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Plasmonic photothermal therapy (PPTT) involves the use of nanoparticles and near-infrared radiation to attain a temperature above 50 °C within the tumor for its thermal damage. PPTT is largely explored for superficial tumors, and its potential to treat deeper subsurface tumors is dealt feebly, requiring the assessment of thermal damage for such tumors. In this paper, the extent of thermal damage is numerically analyzed for PPTT of invasive ductal carcinoma (IDC) situated at 3–9 mm depths. The developed numerical model is validated with suitable tissue-tumor mimicking phantoms. Tumor (IDC) embedded with gold nanorods (GNRs) is subjected to broadband near-infrared radiation. The effect of various GNRs concentrations and their spatial distributions [viz. uniform distribution, intravenous delivery (peripheral distribution) and intratumoral delivery (localized distribution)] are investigated for thermal damage for subsurface tumors situated at various depths. Results show that lower GNRs concentrations lead to more uniform internal heat generation, eventually resulting in uniform temperature rise. Also, the peripheral distribution of nanoparticles provides a more uniform spatial temperature rise within the tumor. Overall, it is concluded that PPTT has potential to induce thermal damage for subsurface tumors, at depths of upto 9 mm, by proper choice of nanoparticle distribution, dose/concentration and irradiation parameters based on the tumor location. Moreover, intravenous administration of nanoparticles seems a good choice for shallower tumors, while for deeper tumors, uniform distribution is required to attain the necessary thermal damage. In the future, the algorithm may be extended further, involving 3D patient-specific tumors and through mice model-based experiments. [ABSTRACT FROM AUTHOR]

Although, superkiller complex protein 8 (SKI8), previously known as WDR61 has been identified and mapped in breast tumor, little is currently known about its function. This study aims to elucidate the role of WDR61 in breast tumor development and its potential as a therapeutic target. Here, we show that tamoxifen‐induced knockout of Wdr61 reduces the risk of breast tumors, resulting in smaller tumor size and weight, and improved overall survival. Furthermore, we show that knockdown of WDR61 compromises the proliferation of breast tumor cells with reduced colony‐forming capacity. Further investigations demonstrate that the protective effect of WDR61 loss on breast tumor development is due to genomic instability. Mechanistic studies reveal that WDR61 interacts with the R‐loop, and loss of WDR61 leads to R‐loops accumulation in breast tumor cells, causing DNA damage and subsequent inhibition of cell proliferation. In summary, this study highlights the critical dependence of breast tumors on WDR61, which suppresses R‐loop and counteracts endogenous DNA damage in tumor cells. [ABSTRACT FROM AUTHOR]

Breast conserving treatment typically involves surgical excision of tumor and adjuvant radiotherapy targeting the breast area or tumor bed. Accurately defining the tumor bed is challenging and lead to irradiation of greater volume of healthy tissues. Preoperative stereotactic body radiotherapy (SBRT) which target tumor may solves that issues. We conducted a systematic literature review to evaluates the early toxicity and cosmetic outcomes of this promising treatment approach. Secondary we reviewed pathological complete response (pCR) rates, late toxicity, patient selection criteria and radiotherapy protocols. We retrieved literature from PubMed, Scopus, Web of Science, Cochrane, ScienceDirect, and ClinicalTrials.gov. The study adhered to the PRISMA 2020 guidelines. Ten prospective clinical trials (7 phase II, 3 phase I), encompassing 188 patients (aged 18–75 years, cT1-T3 cN0-N3 cM0, primarily with ER/PgR-positive, HER2-negative status,), were analyzed. Median follow-up was 15 months (range 3–30). Treatment involved single-fraction SBRT (15-21Gy) in five studies and fractionated (19.5–31.5Gy in 3 fractions) in the rest. Time interval from SBRT to surgery was 9.5 weeks (range 1–28). Acute and late G2 toxicity occurred in 0–17% and 0–19% of patients, respectively, G3 toxicity was rarely observed. The cosmetic outcome was excellent in 85–100%, fair in 0–10% and poor in only 1 patient. pCR varied, showing higher rates (up to 42%) with longer intervals between SBRT and surgery and when combined with neoadjuvant systemic therapy (up to 90%). Preoperative SBRT significantly reduce overall treatment time, enabling to minimalize volumes. Early results indicate excellent cosmetic effects and low toxicity. [ABSTRACT FROM AUTHOR]

