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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: 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]

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: 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]

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]

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]

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]

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.

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

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]

ObjectiveTo investigate whether there is added value of quantitative parameters from synthetic magnetic resonance imaging (SyMRI) as a complement to the Kaiser score (KS) to differentiate benign and malignant breast lesions.Materials and methodsIn this single-institution study, 122 patients who underwent breast MRI from March 2020 to May 2021 were retrospectively analyzed. SyMRI and dynamic contrast-enhanced MRI were performed using a 3.0-T system. Two experienced radiologists independently assigned the KS and measured the quantitative values of T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) from SyMRI. Pathology was regarded as the gold standard. The diagnostic values were compared using the appropriate statistical tests.ResultsThere were 122 lesions (86 malignant and 36 benign) in 122 women. The T1 value was identified as the only independent factor for the differentiation of malignant and benign lesions. The diagnostic accuracy of incorporating the T1 into the KS protocol (T1+KS) was 95.1% and 92.1% for all lesions (ALL) and The American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions, respectively, which was significantly higher than that of either T1 (ALL: 82.8%, P = 0.0001; BI-RADS 4: 78.9%, P = 0.002) or KS (ALL: 90.2%, P = 0.031; BI-RADS 4: 84.2%, P = 0.031) alone. The sensitivity and specificity of T1+KS were also higher than those of the T1 or KS alone. The combined diagnosis could have avoided another 15.6% biopsies compared with using KS alone.ConclusionsIncorporating T1 into the KS protocol improved both the sensitivity and specificity to differentiate benign and malignant breast lesions, thus avoiding unnecessary invasive procedures.

Objective: To evaluate the effectiveness and advantages of a new method for calculating breast tumor volume based on an automated breast ultrasound system (ABUS). Methods: A total of 42 patients (18-70 years old) with breast lesions were selected for this study. The Ivenia ABUS 2.0 (General Electric Company, USA) was used, with a probe frequency of 6-15 MHz. Adobe Photoshop CS6 software was used to calculate the pixel ratio of each ABUS image, and to draw an outline of the tumor cross-section. The resulting area (in pixels) was multiplied by the pixel ratio to yield the area of the tumor cross-section. The Wilcoxon signed rank test and Bland-Altman plot were used to compare mean differences and mean values, respectively, between the two methods. Results: There was no significant difference between the tumor volumes calculated by pixel method as compared to the traditional method (P>0.05). Repeated measurements of the same tumor volume were more consistent with the pixel method. Conclusion: The new pixel method is feasible for measuring breast tumor volume and has good validity and measurement stability. [ABSTRACT FROM AUTHOR]

Introduction: Breast lesions are the leading cause of morbidity and mortality in women. Fine needle aspiration cytology (FNAC) of Breast is a significant component of the "Triple approach" for pre-operative diagnosis of breast lumps. This study is undertaken to explore the utility and accuracy of FNAC in various breast pathologies. Aim: This study aims to classify various breast lesions according to the International Academy of Cytology (IAC) grading system and grade breast carcinoma on FNAC using Robinson's grading system which is then correlated with histopathology and also to evaluate the diagnostic accuracy, sensitivity, and specificity of FNAC in diagnosing breast lesions. Materials and methods: The study was conducted on patients with breast lump. This study was conducted by retrieving FNAC smears and were grouped under five standardized categories proposed by the IAC: Category I (Insufficient material), Category II (Benign), Category III (Atypical), Category IV (Suspicious) & Category V (Malignant) respectively. C5 cases were graded by Robinson's grading system which was then compared with the histopathological Elston Ellis modified Bloom-Richardson grading system. Results: Category I, II, III, IV, and V accounted for 10 (12.5%), 43 (53.75%), 3 (3.75%), 5 (6.25%), and 19 (23.75%) of the 80 breast lesions reported on FNAC, respectively. Histopathology correlations were available in 59 cases. The sensitivity, specificity, positive and negative predictive value, and diagnostic accuracy were 80%, 100%, 100%, 92.10%, and 94% respectively. Conclusion: Cytological characterization based on IAC structured reporting will enhance diagnostic clarity and comparison with histopathology establishes the diagnostic accuracy of FNAC. [ABSTRACT FROM AUTHOR]

