A review of various modalities in breast imaging: technical aspects and clinical outcomes

Deep orthogonal multi-wavelength fusion for tomogram-free diagnosis in diffuse optical imaging

Novel portable diffuse optical tomography (DOT) devices for breast cancer lesions hold great promise for non-invasive, non-ionizing breast cancer screening. Critical to this capability is not just the identification of lesions but rather the complex problem of discriminating between malignant and benign lesions. To accurately reconstruct the highly heterogeneous tissue of a cancer lesion in healthy breast tissue using DOT, multiple wavelengths can be leveraged to maximize signal penetration while minimizing sensitivity to noise. However, these wavelength responses can overlap, capture common information, and correlate, potentially confounding reconstruction and downstream end tasks. We show that an orthogonal fusion loss regularizes multi-wavelength DOT leading to improved reconstruction and accuracy of end-to-end discrimination of malignant versus benign lesions. We further show that our raw-to-task model significantly reduces computational complexity without sacrificing accuracy, making it ideal for real-time throughput, desired in medical settings where handheld devices have severely restricted power budgets. Furthermore, our results indicate that image reconstruction is not necessary for unbiased classification of lesions with a balanced accuracy of 77% and 66% on the synthetic dataset and clinical dataset, respectively, using the raw-to-task model. Code is available at https://github.com/sfu-mial/FuseNet.

A Review of Artificial Intelligence in Breast Imaging

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

Impact of 18F-FDG PET/CT in the management decisions of breast cancer board on early-stage breast cancer

PURPOSE

Breast cancer is the most common malignancy accounting for 11.7% of all cancer cases, with a rising incidence rate. Various diagnostic methods, including 18F-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT), play a crucial role in breast cancer diagnosis and staging. However, the unnecessary use of advanced imaging techniques such as PET/CT in early-stage breast cancer can have negative effects on both economics and patients. We aimed to investigate the impact of PET/CT on the management decisions of early-stage breast cancer patients by the breast cancer tumor board.

METHODS

A retrospective analysis was performed on a cohort of 81 patients with early-stage breast cancer who were evaluated by breast cancer tumor board from January 2015 to December 2020. Demographic, clinical, and radiographic data, along with surgical procedures and treatment options, were documented and analyzed.

RESULTS

The results showed that 18F-FDG PET/CT had a moderate impact on treatment decisions of breast cancer tumor board, as only treatment decisions were changed in 14,86% of the patients. The surgical procedure decision of breast cancer tumor board changed in 12.35% of patients, while 87.65% of patients had consistent decisions before and after PET/CT. Pathological assessments revealed invasive ductal carcinoma as the most prevalent tumor type, and molecular subtypes were predominantly luminal B. PET/CT use had limited impact on surgical procedures and did not significantly alter treatment decisions of breast cancer tumor board in this early-stage breast cancer cohort.

CONCLUSIONS

In conclusion, this study highlights the importance of adherence to the guidelines and appropriate use of PET/CT in early-stage breast cancer management. PET/CT should be reserved for cases where it is clinically warranted, considering the potential economic burden and minimal impact on treatment decisions of breast cancer tumor board in this patient population.

Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models

Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions.

A novel textile-based UWB patch antenna for breast cancer imaging

Breast cancer is the second leading cause of death for women worldwide, and detecting cancer at an early stage increases the survival rate by 97%. In this study, a novel textile-based ultrawideband (UWB) microstrip patch antenna was designed and modeled to work in the 2-11.6 GHz frequency range and a simulation was used to test its performance in early breast cancer detection. The antenna was designed with an overall size of 31*31 mm2 using a denim substrate and 100% metal polyamide-based fabric with copper, silver, and nickel to provide comfort for the wearer. The designed antenna was tested in four numerical breast models. The models ranged from simple tumor-free to complex models with small tumors. The size, structure, and position of the tumor were modified to test the suggested ability of the antenna to detect cancers with different shapes, sizes, and positions. The specific absorption rate (SAR), return loss (S11), and voltage standing wave ratio (VSWR) were calculated for each model to measure the antenna performance. The simulation results showed that SAR values were between 1.6 and 2 W/g (10 g SAR) and were within the allowed range for medical applications. Additionally, the VSWR remained in an acceptable range from 1.15 to 2. Depending on the size and location of the tumor, the antenna return losses of the four models ranged from - 36 to - 18.5 dB. The effect of bending was tested to determine the flexibility. The antenna proved to be highly effective and capable of detecting small tumors with diameters of up to 2 mm.

miR-125 in Breast Cancer Etiopathogenesis: An Emerging Role as a Biomarker in Differential Diagnosis, Regenerative Medicine, and the Challenges of Personalized Medicine