Abstract Breast conserving treatment typically involves surgical excision of tumor and adjuvant radiotherapy targeting the breast area or tumor bed. Accurately defining the tumor bed is challenging and lead to irradiation of greater volume of healthy tissues. Preoperative stereotactic body radiotherapy (SBRT) which target tumor may solves that issues. We conducted a systematic literature review to evaluates the early toxicity and cosmetic outcomes of this promising treatment approach. Secondary we reviewed pathological complete response (pCR) rates, late toxicity, patient selection criteria and radiotherapy protocols. We retrieved literature from PubMed, Scopus, Web of Science, Cochrane, ScienceDirect, and ClinicalTrials.gov. The study adhered to the PRISMA 2020 guidelines. Ten prospective clinical trials (7 phase II, 3 phase I), encompassing 188 patients (aged 18–75 years, cT1-T3 cN0-N3 cM0, primarily with ER/PgR-positive, HER2-negative status,), were analyzed. Median follow-up was 15 months (range 3–30). Treatment involved single-fraction SBRT (15-21Gy) in five studies and fractionated (19.5–31.5Gy in 3 fractions) in the rest. Time interval from SBRT to surgery was 9.5 weeks (range 1–28). Acute and late G2 toxicity occurred in 0–17% and 0–19% of patients, respectively, G3 toxicity was rarely observed. The cosmetic outcome was excellent in 85–100%, fair in 0–10% and poor in only 1 patient. pCR varied, showing higher rates (up to 42%) with longer intervals between SBRT and surgery and when combined with neoadjuvant systemic therapy (up to 90%). Preoperative SBRT significantly reduce overall treatment time, enabling to minimalize volumes. Early results indicate excellent cosmetic effects and low toxicity. Graphical abstract

Core needle biopsy is a common diagnostic procedure in breast cancer patients, but it can occasionally lead to serious complications. We report a rare case of pseudoaneurysm formation following a core needle biopsy in a 54-year-old female patient diagnosed with breast cancer. Despite the routine nature of the procedure, the patient developed a palpable mass at the biopsy site, which prompted further diagnostic imaging and interventions. The pseudoaneurysm was effectively treated using a percutaneous approach with ultrasound-guided thrombin injection, demonstrating a minimally invasive solution that promptly addressed the complication without the need for surgical intervention. This case highlights the critical importance of detecting complications early in the biopsy process, as they have significant implications for disease staging and treatment initiation. It also underscores the importance of being prepared for immediate intervention in case of biopsy-related complications like pseudoaneurysms, to prevent severe consequences.

A Polydimethylsiloxane (PDMS) based antenna is designed for skin tumor detection. The antenna functions at 2.45 GHz with a bandwidth of 2.30–2.64 GHz working in the ISM (Industrial, Scientific, and Medical) band. The size of the antenna is 40 × 40 × 1 mm3. This antenna detects tumors in the skin by considering the variations in values of the E-field, J-surf, and H-field. Various analyses such as the distance between the patch and stacked layer skin phantom for different tumor sizes and input power to the antenna are changed and antenna performance is observed. A significant amount of changes is attained which denotes the presence of the tumor. The proposed antenna is fabricated and the corresponding results are analyzed in the Anechoic Chamber. The antenna has an efficiency of 99 % with a Specific Absorption Rate of 1.3846 W/kg which is lower than 1.6 W/kg as per the recommendations of FCC standard. [ABSTRACT FROM AUTHOR]

Richter transformation (RT) represents the development of intrusive lymphoma in individuals previously or concurrently diagnosed with chronic lymphocytic leukemia (CLL) and is characterized by lymph node enlargement. However, cases involving extra-nodal organ involvement as the first symptom are rare. There are no reports of RT with breast lesions as the first symptom. Nonspecific and atypical clinical manifestations represent key challenges in the accurate diagnosis and appropriate treatment of RT. This case report describes an elderly female patient who presented with breast lesions as the first RT symptom. The patient was admitted with a painless mass in the left breast. Examination revealed multiple lymphadenopathies and abnormally high white blood cell levels. The patient was diagnosed with CLL after hematological tests, assessments of bone marrow morphology, and tissue biopsy. Mammography and B-ultrasonography showed solid space-occupying lesions (BI-RADS category 5) in the left breast. Initially, the patient declined a breast biopsy and was therefore prescribed ibrupotinib treatment, which showed limited efficacy. A needle biopsy of the affected breast indicated the presence of diffuse large B-cell lymphoma. Based on auxiliary and pathological examinations and medical history, the final diagnosis was RT with breast involvement. Zanubrutinib with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone treatment provided initial control; however, the treatment strategy required adjustment because of the patient’s fluctuating condition. The current status of the patient is marked as stable, showing an overall achievement of partial alleviation. The patient is in the process of receiving follow-up treatment. We also performed a comprehensive literature review on RT, with particular emphasis on its biological paradigm, prognosis implications, existing therapeutic approaches, and emerging directions in treatment modalities.