Objectives: The probability of Breast Imaging Reporting and Data Systems (BI-RADS) 4 lesions being malignant is 2%–95%, which shows the difficulty to make a diagnosis. Radiomics models based on magnetic resonance imaging (MRI) can replace clinicopathological diagnosis with high performance. In the present study, we developed and tested a radiomics model based on MRI images that can predict the malignancy of BI-RADS 4 breast lesions. Methods: We retrospective enrolled a total of 216 BI-RADS 4 patients MRI and clinical information. We extracted 3,474 radiomics features from dynamic contrast-enhanced (DCE), T2-weighted images (T2WI), and diffusion-weighted imaging (DWI) MRI images. Least absolute shrinkage and selection operator (LASSO) and logistic regression were used to select features and build radiomics models based on different sequence combinations. We built eight radiomics models which were based on DCE, DWI, T2WI, DCE+DWI, DCE+T2WI, DWI+T2WI, and DCE+DWI+T2WI and a clinical predictive model built based on the visual assessment of radiologists. A nomogram was constructed with the best radiomics signature combined with patient characteristics. The calibration curves for the radiomics signature and nomogram were conducted, combined with the Hosmer-Lemeshow test. Results: Pearson's correlation was used to eliminate 3,329 irrelevant features, and then LASSO and logistic regression were used to screen the remaining feature coefficients for each model we built. Finally, 12 related features were obtained in the model which had the best performance. These 12 features were used to build a radiomics model in combination with the actual clinical diagnosis of benign or malignant lesion labels we have obtained. The best model built by 12 features from the 3 sequences has an AUC value of 0.939 (95% CI, 0.884-0.994) and an accuracy of 0.931 in the testing cohort. The sensitivity, specificity, precision and Matthews correlation coefficient (MCC) of testing cohort are 0.932, 0.923, 0.982, and 0.791, respectively. The nomogram has also been verified to have calibration curves with good overlap. Conclusions: Radiomics is beneficial in the malignancy prediction of BI-RADS 4 breast lesions. The radiomics predictive model built by the combination of DCE, DWI, and T2WI sequences has great application potential. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The sonographic appearance of benign and malignant breast nodules overlaps to some extent, and we aimed to assess the performance of the Gail model as an adjunctive tool to ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) for predicting the malignancy of nodules.Materials and Methods: From 2018 to 2019, 2607 patients were prospectively enrolled by 35 health care facilities. An individual breast cancer risk was assessed by the Gail model. Based on B-mode US, color Doppler, and elastography, all nodules were evaluated according to the fifth edition of BI-RADS, and these nodules were all confirmed later by pathology.Results: We demonstrated that the Gail model, age, tumor size, tumor shape, growth orientation, margin, contour, acoustic shadowing, microcalcification, presence of duct ectasia, presence of architectural distortion, color Doppler flow, BI-RADS, and elastography score were significantly related to breast cancer (all p < 0.001). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve (AUC) for combining the Gail model with the BI-RADS category were 95.6%, 91.3%, 85.0%, 97.6%, 92.8%, and 0.98, respectively. Combining the Gail model with the BI-RADS showed better diagnostic efficiency than the BI-RADS and Gail model alone (AUC 0.98 vs 0.80, p < 0.001; AUC 0.98 vs 0.55, p < 0.001) and demonstrated a higher specificity than the BI-RADS (91.3% vs 59.4%, p < 0.001).Conclusion: The Gail model could be used to differentiate malignant and benign breast lesions. Combined with the BI-RADS category, the Gail model was adjunctive to US for predicting breast lesions for malignancy. For the diagnosis of malignancy, more attention should be paid to high-risk patients with breast lesions. [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]

