Breast cancer diagnosis from histopathology images is often time consuming and prone to human error, impacting treatment and prognosis. Deep learning diagnostic methods offer the potential for improved accuracy and efficiency in breast cancer detection and classification. However, they struggle with limited data and subtle variations within and between cancer types. Attention mechanisms provide feature refinement capabilities that have shown promise in overcoming such challenges. To this end, this paper proposes the Efficient Channel Spatial Attention Network (ECSAnet), an architecture built on EfficientNetV2 and augmented with a convolutional block attention module (CBAM) and additional fully connected layers. ECSAnet was fine-tuned using the BreakHis dataset, employing Reinhard stain normalization and image augmentation techniques to minimize overfitting and enhance generalizability. In testing, ECSAnet outperformed AlexNet, DenseNet121, EfficientNetV2-S, InceptionNetV3, ResNet50, and VGG16 in most settings, achieving accuracies of 94.2% at 40×, 92.96% at 100×, 88.41% at 200×, and 89.42% at 400× magnifications. The results highlight the effectiveness of CBAM in improving classification accuracy and the importance of stain normalization for generalizability.

Syringomatous tumor of the nipple is a benign, locally infiltrative tumor. There are reports in the literature of tumor recurrence in cases of incomplete excision. Clinical and mammographic findings in syringomatous tumors are like those of breast carcinoma and the pathologist has a fundamental role in final tumor diagnosis. Therefore, the aim of this study was to report a case of syringoma located in the areolar region. A 33-year-old woman reported that she had noticed a nodule in her left areolar region 4 years previously (February 2019). A breast ultrasound was performed, detecting intraparenchymatous breast cysts. Surgical resection of the nodule was indicated although it was not performed. Two years later, in August 2021, the patient underwent a mastopexy with prosthesis inclusion. Histopathology study of the surgical specimen revealed a syringomatous tumor with positive margins. Thirteen (13) months after diagnosis (September 3, 2021 - October 16, 2022), the patient is doing well and receives clinical follow-up.

UNASSIGNED: Phyllodes tumor is a rare fibroepithelial neoplasm of the breast, which is classified histologically as benign, borderline, or malignant. Accurate preoperative diagnosis allows the correct surgical planning and reoperation avoidance.
UNASSIGNED: To describe the clinical presentation and radiologic features of phyllodes tumors and differentiate between benign and non-benign (borderline and malignant) groups.
UNASSIGNED: A retrospective study of 57 patients with a diagnosis of phyllodes tumor who had preoperative imaging (mammography, ultrasound, or CT chest) and histological confirmation. The data was collected from 1 June 2011 to 30 September 2021. The imaging features of the phyllodes tumors were described according to the 5th edition of the ACR BI-RADS lexicon. For comparing between two groups, the student t-test, Wilcoxon rank sum test, Chi-square test, and Fisher\'s exact test were used for statistical analyses. The logistic regression analysis was calculated for non-benign phyllodes tumor prediction.
UNASSIGNED: From 57 patients, the pathologic results were benign for 43 cases and non-benign phyllodes tumors for 14 cases. There was no differentiation of mammographic and CT features between benign and non-benign groups. Non-benign phyllodes tumors had the statistical significance of menopausal status, entire breast involvement, tumor size larger than 10 cm, and heterogeneous echo on univariable analysis. After multivariable analysis, menopausal status (odd ratios=13.79, p=0.04) and presence of vessels in the rim (odd ratios=16.51, p=0.019) or absent vascularity (odd ratios=8.45, p=0.047) on doppler ultrasound were significantly increased possibility of non-benign phyllodes tumor.
UNASSIGNED: Menopausal status and presence of vessels in the rim or absent vascularity on Doppler ultrasound were important predictors for the diagnosis of non-benign phyllodes tumor.

UNASSIGNED: The objective of this study was to develop a fully automatic mass segmentation method called AMS-U-Net for digital breast tomosynthesis (DBT), a popular breast cancer screening imaging modality. The aim was to address the challenges posed by the increasing number of slices in DBT, which leads to higher mass contouring workload and decreased treatment efficiency.
UNASSIGNED: The study used 50 slices from different DBT volumes for evaluation. The AMS-U-Net approach consisted of four stages: image pre-processing, AMS-U-Net training, image segmentation, and post-processing. The model performance was evaluated by calculating the true positive ratio (TPR), false positive ratio (FPR), F-score, intersection over union (IoU), and 95% Hausdorff distance (pixels) as they are appropriate for datasets with class imbalance.
UNASSIGNED: The model achieved 0.911, 0.003, 0.911, 0.900, 5.82 for TPR, FPR, F-score, IoU, and 95% Hausdorff distance, respectively.
UNASSIGNED: The AMS-U-Net model demonstrated impressive visual and quantitative results, achieving high accuracy in mass segmentation without the need for human interaction. This capability has the potential to significantly increase clinical efficiency and workflow in DBT for breast cancer screening.