Abstract It is necessary to verify whether nail fold capillaroscopy can be utilized for the early detection of breast cancer. To establish this technology, an animal model was developed, utilizing mice for nail fold observations. Nail fold capillaroscopy revealed a human-like anatomical pattern and facilitated the observation of cellular movement within blood vessels. Injection of MCF-7 or mammary fibroblasts in mice allowed the observation of cellular vibrations using motion microscopy from nail fold. We have named this technology ‘capillary cell motion microscopy.’ Intriguingly, we were able to identify distinct cellular vibrations in the MCF-7 group. Moreover, evaluating its effectiveness in mice with chemically induced cancer revealed higher sensitivity (81%-85%) compared to conventional methods (45%-68%). Capillary cell motion microscopy, operating at 0.5–1.5 Hz, provided clear distinction of tumor cells and demonstrated potential applicability in human subjects. While condition adjustments may be necessary, this method holds promise for noninvasive breast cancer detection through nail fold observations.

The detection of cancer tumors is an essential component that has important consequences for the speedy involvement of medical professionals and the enhancement of patient outcomes. This review paper presents a complete study of the current body of research and methodology, as well as an in-depth assessment of the use of machine learning (ML) and deep learning (DL) in the detection of cancer tumors. In addition, the article gives a full analysis of the approaches involved. Machine learning and deep learning, which effectively handle ambiguity in the identification of malignant tumors, provide an alternative method for dealing with the complexity of brain tissue. This method is offered by a combination of machine learning and deep learning. The first part of the review draws attention to the significance of making an accurate diagnosis of breast cancer, highlights the limits of traditional diagnostic methods, and investigates the cutting-edge area of medical imaging technology. After that, it investigates the fundamentals of ML and DL and how they might be used to deal with the challenges that are inherent in the interpretation of complicated imaging data. In addition, the paper explores the ways in which models enhance the processes of feature extraction, picture segmentation, and classification in breast tumor detection systems.

It is necessary to verify whether nail fold capillaroscopy can be utilized for the early detection of breast cancer. To establish this technology, an animal model was developed, utilizing mice for nail fold observations. Nail fold capillaroscopy revealed a human-like anatomical pattern and facilitated the observation of cellular movement within blood vessels. Injection of MCF-7 or mammary fibroblasts in mice allowed the observation of cellular vibrations using motion microscopy from nail fold. We have named this technology 'capillary cell motion microscopy.' Intriguingly, we were able to identify distinct cellular vibrations in the MCF-7 group. Moreover, evaluating its effectiveness in mice with chemically induced cancer revealed higher sensitivity (81%-85%) compared to conventional methods (45%-68%). Capillary cell motion microscopy, operating at 0.5–1.5 Hz, provided clear distinction of tumor cells and demonstrated potential applicability in human subjects. While condition adjustments may be necessary, this method holds promise for noninvasive breast cancer detection through nail fold observations. [ABSTRACT FROM AUTHOR]

Background: Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI‐RADS) category 4 has the highest false‐positive value of about 30% among five categories. The classification task in BI‐RADS category 4 is challenging and has not been fully studied. Purpose: This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B‐mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy. Methods: First, original US images coming directly from the hospital were cropped and resized. In 1385 B‐mode US BI‐RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K‐means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B‐mode images, K‐means clustering images and entropy images, they are fused in a three‐channel form multi‐feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1‐score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro‐Wilk test. DeLong test and independent t‐test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti‐log compression was conducted but no improvement has shown in CNNs classification results. Results: With multi‐feature fusion images, DenseNet121 has highest accuracy of 80.22 ± 1.45% compared to other CNNs, precision of 77.97 ± 2.89% and AUC of 0.82 ± 0.01. Multi‐feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B‐mode images (p < 0.05). Conclusion: The CNNs with multi‐feature fusion show a good potential of reducing the false‐positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false‐positive rate in breast tumor within US BI‐RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise. [ABSTRACT FROM AUTHOR]

Radiologists and clinicians must automatically examine breast and tumor locations and sizes accurately. In recent years, several neural network‐based feature fusion versions have been created to improve medical image segmentation. Multi‐modal image fusion photos may efficiently identify tumors. This work uses image fusion to identify computed tomography and magnetic resonance imaging alterations. A Gauss‐log ratio operator is recommended for difference image production. The Gauss‐log ratio and log ratio difference image complement the objective of improving the difference map through image fusion. The feature change matrix extracts edge, texture, and intensity from each picture pixel. The final change detection map classifies feature vectors as "changed" or "unchanged" which has been mapped for high‐resolution or low‐resolution pixels. This paper proposes a multi‐feature blocks (MFB) based neural network for multi‐feature fusion. This neural network modeling approach globalizes pixel spatial relationships. MFB‐based feature fusion also aims to capture channel interactions between feature maps. The proposed technique outperforms state‐of‐the‐art approaches which have been discussed in detail in experimental results section. [ABSTRACT FROM AUTHOR]