Background: Breast cancer is the second cause of death among women. Ultrasound (US) imaging is the most common technique for diagnosing breast cancer; however, detecting breast lesions in US images is a difficult task, mainly, because it provides low-quality images. Consequently, identifying lesions in US images is still a challenging task and an open problem in US image processing. This study aims to develop an automated system for the identification of lesions in US images Method: We proposed an automatic method to assist radiologists in inspecting and analyzing US images in breast screening and diagnosing breast cancer. In contrast to previous research, this work focuses on fusing information extracted from different frames. The developed method consists of template matching, morphological features extraction, local binary patterns, fuzzy C-means clustering, region growing, and information fusion-based image segmentation technique. The performance of the system was evaluated using a database composed of 22 US videos where 10 breast US films were obtained from patients with breast lesions and 12 videos belonged to normal cases. Results: The sensitivity, specificity, and accuracy of the system in detecting frames with breast lesions were 95.7%, 97.1%, and 97.1%, respectively. The algorithm reduced the vibration of the physician’s hands’ while probing by assessing every 10 frames regardless of the results of the prior frame; hence, lowering the possibility of missing a lesion during an examination. Conclusion: The presented system outperforms several existing methods in correctly detecting breast lesions in a breast cancer screening test. Fusing information that exists in frames of a breast US film can help improve the identification of lesions (suspect regions) in a screening test.

ObjectivesThe probability of Breast Imaging Reporting and Data Systems (BI-RADS) 4 lesions being malignant is 2%–95%, which shows the difficulty to make a diagnosis. Radiomics models based on magnetic resonance imaging (MRI) can replace clinicopathological diagnosis with high performance. In the present study, we developed and tested a radiomics model based on MRI images that can predict the malignancy of BI-RADS 4 breast lesions.MethodsWe retrospective enrolled a total of 216 BI-RADS 4 patients MRI and clinical information. We extracted 3,474 radiomics features from dynamic contrast-enhanced (DCE), T2-weighted images (T2WI), and diffusion-weighted imaging (DWI) MRI images. Least absolute shrinkage and selection operator (LASSO) and logistic regression were used to select features and build radiomics models based on different sequence combinations. We built eight radiomics models which were based on DCE, DWI, T2WI, DCE+DWI, DCE+T2WI, DWI+T2WI, and DCE+DWI+T2WI and a clinical predictive model built based on the visual assessment of radiologists. A nomogram was constructed with the best radiomics signature combined with patient characteristics. The calibration curves for the radiomics signature and nomogram were conducted, combined with the Hosmer-Lemeshow test.ResultsPearson’s correlation was used to eliminate 3,329 irrelevant features, and then LASSO and logistic regression were used to screen the remaining feature coefficients for each model we built. Finally, 12 related features were obtained in the model which had the best performance. These 12 features were used to build a radiomics model in combination with the actual clinical diagnosis of benign or malignant lesion labels we have obtained. The best model built by 12 features from the 3 sequences has an AUC value of 0.939 (95% CI, 0.884-0.994) and an accuracy of 0.931 in the testing cohort. The sensitivity, specificity, precision and Matthews correlation coefficient (MCC) of testing cohort are 0.932, 0.923, 0.982, and 0.791, respectively. The nomogram has also been verified to have calibration curves with good overlap.ConclusionsRadiomics is beneficial in the malignancy prediction of BI-RADS 4 breast lesions. The radiomics predictive model built by the combination of DCE, DWI, and T2WI sequences has great application potential.