Breast Cancer (BC) is one of the most common cancer types worldwide, and it is characterized by a complex etiopathogenesis, resulting in an equally complex classification of subtypes. MicroRNA (miRNA or miR) are small non-coding RNA molecules that have an essential role in gene expression and are significantly linked to tumor development and angiogenesis in different types of cancer. Recently, complex interactions among coding and non-coding RNA have been elucidated, further shedding light on the complexity of the roles these molecules fulfill in cancer formation. In this context, knowledge about the role of miR in BC has significantly improved, highlighting the deregulation of these molecules as additional factors influencing BC occurrence, development and classification. A considerable number of papers has been published over the past few years regarding the role of miR-125 in human pathology in general and in several types of cancer formation in particular. Interestingly, miR-125 family members have been recently linked to BC formation as well, and complex interactions (competing endogenous RNA networks, or ceRNET) between this molecule and target mRNA have been described. In this review, we summarize the state-of-the-art about research on this topic.

Strain Elastography Fat-to-Lesion Index Is Associated with Mammography BI-RADS Grading, Biopsy, and Molecular Phenotype in Breast Cancer

Breast cancer (BC) affects millions of women worldwide, causing over 500,000 deaths annually. It is the leading cause of cancer mortality in women, with 70% of deaths occurring in developing countries. Elastography, which evaluates tissue stiffness, is a promising real-time minimally invasive technique for BC diagnosis. This study assessed strain elastography (SE) and the fat-to-lesion (F/L) index for BC diagnosis. This prospective study included 216 women who underwent SE, ultrasound, mammography, and breast biopsy (108 malignant, 108 benign). Three expert radiologists performed imaging and biopsies. Mean F/L index was 3.70 ± 2.57 for benign biopsies and 18.10 ± 17.01 for malignant. We developed two predictive models: a logistic regression model with AUC 0.893, 79.63% sensitivity, 87.62% specificity, 86.9% positive predictive value (+PV), and 80.7% negative predictive value (-PV); and a neural network with AUC 0.902, 80.56% sensitivity, 88.57% specificity, 87.9% +PV, and 81.6% -PV. The optimal Youden F/L index cutoff was >5.76, with 84.26% sensitivity and specificity. The F/L index positively correlated with BI-RADS (Spearman's r = 0.073, p < 0.001) and differed among molecular subtypes (Kruskal-Wallis, p = 0.002). SE complements mammography for BC diagnosis. With adequate predictive capacity, SE is fast, minimally invasive, and useful when mammography is contraindicated.

Breast cancer detection using enhanced IRI-numerical engine and inverse heat transfer modeling: model description and clinical validation

Effective treatment of breast cancer relies heavily on early detection. Routine annual mammography is a widely accepted screening technique that has resulted in significantly improving the survival rate. However, it suffers from low sensitivity resulting in high false positives from screening. To overcome this problem, adjunctive technologies such as ultrasound are employed on about 10% of women recalled for additional screening following mammography. These adjunctive techniques still result in a significant number of women, about 1.6%, who undergo biopsy while only 0.4% of women screened have cancers. The main reason for missing cancers during mammography screening arises from the masking effect of dense breast tissue. The presence of a tumor results in the alteration of temperature field in the breast, which is not influenced by the tissue density. In the present paper, the IRI-Numerical Engine is presented as an adjunct for detecting cancer from the surface temperature data. It uses a computerized inverse heat transfer approach based on Pennes's bioheat transfer equations. Validation of this enhanced algorithm is conducted on twenty-three biopsy-proven breast cancer patients after obtaining informed consent under IRB protocol. The algorithm correctly predicted the size and location of cancerous tumors in twenty-four breasts, while twenty-two contralateral breasts were also correctly predicted to have no cancer (one woman had bilateral breast cancer). The tumors are seen as highly perfused and metabolically active heat sources that alter the surface temperatures that are used in heat transfer modeling. Furthermore, the results from this study with twenty-four biopsy-proven cancer cases indicate that the detection of breast cancer is not affected by breast density. This study indicates the potential of the IRI-Numerical Engine as an effective adjunct to mammography. A large scale clinical study in a statistically significant sample size is needed before integrating this approach in the current protocol.