A flexible slotted metamaterial backed microstrip (SMBM) antenna operating at 2.45 GHz is proposed in this article for breast tumor detection. The substrate used is flexible optically transparent polydimethylsiloxane (PDMS) with 1-mm thickness, and copper is used for conducting layer. The SMBM antenna consists of partial ground and metasurface unit cells. It is noted that with the addition of metamaterial layers in the antenna the bandwidth, reflection co-efficient got improved and the specific absorption rate (SAR) value is reduced. Two different kinds of phantoms are arranged: one is a skin phantom and the other is a breast phantom; the antenna of the flat and flexible condition is placed on the phantoms and the analysis is observed. The detection of tumors can be observed by the variance in the value of E-Field, H-Field, and J-Surf between the phantom has a tumor and no tumor. A notable amount of difference is obtained which represents the existence of a tumor. The antenna has the SAR value, which is less than 1.6 W/kg as per the guidance of Federal Communications Commission (FCC) standard. Alongside SAR, by utilizing metamaterial, the gain of the SMBM antenna is likewise improved and the efficiency of the SMBM is expanded with 67% (to 92%) by the consideration of metamaterial construction. The results of the fabricated antenna are analyzed, and comparisons are made with the simulated antenna. [ABSTRACT FROM AUTHOR]

This study elucidated the effects of a three-dimensional k-space trajectory incorporating the partial Fourier (PF) technique on a time–intensity curve (TIC) in a dynamic contrast-enhanced magnetic resonance imaging of a typical malignant breast tumor using a digital phantom. Images were obtained from the Cancer Imaging Archive Open Data for Breast Cancer, and 1-min scans with high temporal resolution were analyzed. The order of the k-space trajectory was set as Linear (sequential), Low–High (centric), PF (62.5%; Z-, Y-, and both directions), and Low–High Radial. k0 (center of the k-space) timing and TIC shape were affected by the chosen k-space trajectory and implementation of the PF technique. A small TIC gradient was obtained using a Low–High Radial order. If the k-space filling method (particularly the radial method) produces a gentle TIC gradient, misinterpretation could arise during the assessment of tumor malignancy status. [ABSTRACT FROM AUTHOR]

The computational modelling and analysis of internal and external temperature distributions and their gradients, associated with the first stage of mammary tumors, was performed to relate thermal parameters to relevant tumor characteristics. A realistic 3D geometric model of breast anatomy was used to simulate tumor cases that were characterized in real life at their primary clinical stage. The thermophysical parameters of three tumors were extracted to implement the models; a fourth case without a tumor was used as a reference to provide quantitative measurements of temperature increases and gradient changes. The analysis considered superficial and internal temperature distributions and gradients, computed throughout specific paths. Finally, an evaluation was made of the ability of the thermometric technologies available today to detect the changes estimated in simulations. Maximum temperature increments in the range of 2.30 to 3.20 °C and in the range of 0.15 to 0.30 °C were found on internal and superficial paths, respectively. Internal gradient peak magnitudes fluctuated within the range of 0.34 to 1.14 °C/mm. Thermal results indicated a direct correlation between tumor size and temperature rise. Nevertheless, gradient results showed that the heat generation rate, an indicator of tumor malignancy, was directly proportional to internal gradient maximum peaks, which were related to tumor boundaries regardless of tumor size. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, this method extracts 3D mesh features that describe tumor patterns more precisely. Ten informative and medically relevant mesh features are extracted and assessed with two feature selection techniques. Additionally, a feature pattern analysis has been conducted to determine the feature's significance. A feature table with dimensions of 445 × 12 is generated and a graph is constructed, considering the rows as nodes and the relationships among the nodes as edges. The Spearman correlation coefficient method is employed to identify edges between the strongly connected nodes (with a correlation score greater than or equal to 0.7), resulting in a graph containing 56,054 edges and 445 nodes. A graph attention network (GAT) is proposed for the classification task and the model is optimized with an ablation study, resulting in the highest accuracy of 99.34%. The performance of the proposed model is compared with ten machine learning (ML) models and one-dimensional convolutional neural network where the test accuracy of these models ranges from 73 to 91%. Our novel 3D mesh-based approach, coupled with the GAT, yields promising performance for breast tumor classification, outperforming traditional models, and has the potential to reduce time and effort of radiologists providing a reliable diagnostic system. [ABSTRACT FROM AUTHOR]

This article presents a case study of a rare type of breast tumor called a phyllodes tumor. The tumor in this case exhibited features of osteosarcoma and chondrosarcoma differentiation, which are uncommon in phyllodes tumors. The study emphasizes the challenges in diagnosing and managing these tumors and highlights the importance of accurate diagnosis and appropriate treatment. The article also provides a list of references to other studies and articles that cover various aspects of phyllodes tumors, making it a valuable resource for researchers and healthcare professionals interested in understanding and treating these tumors. [Extracted from the article]