BACKGROUND: Breast ultrasound (US) is useful for dense breasts, and the introduction of artificial intelligence (AI)-assisted diagnoses of breast US images should be considered. However, the implementation of AI-based technologies in clinical practice is problematic because of the costs of introducing such approaches to hospital information systems (HISs) and the security risk of connecting HIS to the Internet to access AI services. To solve these problems, we developed a system that applies AI to the analysis of breast US images captured using a smartphone.
METHODS: Training data were prepared using 115 images of benign lesions and 201 images of malignant lesions acquired at the Division of Breast Surgery, Gifu University Hospital. YOLOv3 (object detection models) was used to detect lesions on US images. A graphical user interface (GUI) was developed to predict an AI server. A smartphone application was also developed for capturing US images displayed on the HIS monitor with its camera and displaying the prediction results received from the AI server. The sensitivity and specificity of the prediction performed on the AI server and via the smartphone were calculated using 60 images spared from the training.
RESULTS: The established AI showed 100% sensitivity and 75% specificity for malignant lesions and took 0.2 s per prediction with the AI sever. Prediction using a smartphone required 2 s per prediction and showed 100% sensitivity and 97.5% specificity for malignant lesions.
CONCLUSIONS: Good-quality predictions were obtained using the AI server. Moreover, the quality of the prediction via the smartphone was slightly better than that on the AI server, which can be safely and inexpensively introduced into HISs.

UNASSIGNED: We explored the role of maximum intensity projection (MIP) based on high frame rate contrast-enhanced ultrasound (H-CEUS) for the differentiation of breast tumors.
UNASSIGNED: MIP imaging was performed in patients with breast tumors who underwent H-CEUS examinations. The microvasculature morphology of breast tumors was assessed. The receiver operating characteristic curve was plotted to evaluate the diagnostic performance of MIP.
UNASSIGNED: Forty-three breast tumors were finally analyzed, consisting of 19 benign and 24 malignant tumors. For the ≤30-s and >30-s phases, dot-, line-, or branch-like patterns were significantly more common in benign tumors. A tree-like pattern was only present in the benign tumors. A crab claw-like pattern was significantly more common in the malignant tumors. Among the tumors with crab claw-like patterns, three cases of malignant tumors had multiple parallel small spiculated vessels. There were significant differences in the microvasculature morphology for the ≤30-s and >30-s phases between the benign and malignant tumors (all p < 0.001). The area under the curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the ≤30-s phase were all higher than those of the >30-s phase for the classification of breast tumors.
UNASSIGNED: MIP based on H-CEUS can be used for the differentiation of breast tumors, and the ≤30-s phase had a better diagnostic value. Multiple parallel small spiculated vessels were a new finding, which could provide new insight for the subsequent study of breast tumors.

Introduction Phyllodes tumor (PT) is an uncommon fibroepithelial neoplasm of the breast. It is a biphasic tumor with stromal and epithelial components, with a tendency to recur. Because of its wide range of disease manifestations, it has been subclassified into three categories, i.e., benign, borderline, and malignant, based on several histological parameters. This study was conducted to evaluate the clinicopathological features associated with malignancy in breast PTs. Methods We conducted a retrospective study at the Department of Histopathology at Liaquat National Hospital, Karachi, Pakistan. A total of 146 biopsy-proven cases of PTs were enrolled in the study. Clinical data were obtained from the clinical referral forms. Specimens were obtained from either lumpectomy or simple mastectomy. The specimens obtained were received at the laboratory where after gross examination, paraffin-embedded tissue blocks were prepared, which were sectioned, stained, and studied by a senior histopathologist. Pathological features, such as mitotic count, necrosis, stromal atypia, stromal overgrowth, and heterologous elements, were observed. Based on these features, the PTs were classified into benign, borderline, and malignant tumors. Results The mean age of the PTs in our setup was 40.65 ± 12.17 years with a mean size of 9.40 ± 6.49 cm. Malignant PT was found to be the most prevalent in our population, accounting for 63 (43.2%) cases, followed by borderline (51, 34.9%) and benign (32, 21.9%). A significant association was found between the tumor subtype and patient age, i.e., patients diagnosed with malignant and borderline PTs were found to be of older age (mean 42.82 ± 12.94 and 42.05 ± 11.31 years, respectively) than those diagnosed with benign PTs (mean age 34.12 ± 9.75 years). Moreover, malignant PTs were associated with larger tumor size (mean 11.46 ± 6.08) compared with the other two subtypes. Conclusion We found a significant association among patient age, tumor size, and PT subtype. Therefore, apart from the usual histological parameters, patient age and tumor size are important parameters for predicting the behavior of breast PT and should be considered for management.