Imaging Molecular Targets and Metabolic Pathways in Breast Cancer for Improved Clinical Management: Current Practice and Future Perspectives

Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related deaths worldwide. Timely decision-making that enables implementation of the most appropriate therapy or therapies is essential for achieving the best clinical outcomes in breast cancer. While clinicopathologic characteristics and immunohistochemistry have traditionally been used in decision-making, these clinical and laboratory parameters may be difficult to ascertain or be equivocal due to tumor heterogeneity. Tumor heterogeneity is described as a phenomenon characterized by spatial or temporal phenotypic variations in tumor characteristics. Spatial variations occur within tumor lesions or between lesions at a single time point while temporal variations are seen as tumor lesions evolve with time. Due to limitations associated with immunohistochemistry (which requires invasive biopsies), whole-body molecular imaging tools such as standard-of-care [18F]FDG and [18F]FES PET/CT are indispensable in addressing this conundrum. Despite their proven utility, these standard-of-care imaging methods are often unable to image a myriad of other molecular pathways associated with breast cancer. This has stimulated interest in the development of novel radiopharmaceuticals targeting other molecular pathways and processes. In this review, we discuss validated and potential roles of these standard-of-care and novel molecular approaches. These approaches' relationships with patient clinicopathologic and immunohistochemical characteristics as well as their influence on patient management will be discussed in greater detail. This paper will also introduce and discuss the potential utility of novel PARP inhibitor-based radiopharmaceuticals as non-invasive biomarkers of PARP expression/upregulation.

A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification

There is a wide application of deep learning technique to unimodal medical image analysis with significant classification accuracy performance observed. However, real-world diagnosis of some chronic diseases such as breast cancer often require multimodal data streams with different modalities of visual and textual content. Mammography, magnetic resonance imaging (MRI) and image-guided breast biopsy represent a few of multimodal visual streams considered by physicians in isolating cases of breast cancer. Unfortunately, most studies applying deep learning techniques to solving classification problems in digital breast images have often narrowed their study to unimodal samples. This is understood considering the challenging nature of multimodal image abnormality classification where the fusion of high dimension heterogeneous features learned needs to be projected into a common representation space. This paper presents a novel deep learning approach combining a dual/twin convolutional neural network (TwinCNN) framework to address the challenge of breast cancer image classification from multi-modalities. First, modality-based feature learning was achieved by extracting both low and high levels features using the networks embedded with TwinCNN. Secondly, to address the notorious problem of high dimensionality associated with the extracted features, binary optimization method is adapted to effectively eliminate non-discriminant features in the search space. Furthermore, a novel method for feature fusion is applied to computationally leverage the ground-truth and predicted labels for each sample to enable multimodality classification. To evaluate the proposed method, digital mammography images and digital histopathology breast biopsy samples from benchmark datasets namely MIAS and BreakHis respectively. Experimental results obtained showed that the classification accuracy and area under the curve (AUC) for the single modalities yielded 0.755 and 0.861871 for histology, and 0.791 and 0.638 for mammography. Furthermore, the study investigated classification accuracy resulting from the fused feature method, and the result obtained showed that 0.977, 0.913, and 0.667 for histology, mammography, and multimodality respectively. The findings from the study confirmed that multimodal image classification based on combination of image features and predicted label improves performance. In addition, the contribution of the study shows that feature dimensionality reduction based on binary optimizer supports the elimination of non-discriminant features capable of bottle-necking the classifier.

Advances in Breast PET Instrumentation

Dedicated breast PET scanners currently have a spatial resolution in the 1.5 to 2 mm range, and the ability to provide tomographic images and quantitative data. They are also commercially available from a few vendors. A review of past and recent advances in the development and performance of dedicated breast PET scanners is summarized.

Breast Mass Detection and Classification Using Machine Learning Approaches on Two-Dimensional Mammogram: A Review

Breast cancer is a leading cause of mortality among women, both in India and globally. The prevalence of breast masses is notably common in women aged 20 to 60. These breast masses are classified, according to the breast imaging-reporting and data systems (BI-RADS) standard, into categories such as fibroadenoma, breast cysts, benign, and malignant masses. To aid in the diagnosis of breast disorders, imaging plays a vital role, with mammography being the most widely used modality for detecting breast abnormalities over the years. However, the process of identifying breast diseases through mammograms can be time-consuming, requiring experienced radiologists to review a significant volume of images. Early detection of breast masses is crucial for effective disease management, ultimately reducing mortality rates. To address this challenge, advancements in image processing techniques, specifically utilizing artificial intelligence (AI) and machine learning (ML), have tiled the way for the development of decision support systems. These systems assist radiologists in the accurate identification and classification of breast disorders. This paper presents a review of various studies where diverse machine learning approaches have been applied to digital mammograms. These approaches aim to identify breast masses and classify them into distinct subclasses such as normal, benign and malignant. Additionally, the paper highlights both the advantages and limitations of existing techniques, offering valuable insights for the benefit of future research endeavors in this critical area of medical imaging and breast health.