Aims: The development of nanocomposites (NCs) of antitumor activity provides a new paradigm for fighting cancer. Here, a novel NC of green synthetic silver nanoparticles (AgNPs), graphene oxide (GO) and chitosan (Cs) NPs was developed. Materials & methods: The prepared GO/Cs/Ag NCs were analyzed using various techniques. Cytotoxicity of the NCs was evaluated against different cancer cell lines by Sulforhodamine B (SRB) assay. Results: GO/Cs/Ag NCs are novel and highly stable. UV-Vis showed two peaks at 227 and 469 nm, indicating the decoration of AgNPs on the surface of GO/Cs NPs. All tested cell lines were affected by GO/Cs NPs and GO/Cs/Ag NCs. Conclusion: The results indicate that GO/Cs/Ag NCs were present on tested cell lines and are a promising candidate for cancer therapy. [ABSTRACT FROM AUTHOR]

Background. The inverse problem of calculating coefficients in the Havriliak – Negami model is considered in order to determine the structure of an inhomogeneous dielectric object based on the determination of parameters and analysis of the frequency characteristics of the relative permittivity. Materials and methods. The method of calculating the coefficients in Havriliak – Negami model using three measurements of the complex permittivity at different frequencies is applied. Results and conclusions. A numerical method has been developed to determine the coefficients in Havriliak – Negami model, which is used in dielectric spectroscopy for the early detection of tumors in the mammary gland.

Abstract Background Nipple adenoma is a relatively rare benign disease. Clinically, it often presents with nipple erosions, and it should be differentiated from Paget’s disease. Case presentation The patient was a 63-year-old woman who complained of a lump in her left nipple for more than 30 years. Computed tomography performed for screening congestive heart failure suggested a left nipple mass of 40 mm in size. Needle biopsy revealed nipple adenoma, and skin biopsy was also performed to confirm the diagnosis. Nipple tumor resection was performed under local anesthesia, and we confirmed that the final diagnosis was nipple adenoma with negative margins. The patient has been free from recurrence for 2 years since the surgery. Conclusions We have reported our experience of a case of giant nipple adenoma.

Immediate monitoring of the patient has always been very important. Achieving this knowledge, which can be integrated to monitor damaged tissue, is very important. In previous methods, the tissue was monitored using a sensor. In this article, not only is a tissue monitored using a sensor, but also the monitoring and evaluation of the effect of other tissues on tumor tissue is evaluated. The smart grid discussed in this article is designed to monitor the condition of a patient with a breast tumor. The structure of the smart grid, given the weight of the communication paths between the nodes and the ability of the nodes, shows us a strong network to assess the patient's condition. As the patient's condition changes, the nodes and weights of the communication pathways change, indicating that there is important information in the network and helping specialists to better assess the condition of the disease. Network monitoring is such that the evaluator node continuously evaluates the tumor node, by changing the status of the tumor node, the status of other nodes and communication paths between them changes, the result of changes in the network by the node The evaluator is evaluated. The simulation results show that this network has the necessary intelligence to assess the patient's condition in adverse conditions.

Medical image segmentation is crucial for deep learning (DL) applications in clinical settings. Ensuring accurate segmentation is challenging due to diverse image sources and significant data sharing and privacy concerns in centralized learning setups. To address these challenges, we introduce a novel federated learning (FL) framework tailored for breast cancer. First, we use random regions of interest (ROIs) and bilinear interpolation to determine pixel color intensity based on neighboring pixels, addressing data inconsistencies from heterogeneous distribution parameters and increasing dataset size. We then employ the UNet model with a deep convolutional backbone (Visual Geometry Group [VGG]) to train the augmented data, enhancing recognition during training and testing. Second, we apply the Gaussian Mixture Model (GMM) to improve segmentation quality. This approach effectively manages distinct data distributions across hospitals and highlights images with a higher likelihood of tumor presence. Compared to other segmentation algorithms, GMM enhances the salience of valuable images, improving tumor detection. Finally, extensive experiments in two scenarios, federated averaging (FedAvg) and federated batch normalization (FedBN), demonstrate that our method outperforms several state-of-the-art segmentation methods on five public breast cancer datasets. These findings validate the effectiveness of our proposed framework, promising significant benefits for the community and society.

Background: Nipple adenoma is a relatively rare benign disease. Clinically, it often presents with nipple erosions, and it should be differentiated from Paget's disease. Case presentation: The patient was a 63-year-old woman who complained of a lump in her left nipple for more than 30 years. Computed tomography performed for screening congestive heart failure suggested a left nipple mass of 40 mm in size. Needle biopsy revealed nipple adenoma, and skin biopsy was also performed to confirm the diagnosis. Nipple tumor resection was performed under local anesthesia, and we confirmed that the final diagnosis was nipple adenoma with negative margins. The patient has been free from recurrence for 2 years since the surgery. Conclusions: We have reported our experience of a case of giant nipple adenoma. [ABSTRACT FROM AUTHOR]