Introduction  Diffusion-weighted imaging (DWI) is a promising magnetic resonance imaging (MRI) technique for differentiating between benign and malignant breast lesions. This study set out to assess the diagnostic utility of DWI and apparent diffusion coefficient (ADC) values in the characterization of breast lesions. Materials and methods A retrospective analysis comprised 30 patients with breast lesions who had breast MRI with DWI. The histopathological findings, ADC readings, and conventional MRI features were all analyzed. The receiver operating characteristic (ROC) curve analysis method was utilized to assess the diagnostic accuracy of DWI. Results Out of the 30 lesions, 22 (73.3%) were benign and eight (26.7%) were malignant. Malignant lesions exhibited significantly lower ADC values (p < 0.001) compared to benign lesions. An ADC cutoff value of 1.1 × 10 -3  mm 2 /s was optimal for differentiating benign from malignant lesions, yielding 90.81% sensitivity, 91.51% specificity, and 91.5% accuracy. Conclusion Combining DWI with quantitative ADC analysis is a helpful, non-invasive method for the characterization of breast lesions. It shows excellent diagnostic accuracy in identifying benign and malignant lesions, which may cut down on pointless biopsies and help with patient management., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Jerosha et al.)

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., Competing Interests: ZL is an employee of Fuzhou Bochuang Medical Laboratory Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest., (Copyright © 2024 Huang, Zhao, Chen, Sun, Lin and Yang.)

Background: Differential diagnosis between benign and malignant breast lesions is of crucial importance relating to follow-up treatment. Recent development in texture analysis and machine learning may lead to a new solution to this problem. Method: This current study enrolled a total number of 265 patients (benign breast lesions:malignant breast lesions = 71:194) diagnosed in our hospital and received magnetic resonance imaging between January 2014 and August 2017. Patients were randomly divided into the training group and validation group (4:1), and two radiologists extracted their texture features from the contrast-enhanced T1-weighted images. We performed five different feature selection methods including Distance correlation, Gradient Boosting Decision Tree (GBDT), least absolute shrinkage and selection operator (LASSO), random forest (RF), eXtreme gradient boosting (Xgboost) and five independent classification models were built based on Linear discriminant analysis (LDA) algorithm. Results: All five models showed promising results to discriminate malignant breast lesions from benign breast lesions, and the areas under the curve (AUCs) of receiver operating characteristic (ROC) were all above 0.830 in both training and validation groups. The model with a better discriminating ability was the combination of LDA + gradient boosting decision tree (GBDT). The sensitivity, specificity, AUC, and accuracy in the training group were 0.814, 0.883, 0.922, and 0.868, respectively; LDA + random forest (RF) also suggests promising results with the AUC of 0.906 in the training group. Conclusion: The evidence of this study, while preliminary, suggested that a combination of MRI texture analysis and LDA algorithm could discriminate benign breast lesions from malignant breast lesions. Further multicenter researches in this field would be of great help in the validation of the result. [ABSTRACT FROM AUTHOR]

BackgroundDifferential diagnosis between benign and malignant breast lesions is of crucial importance relating to follow-up treatment. Recent development in texture analysis and machine learning may lead to a new solution to this problem.MethodThis current study enrolled a total number of 265 patients (benign breast lesions:malignant breast lesions = 71:194) diagnosed in our hospital and received magnetic resonance imaging between January 2014 and August 2017. Patients were randomly divided into the training group and validation group (4:1), and two radiologists extracted their texture features from the contrast-enhanced T1-weighted images. We performed five different feature selection methods including Distance correlation, Gradient Boosting Decision Tree (GBDT), least absolute shrinkage and selection operator (LASSO), random forest (RF), eXtreme gradient boosting (Xgboost) and five independent classification models were built based on Linear discriminant analysis (LDA) algorithm.ResultsAll five models showed promising results to discriminate malignant breast lesions from benign breast lesions, and the areas under the curve (AUCs) of receiver operating characteristic (ROC) were all above 0.830 in both training and validation groups. The model with a better discriminating ability was the combination of LDA + gradient boosting decision tree (GBDT). The sensitivity, specificity, AUC, and accuracy in the training group were 0.814, 0.883, 0.922, and 0.868, respectively; LDA + random forest (RF) also suggests promising results with the AUC of 0.906 in the training group.ConclusionThe evidence of this study, while preliminary, suggested that a combination of MRI texture analysis and LDA algorithm could discriminate benign breast lesions from malignant breast lesions. Further multicenter researches in this field would be of great help in the validation of the result.