Ag nanorod@PEI-Ag nanohybrid as an excellent signal label for sensitive and rapid detection of serum HER2

The accurate detection of Human epidermal growth factor receptor-2 (HER2) as a critical breast cancer biomarker can be essential for the early selection of therapeutic approaches. HER2 is a prominent component of a signaling network. Overexpression of the HER2 protein due to amplification of its gene leads to the development of an aggressive subtype of breast cancer. Patients with tumors that overexpress HER2 are eligible for treatment that significantly reduces mortality rates. Herein, we present a fast and simple method for detecting serum HER2. A new electrochemical label has been developed using charged Ag nanorod@ polyethylenimine-Ag (Ag NR@ PEI-Ag) nanohybrid. The synthesized Ag NR@PEI-Ag nanohybrid simultaneously has the electroactive property of silver and the large surface area of the PEI, which results in the enhancement of the detection signal. So, using Ag NR@PEI-Ag nanohybrid as the electrochemical label, a simple, fast, and sensitive electrochemical biosensor was designed to detect HER2. This way, after immobilizing HER2 aptamer on the Au electrode surface, HER2 or human serum was exposed to the aptamer. Then, the positively charged Ag NR@PEI-Ag nanohybrid was adsorbed onto the negatively charged aptamer-HER2 complex, and the current that was produced due to the Ag/AgCl reaction was measured as the electrochemical signal. The aptasensor shows a broad linear response from 10-12 to 10-7 g, a low detection limit (LOD) of 10 pg, and a total assay time of ~ 30 min.

Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review

Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.

MicroRNA targeting: A novel therapeutic intervention for ovarian cancer

Ovarian cancer, a perilous form of cancer affecting the female reproductive system, exhibits intricate communication networks that contribute to its progression. This study aims to identify crucial molecular abnormalities linked to the disease to enhance diagnostic and therapeutic strategies. In particular, we investigate the role of microRNAs (miRNAs) as diagnostic biomarkers and explore their potential in treating ovarian cancer. By targeting miRNAs, which can influence multiple pathways and genes, substantial therapeutic benefits can be attained. In this review we want to shed light on the promising application of miRNA-based interventions and provide insights into the specific miRNAs implicated in ovarian cancer pathogenesis.

Ultrasound as a Method for Early Diagnosis of Breast Pathology

INTRODUCTION

Ultrasound is a non-invasive, low-cost technique that does not use ionising radiation and provides a "real-time" image, and for these reasons, this method is ideal in several situations.

PURPOSE

To demonstrate breast ultrasound evaluation as a first-line diagnostic method and to evaluate the variation of breast characteristics with age.

MATERIAL AND METHODS

A total of 105 women with a mean age of 30 years participated and were divided into three age groups: 18-39, 40-59, and 60-79 years, excluding participants subject to mastectomy. After completing the informed consent, all participants answered personal and sociodemographic questions, such as personal and family history, menstrual cycle, pregnancy, ultrasound, and mammography, among others. They were then submitted to a bilateral breast ultrasound examination. Subsequently, all the images and their data were analysed, and a technical report of the examination was given to all the participants.

RESULTS

A total of 105 women with a mean age of 30 years participated, 58 of whom underwent the examination for the first time. In 31, changes (of which only 7 were known) were diagnosed. It was verified that, according to age group, the density of the breast stroma varied; older women have less breast density.

CONCLUSIONS

Ultrasound is a good method for breast evaluation and can be considered important for the early evaluation of breast pathology and follow-up of the pathology.

Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.

Design of a Low-Cost Diffuse Optical Mammography System for Biomedical Image Processing in Breast Cancer Diagnosis

Worldwide, breast cancer is the most common type of cancer that mainly affects women. Several diagnosis techniques based on optical instrumentation and image analysis have been developed, and these are commonly used in conjunction with conventional diagnostic devices such as mammographs, ultrasound, and magnetic resonance imaging of the breast. The cost of using these instruments is increasing, and developing countries, whose deaths indices due to breast cancer are high, cannot access conventional diagnostic methods and have even less access to newer techniques. Other studies, based on the analysis of images acquired by traditional methods, require high resolutions and knowledge of the origin of the captures in order to avoid errors. For this reason, the design of a low-cost diffuse optical mammography system for biomedical image processing in breast cancer diagnosis is presented. The system combines the acquisition of breast tissue photographs, diffuse optical reflectance (as a biophotonics technique), and the processing of digital images for the study and diagnosis of breast cancer. The system was developed in the form of a medical examination table with a 638 nm red-light source, using light-emitted diode technology (LED) and a low-cost web camera for the acquisition of breast tissue images. The system is automatic, and its control, through a graphical user interface (GUI), saves costs and allows for the subsequent analysis of images using a digital image-processing algorithm. The results obtained allow for the possibility of planning in vivo measurements. In addition, the acquisition of images every 30° around the breast tissue could be used in future research in order to perform a three-dimensional (3D) reconstruction and an analysis of the captures through deep learning techniques. These could be combined with virtual, augmented, or mixed reality environments to predict the position of tumors, increase the likelihood of a correct medical diagnosis, and develop a training system for specialists. Furthermore, the system allows for the possibility to develop analysis of optical characterization for new phantom studies in breast cancer diagnosis through bioimaging techniques.