In preclinical studies, accurate monitoring of tumor dynamics is crucial for understanding cancer biology and evaluating therapeutic interventions. Traditional methods like caliper measurements and bioluminescence imaging (BLI) have limitations, prompting the need for improved imaging techniques. This study introduces a fast-scan high-frequency ultrasound (HFUS) protocol for the longitudinal assessment of syngeneic breast tumor grafts in mice, comparing its performance with caliper, BLI measurements and with histological analysis. The E0771 mammary gland tumor cell line, engineered to express luciferase, was orthotopically grafted into immunocompetent C57BL/6 mice. Tumor growth was monitored longitudinally at multiple timepoints using caliper measurement, HFUS, and BLI, with the latter two modalities assessed against histopathological standards post-euthanasia. The HFUS protocol was designed for rapid, anesthesia-free scanning, focusing on volume estimation, echogenicity, and necrosis visualization. All mice developed tumors, only 20.6% were palpable at day 4. HFUS detected tumors as small as 2.2 mm in average diameter from day 4 post-implantation, with an average scanning duration of 47 s per mouse. It provided a more accurate volume assessment than caliper, with a lower average bias relative to reference tumor volume. HFUS also revealed tumor necrosis, correlating strongly with BLI in terms of tumor volume and cellularity. Notable discrepancies between HFUS and BLI growth rates were attributed to immune cell infiltration. The fast HFUS protocol enables precise and efficient tumor assessment in preclinical studies, offering significant advantages over traditional methods in terms of speed, accuracy, and animal welfare, aligning with the 3R principle in animal research. [ABSTRACT FROM AUTHOR]

Objective To construct methoxy polyethylene glycol (mPEG) modified gold nanoparticles (AuNPs) loaded with doxorubicin (DOX) AuNPs-mPEG@DOX in order to reduce the toxicity and side effects of DOX. Methods AuNPsmPEG@DOX was prepared and characterized by Z-Average, Zeta potential and UV-Vis spectroscopy. The impact of thiol-linked DOX (HS-DOX) at various dosage concentrations on the drug adsorption rate and drug loading of AuNPs-mPEG@DOX was investigated. Furthermore, a HPLC method was developed to accurately determine the content of unadsorbed HS-DOX in AuNPsmPEG@DOX. The specificity, linearity, precision, stability and average recovery of this method were thoroughly investigated. The cytotoxic effect of AuNPs-mPEG@DOX on MCF-10A and MCF-7 cells was evaluated using a CCK-8 assay. Results AuNPsmPEG@DOX was successfully prepared with Z-Average of (46.12±0.49) nm, Zeta potential of (18.60±1.51) nm and the maximum absorption wavelength of 530 nm. An efficient HPLC method for the detection of unadsorbed HS-DOX in AuNPs-mPEG@DOX was devised. The optimal dosage concentration of HS-DOX for AuNPs-mPEG@DOX was determined to be 11.18 μg/ml, resulting in a drug adsorption rate of (9.21±2.88)% and a drug loading rate of (2.01±0.62)%. Cytotoxicity experiments demonstrated that AuNPs-mPEG@DOX significantly reduced the toxic and side effects of DOX on normal breast cells. Additionally, AuNPsmPEG@DOX and free DOX exhibited comparable cytotoxic effects on breast tumor cells when DOX concentration was equal to or greater than 4.75 μmol/L. Conclusion AuNPs-mPEG@DOX effectively reduce the toxicity of DOX, providing a reference for future research on reducing the toxicity of AuNPs-linked drugs. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common cancers in the world, especially among women. Breast tumor segmentation is a key step in the identification and localization of the breast tumor region, which has important clinical significance. Inspired by the swin-transformer model with powerful global modeling ability, we propose a semantic segmentation framework named Swin-Net for breast ultrasound images, which combines Transformer and Convolutional Neural Networks (CNNs) to effectively improve the accuracy of breast ultrasound segmentation. Firstly, our model utilizes a swin-transformer encoder with stronger learning ability, which can extract features of images more precisely. In addition, two new modules are introduced in our method, including the feature refinement and enhancement module (RLM) and the hierarchical multi-scale feature fusion module (HFM), given that the influence of ultrasonic image acquisition methods and the characteristics of tumor lesions is difficult to capture. Among them, the RLM module is used to further refine and enhance the feature map learned by the transformer encoder. The HFM module is used to process multi-scale high-level semantic features and low-level details, so as to achieve effective cross-layer feature fusion, suppress noise, and improve model segmentation performance. Experimental results show that Swin-Net performs significantly better than the most advanced methods on the two public benchmark datasets. In particular, it achieves an absolute improvement of 1.4–1.8% on Dice. Additionally, we provide a new dataset of breast ultrasound images on which we test the effect of our model, further demonstrating the validity of our method. In summary, the proposed Swin-Net framework makes significant advancements in breast ultrasound image segmentation, providing valuable exploration for research and applications in this domain. [ABSTRACT FROM AUTHOR]

