SAN, and workup of this pathology is still not clear to date. Syringomatous adenoma of the nipple (SAN) is known to be a rare benign breast neoplasm. With a few cases documented in the literature, preoperatively diagnosing this tumor is a challenge, which often leads to invasive procedure such as mass excision with nipple removal. This study was aimed at presenting a case report of SAN and to conduct a review of published cases. Literature search was conducted through PubMed databases. Articles published from year 1983 to March of 2022 were included. Only histologically confirmed cases of SAN were included. The review was performed according to the PRISMA guidelines. Twenty‐eight cases, including the newly reported case, were included in the review after going through inclusion criteria. The mean age at diagnosis was 44 ± 16 years. 7% were male. The most common presentation was palpable mass. Preoperative biopsy was done for 9 cases, out of which 7 did not indicate typical histopathological characteristic of SAN. Most common treatment was wide local excision with nipple removal. Immunohistochemical staining of the resected tumor was performed in 16 cases postoperatively. 32.1% (9/28) utilized p63 in constellation with histologic findings. Five cases that utilized staining also used Estrogen Receptor (ER) marker, while three used progesterone receptor (PR) marker. SAN should be considered in the setting of nipple discharge or morphology changes with typical histological findings. There are limited published cases of SAN, and workup of this pathology is still not clear to date. The case presented here and our comprehensive literature review suggest that pathohistological findings of SAN can be heterogeneous. Clinicians would also benefit from recognizing these variances. Further research and reported cases are needed to confidently diagnose SAN, which may open doors for less aggressive surgical treatment or surveillance option for asymptomatic patients. [ABSTRACT FROM AUTHOR]

Infrared thermography is a potential method to improve efficiency for early detection of breast cancer. This technique does not use ionizing radiation and is feasible for screening in men and for detecting changes in young women. In this study, ninety-eight infrared images were used to create a database to develop a computer-aided diagnosis system. Typically, this kind of system is associated with graphical interfaces to facilitate users' work. In this study, the computer-aided diagnosis was implemented based on statistical classifiers for analysis of four classes: Malignant Tumor, Benign Tumor, Cyst and Healthy. The region of interest was segmented in automatic and semiautomatic ways, which is respectively associated with the Support Vector Machine classifier and Mahalanobis classifier. To evaluate the performance of the proposed classifiers, a confusion matrix was applied to each result obtained. Using the proposed GUI-CAD tool, it was possible to carry out individual and unsupervised classification of patients, with 93% sensitivity. [ABSTRACT FROM AUTHOR]

Introduction: Worldwide breast cancer is the leading type of cancer in women accounting for 25% of all cases. In 2012, it resulted in 1.68 million cases and 5, 22,000 deaths. It is more common in developed countries and is more than 100 times more common in women than in men. Aims: To correlate cytological diagnosis with histopathological diagnosis of breast lesions, accuracy of FNAC in diagnosing breast lesions and the cytomorphology of various breast lesions of patients. Materials and Methods: The present study was a descriptive Cross Sectional study. This Study was conducted from March 2021 to November 2022, department of pathology at Darbhanga Medical College. Total 100 patients were included in this study. Result: On histopathology, Fibroadenoma was diagnosed in 54 cases, 20 cases were of infiltrating ductal carcinoma, 10 cases were Fibroadenoma with Fibrocystic change and 8 cases were fibrocystic change. There was one case of Borderline phyllodes, Adenosis with myoepithelial hyperplasia, Ductal Carcinoma insitu, Mucinous Carcinoma. Conclusion: Benign neoplasms of the breast are more common than malignant one. The present study shows FNAC correlation with histopathology with high specificity and maximum positive predictive value. However, FNAC can be used as an indicative diagnosis (one stop) for breast cancer examination in outpatient setting. Accuracy of FNAC enables to proceed with surgery or not. It bridges the gap between clinical evaluation and final surgical pathological diagnosis in majority of cases. It enables the clinician to obtain a diagnosis in high percentage of cases with minimal expenditure of time, amd money and often to avoid unnecessary surgery. [ABSTRACT FROM AUTHOR]