To compare images generated by synthetic diffusion-weighted imaging (sDWI) with those from conventional DWI in terms of their diagnostic performance in detecting breast lesions when performing breast magnetic resonance imaging (MRI).
A total of 128 consecutive patients with 135 enhanced lesions who underwent dynamic MRI between 2018 and 2021 were included. The sDWI and DWI signals were compared by three radiologists with at least 10 years of experience in breast radiology.
Of the 82 malignant lesions, 91.5% were hyperintense on sDWI and 73.2% were hyperintense on DWI. Of the 53 benign lesions, 71.7% were isointense on sDWI and 37.7% were isointense on DWI. sDWI provides accurate signal intensity data with statistical significance compared with DWI (P < 0.05). The diagnostic performance of DWI and sDWI to differentiate malignant breast masses from benign masses was as follows: sensitivity 73.1% [95% confidence interval (CI): 62-82], specificity 37.7% (95% CI: 24-52); sensitivity 91.5% (95% CI: 83-96), specificity 71.7% (95% CI: 57-83), respectively. The diagnostic accuracy of DWI and sDWI was 59.2% and 83.7%, respectively. However, when the DWI images were evaluated with apparent diffusion coefficient mapping and compared with the sDWI images, the sensitivity was 92.68% (95% CI: 84-97) and the specificity was 79.25% (95% CI: 65-89) with no statistically significant difference. The inter-reader agreement was almost perfect (P < 0.001).
Synthetic DWI is superior to DWI for lesion visibility with no additional acquisition time and should be taken into consideration when conducting breast MRI scans. The evaluation of sDWI in routine MRI reporting will increase diagnostic accuracy.

UNASSIGNED: The differences in benign and malignant breast tumors are not only within the nodules but also involve changes in the surrounding tissues. Radiomics can reveal many details that are not discernible to the naked eye. This study aimed to distinguish between benign and malignant breast nodules using an ultrasound-based intra- and peritumoral radiomics model.
UNASSIGNED: This study retrospectively collected the information from 379 patients with Breast Imaging Reporting and Data System (BI-RADS) category 3-5 nodules and clear pathological diagnosis of breast nodules screened by routine ultrasound examination in the Sixth People\'s Hospital Affiliated to Medical College of Shanghai Jiao Tong University from January 2017 to December 2022. The largest dimension of the lesion on the 2D ultrasound image was selected to outline the area of interest which was conformally and outwardly expanded automatically by 5 mm to extract intra- and peritumor radiomics features. The included cases were randomly divided into training sets and test sets in a ratio of 7:3. The optimal features of the included models were retained by statistical and machine learning methods of dimensionality reduction, and logistic regression was used as the classifier to build an intratumoral model and a combined intratumoral-peritumoral radiomics model, respectively; through single-factor and multifactor logistic regression, the optimal features that could predict benign and malignant breast tumors were screened. The clinical and imaging models were established by selecting independent risk factors as clinical and imaging features through univariate and multifactorial logistic regression.
UNASSIGNED: Among 379 BI-RADS category 3-5 breast nodules, there were 124 malignant nodules and 255 benign nodules; patients were aged 14 to 88 (46.22±15.51) years, and the age differences, radiomics score, and mass diameter between the training and test sets were not statistically significant (P>0.05). The intra- and peritumor radiomics model had an area under the curve (AUC) of 0.840 [95% confidence interval (CI): 0.766-0.914] in the test set. The model with intra- and peritumoral ultrasound radiomics features combined with clinical features had an AUC value of 0.960 (95% CI: 0.920-0.999).
UNASSIGNED: The nomogram, developed using intratumoral and peritumoral radiomics features combined with clinical risk features, demonstrated superior performance in distinguishing between benign and malignant BI-RADS 3-5 lesions.

As mammography X-ray imaging technologies advance and provide elevated contrast in soft tissues, a need has developed for reliable imaging phantoms for use in system design and component calibration. In advanced imaging modalities such as refraction-based methods, it is critical that developed phantoms capture the biological details seen in clinical precancerous and cancerous cases while minimizing artifacts that may be caused due to phantom production. This work presents the fabrication of a breast tissue imaging phantom from cadaveric breast tissue suitable for use in both transmission and refraction-enhanced imaging systems.
Human cancer cell tumors were grown orthotopically in nude athymic mice and implanted into the fixed tissue while maintaining the native tumor/adipose tissue interface.
The resulting human-murine tissue hybrid phantom was mounted on a clear acrylic housing for absorption and refraction X-ray imaging. Digital breast tomosynthesis was also performed.
Both attenuation-based imaging and refraction-based imaging of the phantom are presented to confirm the suitability of this phantom\'s use in both imaging modalities.