Key Clinical Message: Pseudoangiomatous stromal hyperplasia (PASH) is a rare lesion of the breast stromal tissue with unknown mechanism. Hormonal stimulation of mammary myofibroblasts is the most important theory due to stromal positivity of progesterone receptor (PR) or/and estrogen receptor (ER). We report a case of PASH with stromal PR/ER negativity. [ABSTRACT FROM AUTHOR]

Purpose: This article introduces an intelligent network for monitoring breast cancer patients. The increase in the network's speed for monitoring the patient depends on the extracted information from the nodes, information rate, network topology, type of data, and the analysis method used for processing this information. The extracted information from the network determines the patient monitoring rate, and the network infrastructure is designed in a way that reports the patient's information in real-time, moment by moment. The decision-making model is based on the OODA cycle, which includes the stages of "Observe," where information is extracted from the network, "Orient," "Decide," and "Act." The stages of "Analyze," "Decide," and "Act" are based on the Fuzzy Analytic Hierarchy Process (FAHP) model. Methods: In this paper, three algorithms, namely, Fuzzy Analytic Hierarchy Process (FAHP), Fuzzy Analytic Hierarchy Process (FAHP), and one-way analysis of variance (ANOVA), have been used to evaluate the "Analysis" and "Decision" stages. These three algorithms are compared with each other using the criteria of Accuracy, Specificity, and Sensitivity. Furthermore, the intelligent network is evaluated based on its network topology, data, and decision-making speed. Results: The simulation results demonstrate that the Fuzzy Analytic Hierarchy Process (FAHP) method exhibits a significantly higher level of accuracy compared to the other methods. The simulation results indicate that the accuracy and speed of the intelligent network monitoring have improved by approximately 9.8 compared to other non-intelligent networks (OODA loop). Conclusions: In this article, an intelligent network is proposed for monitoring breast cancer patients. The intelligent network has an OODA loop for monitoring and controlling the patient. The OODA loop includes observation, analysis, decision, and action, making it a continuous and ever-changing process. In order to expand the OODA loop for better control and monitoring of the patient, we have extended the "observation" and "orientation" phases of the process.The observation method is determined based on the type of data and the network structure. Another method is the directionality, which is determined based on the hierarchical fuzzy analysis. Indeed, the intelligence of the network aims to adjust the decision-making criteria analysis in a way that maximizes the speed of diagnosing the patient with minimal time. [ABSTRACT FROM AUTHOR]

Context: Breast cancer remains a major health burden worldwide, necessitating improved screening modalities for early detection. However, existing techniques such as mammography and MRI exhibit limitations regarding sensitivity and specificity. Microwave imaging has recently emerged as a promising technology for breast cancer diagnosis, exploiting the dielectric contrast between normal and malignant tissues. Objectives: This study proposes a novel computational framework integrating thresholding, edge segmentation, and logistic regression to enhance microwave image-based breast tumor delineation. Methodology: The employed algorithm selects optimal features using logistic regression to mitigate the class imbalance between tumor and healthy tissues. Localized density thresholds are applied to identify tumor regions, followed by edge segmentation methods to precisely localize the detected lesions. Results: When evaluated on a dataset of microwave breast images, our approach demonstrated high accuracy for detecting and segmenting malignant tissues. Density thresholds ranging from 0.1 to 0.8 showcase the highest accuracy in detecting breast tumors from these images. Conclusions: The results highlight the potential of the proposed segmentation algorithm to improve the reliability of microwave imaging as an adjunct modality for breast cancer screening. This could promote earlier diagnosis and better clinical outcomes. The proposed framework represents a significant advance in developing robust image processing techniques tailored to emerging medical imaging modalities challenged by class imbalance and low intrinsic contrast.

Breast cancer is the most common cancer in women, and non-destructive detection of the tumor is vital. The interaction of electromagnetic waves with breast tissue and the behavior of waves after interaction are used to model tumor detection mathematically. The behavior of electromagnetic waves in a medium is described using Maxwell's equations. Electromagnetic waves propagate according to the electrical properties of a medium. Since the electrical properties of tumor tissue are different from those of normal breast tissue, it is assumed that the tumor is a lossy dielectric sphere, and the breast is a lossy dielectric medium. Under this assumption, Maxwell's equations are used to calculate the scattered field from the tumor. The field scattered by the tumor is different from other tissues because their dielectric properties are different. The location and size of the tumor can be determined by utilizing the difference in scattering from the tissues. While the scattering field from the tumor in spherical geometric form is analytically calculated, it is not analytically possible to calculate the scattering field from the tumor in different geometric shapes. In addition to non-destructive detection of the tumor, an efficient numerical method, the finite difference time domain method (FDTD), is used to simulate the field distribution. After the location of the tumor is determined, the Alternating Direction Implicit (ADI) FDTD method, which gives simulation results by dividing the computation domain into smaller sub-intervals, can be used. Scattered fields are calculated analytically in the geometry where the tumor is in the form of a smooth sphere, and in more complex geometry, the field distributions are successfully obtained with the help of MATLAB using FDTD and ADI-FDTD algorithms.