Abstract Background Outlining lesion contours in Ultra Sound (US) breast images is an important step in breast cancer diagnosis. Malignant lesions infiltrate the surrounding tissue, generating irregular contours, with spiculation and angulated margins, whereas benign lesions produce contours with a smooth outline and elliptical shape. In breast imaging, the majority of the existing publications in the literature focus on using Convolutional Neural Networks (CNNs) for segmentation and classification of lesions in mammographic images. In this study our main objective is to assess the ability of CNNs in detecting contour irregularities in breast lesions in US images. Methods In this study we compare the performance of two CNNs with Direct Acyclic Graph (DAG) architecture and one CNN with a series architecture for breast lesion segmentation in US images. DAG and series architectures are both feedforward networks. The difference is that a DAG architecture could have more than one path between the first layer and end layer, whereas a series architecture has only one path from the beginning layer to the end layer. The CNN architectures were evaluated with two datasets. Results With the more complex DAG architecture, the following mean values were obtained for the metrics used to evaluate the segmented contours: global accuracy: 0.956; IOU: 0.876; F measure: 68.77%; Dice coefficient: 0.892. Conclusion The CNN DAG architecture shows the best metric values used for quantitatively evaluating the segmented contours compared with the gold-standard contours. The segmented contours obtained with this architecture also have more details and irregularities, like the gold-standard contours.

Background and purpose: Breast cancer is the common cancer in women and the major cause of death due to malignancy in women aged 40-44 years old. The routine method for diagnosis of biopsy specimens is pathologic procedure. Expert pathologists could accurately diagnose malignant and benign diseases using cytology methods. The aim of this study was to investigate the sensitivity and specificity of touch preparation cytology compared with pathology in diagnosis of breast lesions. Materials and methods: In this descriptive cross-sectional study we evaluated patients that had breast operation in Hamadan Bouali Hospital, 2016-2017 by analyzing pathologic recorded reports. Results: Totally 121 patients were studied including 114 (94.2%) females and 7 (5.8%) males. The mean age of the patients was 40.7 years (15-74 years). Malignant and benign diseases were observed in 61 and 60 patients, respectively. Sensitivity and specificity of touch preparation cytology were 91.8% and 95%, respectively. Positive predictive value and negative predictive value were 94.91% and 91.93%, respectively. Conclusion: Touch preparation cytology is an accurate and valid method for detecting benign breast lesions and is suggested to be used alongside frozen section to increase sensitivity and specificity of diagnosis.

To explore the value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusional kurtosis imaging (DKI) based on diffusion weighted magnetic resonance imaging (DW-MRI) in differentiating benign and malignant breast lesions. A total of 215 patients with breast lesions were prospectively collected for breast MR examination. Single exponential, IVIM, and DKI models were calculated using a series of b values. Parameters including ADC, perfusion fraction (f), tissue diffusion coefficient (D), perfusion-related incoherent microcirculation (D*), average kurtosis (MK), and average diffusivity (MD) were compared between benign and malignant lesions. ROC curves were used to analyze the optimal diagnostic threshold of each parameter, and to evaluate the diagnostic efficacy of single and combined parameters. ADC, D, MK, and MD values were significantly different between benign and malignant breast lesions (P