Breast ultrasound (BUS) imaging has become one of the key imaging modalities for medical image diagnosis and prognosis. However, the manual process of lesion delineation from ultrasound images can incur various challenges concerning variable shape, size, intensity, curvature, or other medical priors of the lesion in the image. Therefore, computer-aided diagnostic (CADx) techniques incorporating deep learning–based neural networks are automatically used to segment the lesion from BUS images. This paper proposes an encoder-decoder-based architecture to recognize and accurately segment the lesion from two-dimensional BUS images. The architecture is utilized with the residual connection in both encoder and decoder paths; bi-directional ConvLSTM (BConvLSTM) units in the decoder extract the minute and detailed region of interest (ROI) information. BConvLSTM units and residual blocks help the network weigh ROI information more than the similar background region. Two public BUS image datasets, one with 163 images and the other with 42 images, are used. The proposed model is trained with the augmented images (ten forms) of dataset one (with 163 images), and test results are produced on the second dataset and the testing set of the first dataset—the segmentation performance yielding comparable results with the state-of-the-art segmentation methodologies. Similarly, the visual results show that the proposed approach for BUS image segmentation can accurately identify lesion contours and can potentially be applied for similar and larger datasets. [ABSTRACT FROM AUTHOR]

Key Clinical Message SAN should be considered in the setting of nipple discharge or morphology changes with typical histological findings. There are limited published cases of SAN, and workup of this pathology is still not clear to date. Abstract Syringomatous adenoma of the nipple (SAN) is known to be a rare benign breast neoplasm. With a few cases documented in the literature, preoperatively diagnosing this tumor is a challenge, which often leads to invasive procedure such as mass excision with nipple removal. This study was aimed at presenting a case report of SAN and to conduct a review of published cases. Literature search was conducted through PubMed databases. Articles published from year 1983 to March of 2022 were included. Only histologically confirmed cases of SAN were included. The review was performed according to the PRISMA guidelines. Twenty‐eight cases, including the newly reported case, were included in the review after going through inclusion criteria. The mean age at diagnosis was 44 ± 16 years. 7% were male. The most common presentation was palpable mass. Preoperative biopsy was done for 9 cases, out of which 7 did not indicate typical histopathological characteristic of SAN. Most common treatment was wide local excision with nipple removal. Immunohistochemical staining of the resected tumor was performed in 16 cases postoperatively. 32.1% (9/28) utilized p63 in constellation with histologic findings. Five cases that utilized staining also used Estrogen Receptor (ER) marker, while three used progesterone receptor (PR) marker. SAN should be considered in the setting of nipple discharge or morphology changes with typical histological findings. There are limited published cases of SAN, and workup of this pathology is still not clear to date. The case presented here and our comprehensive literature review suggest that pathohistological findings of SAN can be heterogeneous. Clinicians would also benefit from recognizing these variances. Further research and reported cases are needed to confidently diagnose SAN, which may open doors for less aggressive surgical treatment or surveillance option for asymptomatic patients.

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal 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.)

Background: Ultrasound-detected breast lesions with probably benign features are a great challenge for clinicians, especially in breasts with dense composition. We aimed to investigate the finding of two radiologic modalities on these lesions. Methods: This retrospective cross-sectional study recruited patients including (1) candidates of assisted reproductive therapy (ART), (2) patients with prior high-risk lesions, and (3) the “suspected” BIRADS-3 masses referring to masses that US BIRADS-3 was not compatible with the clinical breast exam. The degree of agreement in diagnosing BIRADS-3 lesions between two modalities of magnetic resonance imaging (MRI) and ultrasonography (US), and comparison of the lesions in US and MRI were the study variables. Results: A total number of 123 lesions in 67 patients with a median age of 38 (IQR: 11, range: 17–67). In the examination by MRI, 107 (87.0 %) lesions were BIRADS-3 indicating the agreement level between these two modalities. The median size of the lesions in US was 9 mm (IQR: 5, range: 3–43) and 9 mm (IQR: 10, range: 4–46) in MRI. The measured size of the lesions between the two modalities was highly correlated (Spearman correlation coefficient: 0.889, P-value