The subject matter of this study is breast tumor prediction and feature importance score finding using machine learning algorithms. The goal of this study was to develop an accurate predictive model for identifying breast tumors and determining the importance of various features in the prediction process. The tasks undertaken included collecting and preprocessing the Wisconsin Breast Cancer original dataset (WBCD). Dividing the dataset into training and testing sets, training using machine learning algorithms such as Random Forest, Decision Tree (DT), Logistic Regression, Multi-Layer Perceptron, Gradient Boosting Classifier, Gradient Boosting Classifier (GBC), and K-Nearest Neighbors, evaluating the models using performance metrics, and calculating feature importance scores. The methods used involve data collection, preprocessing, model training, and evaluation. The outcomes showed that the Random Forest model is the most reliable predictor with 98.56 % accuracy. A total of 699 instances were found, and 461 instances were reached using data optimization methods. In addition, we ranked the top features from the dataset by feature importance scores to determine how they affect the classification models. Furthermore, it was subjected to a 10-fold cross-validation process for performance analysis and comparison. The conclusions drawn from this study highlight the effectiveness of machine learning algorithms in breast tumor prediction, achieving high accuracy and robust performance metrics. In addition, the analysis of feature importance scores provides valuable insights into the key indicators of breast cancer development. These findings contribute to the field of breast cancer diagnosis and prediction by enhancing early detection and personalized treatment strategies and improving patient outcomes.

Abstract Background TGFβ induces several cell phenotypes including senescence, a stable cell cycle arrest accompanied by a secretory program, and epithelial-mesenchymal transition (EMT) in normal epithelial cells. During carcinogenesis cells lose the ability to undergo senescence in response to TGFβ but they maintain an EMT, which can contribute to tumor progression. Our aim was to identify mechanisms promoting TGFβ-induced senescence escape. Methods In vitro experiments were performed with primary human mammary epithelial cells (HMEC) immortalized by hTert. For kinase library screen and modulation of gene expression retroviral transduction was used. To characterize gene expression, RNA microarray with GSEA analysis and RT-qPCR were used. For protein level and localization, Western blot and immunofluorescence were performed. For senescence characterization crystal violet assay, Senescence Associated-β-Galactosidase activity, EdU staining were conducted. To determine RSK3 partners FLAG-baited immunoprecipitation and mass spectrometry-based proteomic analyses were performed. Proteosome activity and proteasome enrichment assays were performed. To validate the role of RSK3 in human breast cancer, analysis of METABRIC database was performed. Murine intraductal xenografts using MCF10DCIS.com cells were carried out, with histological and immunofluorescence analysis of mouse tissue sections. Results A screen with active kinases in HMECs upon TGFβ treatment identified that the serine threonine kinase RSK3, or RPS6KA2, a kinase mainly known to regulate cancer cell death including in breast cancer, reverted TGFβ-induced senescence. Interestingly, RSK3 expression decreased in response to TGFβ in a SMAD3-dependent manner, and its constitutive expression rescued SMAD3-induced senescence, indicating that a decrease in RSK3 itself contributes to TGFβ-induced senescence. Using transcriptomic analyses and affinity purification coupled to mass spectrometry-based proteomics, we unveiled that RSK3 regulates senescence by inhibiting the NF-κΒ pathway through the decrease in proteasome-mediated IκBα degradation. Strikingly, senescent TGFβ-treated HMECs display features of epithelial to mesenchymal transition (EMT) and during RSK3-induced senescence escaped HMECs conserve EMT features. Importantly, RSK3 expression is correlated with EMT and invasion, and inversely correlated with senescence and NF-κΒ in human claudin-low breast tumors and its expression enhances the formation of breast invasive tumors in the mouse mammary gland. Conclusions We conclude that RSK3 switches cell fate from senescence to malignancy in response to TGFβ signaling.

In radiation therapy, investigating of the effects of the surface flux reaching on the tissue is important in planning the treatment and this requires a precise evaluation of the absorbed dose distribution throughout the irradiated tissue. Therefore, by Monte Carlo simulation with MCNP code, a point source with the size of E with a spectrum width of 0.6 µm with a single energy transfer of 6 MeV to the breast tumor tissue with a size of 2 x 4 x 4 cm and also a density (Kg/m^3) of 11.34 at a fixed depth of 3 cm. It is radiated from a standard phantom (VIP MAN) made of tissue. The results show the highest surface flux that received on the tumor is around 9.97 × 〖10〗^(-6) and is located almost in the center of the tumor in dimensions (-0.75 cm - 1.3 cm) and the less surface flux around the tumor is caused by the rate of the dose which is distributed. Also, the template phenomenon in the creation of electrons is based on the Compton effect, while in the creation of photons, the Compton effect did not occur.