To explore the value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusional kurtosis imaging (DKI) based on diffusion weighted magnetic resonance imaging (DW-MRI) in differentiating benign and malignant breast lesions. A total of 215 patients with breast lesions were prospectively collected for breast MR examination. Single exponential, IVIM, and DKI models were calculated using a series of b values. Parameters including ADC, perfusion fraction (f), tissue diffusion coefficient (D), perfusion-related incoherent microcirculation (D*), average kurtosis (MK), and average diffusivity (MD) were compared between benign and malignant lesions. ROC curves were used to analyze the optimal diagnostic threshold of each parameter, and to evaluate the diagnostic efficacy of single and combined parameters. ADC, D , MK, and MD values were significantly different between benign and malignant breast lesions (P<0.001). Among the single parameters, ADC had the highest diagnostic efficiency (sensitivity 91.45%, specificity 82.54%, accuracy 88.84%, AUC 0.915) and the best diagnostic threshold (0.983 μm2/ms). The combination of ADC and MK offered high diagnostic performance (sensitivity 90.79%, specificity 85.71%, accuracy 89.30%, AUC 0.923), but no statistically significant difference in diagnostic performance as compared with single-parameter ADC (P=0.268). The ADC, D , MK, and MD parameters have high diagnostic value in differentiating benign and malignant breast lesions, and of these individual parameters the ADC has the best diagnostic performance. Therefore, our study revealed that the use of ADC alone should be useful for differentiating between benign and malignant breast lesions, whereas the combination of MK and ADC might improve the diagnostic performance to some extent. [ABSTRACT FROM AUTHOR]

Pleomorphic adenoma of the breast is a rare, salivary-type tumor which can be misdiagnosed as invasive ductal carcinoma due to the morphologic and immunophenotypic overlap between these two entities. We report a pleomorphic adenoma of the breast initially misdiagnosed as estrogen receptor (ER)-positive low-grade invasive ductal carcinoma, a classification error which prompted genomic risk stratification with Oncotype Dx testing. This produced a high-risk recurrence score suggesting possible chemotherapeutic benefit. The patient was therefore scheduled to receive adjuvant chemotherapy following lumpectomy. However, fortunately this plan was halted when the final diagnosis of pleomorphic adenoma was rendered on the resection specimen. This is the only reported case of genomic risk stratification being performed on a salivary-type breast tumor in the literature, and it demonstrates the clinical implications of this misdiagnosis.

Abstract Background Ultrasound shear-wave elastography (SWE) may increase specificity of breast lesion assessment with ultrasound, but elasticity measurements may change with transducer orientation, defined as anisotropy. In this study, we aimed to observe the anisotropy of SWE of breast lesions, and its correlation with clinical and histopathological findings. Methods This retrospective study was approved by institutional review board. From June 2014 to June 2015, a total of 276 women (mean age, 48.75 ± 12.12 years) with 276 breast lesions (174 malignant, 102 benign) were enrolled for conventional ultrasound and SWE before surgical excision. Elasticity modulus in the longest diameter and orthogonal diameter were recorded, including maximum elasticity (Emax), mean elasticity (Emean), standard deviation (Esd) and ratio between mean elasticity of lesion and normal fatty tissue (Eratio). Anisotropy coefficients including anisotropic difference (AD) and anisotropy factors (AF) were calculated, and correlations with malignancy, tumor size, palpability, movability, lesion location and histopathology were analyzed. Results The average Emax, Emean, Esd and Eratio of the longest diameter were significantly higher than orthogonal diameter (P

Background: Breast cancer is the second cause of death among women. Ultrasound (US) imaging is the most common technique for diagnosing breast cancer; however, detecting breast lesions in US images is a difficult task, mainly, because it provides low-quality images. Consequently, identifying lesions in US images is still a challenging task and an open problem in US image processing. This study aims to develop an automated system for the identification of lesions in US images Method: We proposed an automatic method to assist radiologists in inspecting and analyzing US images in breast screening and diagnosing breast cancer. In contrast to previous research, this work focuses on fusing information extracted from different frames. The developed method consists of template matching, morphological features extraction, local binary patterns, fuzzy C-means clustering, region growing, and information fusion-based image segmentation technique. The performance of the system was evaluated using a database composed of 22 US videos where 10 breast US films were obtained from patients with breast lesions and 12 videos belonged to normal cases. Results: The sensitivity, specificity, and accuracy of the system in detecting frames with breast lesions were 95.7%, 97.1%, and 97.1%, respectively. The algorithm reduced the vibration of the physician's hands' while probing by assessing every 10 frames regardless of the results of the prior frame; hence, lowering the possibility of missing a lesion during an examination. Conclusion: The presented system outperforms several existing methods in correctly detecting breast lesions in a breast cancer screening test. Fusing information that exists in frames of a breast US film can help improve the identification of lesions (suspect regions) in a screening test. [ABSTRACT FROM AUTHOR]