DR-70 test measures both "Fibrin" and "Fibrinogen degradation products (FDP)" in human serum samples, which created by the organism to limit cancer cells. The aim of the study was to investigate the diagnostic value of serum DR-70 levels in breast cancer and to define the cut-off value of the serum DR-70 with a high diagnostic sensitivity and specificity. This study was conducted as a prospective study in a tertiary-care training and research hospital. One hundred twenty-five patients with benign or malignant breast mass and 33 healthy individuals with no breast lesion on breast imaging methods were included in the study. The patients were divided into three groups: "Breast cancer (BC)," "Benign breast disease (BBD)," and "Normal controls (NC)." Demographical data, breast imaging findings, histopathological data, carcinoembryonic antigen (CEA), cancer antigen 125 (CA 125), cancer antigen 15–3 (CA 15–3), and DR-70 levels were evaluated. Diagnostic cut-off value was calculated for serum DR-70 levels by ROC (receiver operating characteristic) analysis. Serum DR-70 levels were significantly higher in BC than other groups (p < 0.001). ROC analysis considered the cut-off value of serum DR-70 0.86 μg/ml; the sensitivity, specificity, positive predictive value, and negative predictive value were 43.6%, 90.9%, 85%, and 57.7%, respectively. Serum DR-70 level has higher specificity and positive predictive value than other biomarkers for breast cancer. It is thought to be a suitable diagnostic biomarker for breast cancer. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore whether intravoxel incoherent motion (IVIM) combined with a dynamic contrast–enhanced magnetic resonance imaging (DCE–MRI) quantitative model can improve the ability to distinguish between benign and malignant BI-RADS 4 breast lesions. We enrolled 100 patients who underwent breast MRI at our institution and extracted the quantitative parameters of lesions with a post-processing workstation. Statistical differences in these parameters between benign and malignant BI-RADS 4 lesions were assessed using a two independent samples t-test or a Mann–Whitney U test. Binary logistic regression analysis was performed to establish five diagnostic models (model_ADC, model_IVIM, model_DCE, model_DCE+ADC, and model_DCE+IVIM). Receiver operating characteristic (ROC) curves, leave-one-out cross-validation, and the Delong test were used to assess and compare the diagnostic performance of these models. The model_DCE+IVIM showed the highest area under the curve (AUC) of 0.903 (95% confidence interval (CI): 0.828–0.953, sensitivity: 87.50%, specificity: 85.00%), which was significantly higher than that of model_ADC (p = 0.014) and model_IVIM (p = 0.033). The model_ADC had the lowest diagnostic performance (AUC = 0.768, 95%CI: 0.672–0.846) but was not significantly different from model_IVIM (p = 0.168). The united quantitative model with DCE–MRI and IVIM could improve the ability to evaluate the malignancy in BI-RADS 4 lesions, and unnecessary breast biopsies may be obviated. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cause of cancer death in women. Early screening and treatment can effectively improve the success rate of treatment. Ultrasound imaging technology, as the preferred modality for breast cancer screening, provides an essential reference for early diagnosis. Existing computer-aided ultrasound imaging diagnostic techniques mainly rely on the selected key frames for breast cancer lesion diagnosis. In this paper, we first collected and annotated a dataset of ultrasound video sequences of 268 cases of breast lesions. Moreover, we propose a contrastive learning-guided multimeta attention network (CLMAN) by combining a deformed feature extraction module and a multi-meta attention module to address breast lesion diagnosis in ultrasound sequence. The proposed feature extraction module can autonomously acquire key information of the feature map in the spatial dimension, whereas the designed multi-meta attention module is dedicated to effective information aggregation in the temporal dimension. In addition, we utilize a contrast learning strategy to alleviate the problem of high imaging variability within ultrasound lesion videos. The experimental results on our collected dataset show that our CLMAN significantly outperforms existing advanced methods for video classification. [ABSTRACT FROM AUTHOR]