Background: Outlining lesion contours in Ultra Sound (US) breast images is an important step in breast cancer diagnosis. Malignant lesions infiltrate the surrounding tissue, generating irregular contours, with spiculation and angulated margins, whereas benign lesions produce contours with a smooth outline and elliptical shape. In breast imaging, the majority of the existing publications in the literature focus on using Convolutional Neural Networks (CNNs) for segmentation and classification of lesions in mammographic images. In this study our main objective is to assess the ability of CNNs in detecting contour irregularities in breast lesions in US images. Methods: In this study we compare the performance of two CNNs with Direct Acyclic Graph (DAG) architecture and one CNN with a series architecture for breast lesion segmentation in US images. DAG and series architectures are both feedforward networks. The difference is that a DAG architecture could have more than one path between the first layer and end layer, whereas a series architecture has only one path from the beginning layer to the end layer. The CNN architectures were evaluated with two datasets. Results: With the more complex DAG architecture, the following mean values were obtained for the metrics used to evaluate the segmented contours: global accuracy: 0.956; IOU: 0.876; F measure: 68.77%; Dice coefficient: 0.892. Conclusion: The CNN DAG architecture shows the best metric values used for quantitatively evaluating the segmented contours compared with the gold-standard contours. The segmented contours obtained with this architecture also have more details and irregularities, like the gold-standard contours. [ABSTRACT FROM AUTHOR]

Purpose: It is known that, besides its benefits, ultrasound (US) elastography may show discordance findings, leading to biopsy, so this technique has some limitations. The purpose of the present study was to compare histopathological results and shear wave velocity (SWV) values in patients, to reveal discordance findings, and to investigate its statistical significance in making the decision for biopsy with SWV values. Material and methods: A total of 190 patients presenting with breast lesions were included in the study. The mean age of the patients was 39.87 ± 13.56 years. All morphological features of lesions were evaluated by B-mode US. The mean SWV values were measured using the region of interest (ROI) through all lesions with ARFI elastography. An ultrasound-guided Tru-Cut needle biopsy was performed, and histopathologic data were obtained. Results: The SWV values of false positive benign lesions, such as: granulomatous mastitis, sclerosing adenosis, chronic inflammation, fat necrosis, fibrotic breast tissue, and scar tissue, were as high as the SWV values of malignant lesions. Sensitivity was found as 91.6% and specificity as 40.9% for SWV values in ARFI US. Conclusions: Our results showed that breast lesions showed false positivity (59.1%) and false negativity (8.3%) in ARFI US. Elastography is useful and sensitive in the evaluation of breast lesions, but some benign lesions may mimic malignant lesions; thus, this information should not be underestimated in biopsy decision-making for elastography users. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) has been a prevalence technique for breast cancer diagnosis. Computer-aided detection and segmentation of lesions from MRIs plays a vital role for the MRI-based disease analysis. There are two main issues of the existing breast lesion segmentation techniques: requiring manual delineation of Regions of Interests (ROIs) as a step of initialization; and requiring a large amount of labeled images for model construction or parameter learning, while in real clinical or experimental settings, it is highly challenging to get sufficient labeled MRIs. To resolve these issues, this work proposes a semi-supervised method for breast tumor segmentation based on super voxel strategies. After image segmentation with advanced cluster techniques, we take a supervised learning step to classify the tumor and nontumor patches in order to automatically locate the tumor regions in an MRI. To obtain the optimal performance of tumor extraction, we take extensive experiments to learn parameters for tumor segmentation and classification, and design 225 classifiers corresponding to different parameter settings. We call the proposed method as Semi-supervised Tumor Segmentation (SSTS), and apply it to both mass and nonmass lesions. Experimental results show better performance of SSTS compared with five state-of-the-art methods.