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Purpose

Histopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.

Design/methodology/approach

Three pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.

Findings

This study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.

Originality/value

The best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

The high dimensionality and sparsity of the microarray gene expression data make it challenging to analyze and screen the optimal subset of genes as predictors of breast cancer (BC). The authors in the present study propose a novel hybrid Feature Selection (FS) sequential framework involving minimum Redundancy-Maximum Relevance (mRMR), a two-tailed unpaired t-test, and meta-heuristics to screen the most optimal set of gene biomarkers as predictors for BC. The proposed framework identified a set of three most optimal gene biomarkers, namely, MAPK 1, APOBEC3B, and ENAH. In addition, the state-of-the-art supervised Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Net (NN), Naïve Bayes (NB), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR) were used to test the predictive capability of the selected gene biomarkers and select the most effective breast cancer diagnostic model with higher values of performance matrices. Our study found that the XGBoost-based model was the superior performer with an accuracy of 0.976 ± 0.027, an F1-Score of 0.974 ± 0.030, and an AUC value of 0.961 ± 0.035 when tested on an independent test dataset. The screened gene biomarkers-based classification system efficiently detects primary breast tumors from normal breast samples.

Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis

Mass detection in mammograms has a limited approach to the presence of a mass in overlapping denser fibroglandular breast regions. In addition, various breast density levels could decrease the learning system's ability to extract sufficient feature descriptors and may result in lower accuracy performance. Therefore, this study is proposing a textural-based image enhancement technique named Spatial-based Breast Density Enhancement for Mass Detection (SbBDEM) to boost textural features of the overlapped mass region based on the breast density level. This approach determines the optimal exposure threshold of the images' lower contrast limit and optimizes the parameters by selecting the best intensity factor guided by the best Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE) scores separately for both dense and non-dense breast classes prior to training. Meanwhile, a modified You Only Look Once v3 (YOLOv3) architecture is employed for mass detection by specifically assigning an extra number of higher-valued anchor boxes to the shallower detection head using the enhanced image. The experimental results show that the use of SbBDEM prior to training mass detection promotes superior performance with an increase in mean Average Precision (mAP) of 17.24% improvement over the non-enhanced trained image for mass detection, mass segmentation of 94.41% accuracy, and 96% accuracy for benign and malignant mass classification. Enhancing the mammogram images based on breast density is proven to increase the overall system's performance and can aid in an improved clinical diagnosis process.

Optimized S-Curve Transformation and Wavelets-Based Fusion for Contrast Elevation of Breast Tomograms and Mammograms

For the purpose of accuracy in detection and diagnosis, Computer-Aided Diagnosis (CAD) is preferred by radiologists for the analysis of Breast Cancer. However, the presence of noise, artifacts, and poor contrast in breast images during acquisition highlights the need for sophisticated enhancement techniques for the proper visualization of region-of-interest (ROI). In this work, contrast elevation of breast mammographic and tomographic images is performed with an improved S-Curve transform using the Particle Swarm Optimization (PSO) algorithm. The enhanced images are assessed using dedicated quality metrics such as the Enhancement Measure (EME) and Absolute Mean Brightness Error (AMBE) measurement. Although the enhancement techniques help in attaining better images, certain features relevant for diagnosis purposes are removed during the enhancement process, creating contradictions for radiological interpretation. Hence, to ensure the retention of diagnostic features from original breast tomograms and mammograms, a Discrete Wavelet Transform (DWT)-based fusion approach is incorporated, which fuses the original and contrast-enhanced images (with optimized s-curve transformation function) using the maximum fusion rule. The fusion performance is thereafter measured using the Image Quality Index (IQI), Standard Deviation (SD), and Entropy (E) as fusion metrics.

Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS-BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS-BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS-BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.

An extensive review of state-of-the-art transfer learning techniques used in medical imaging: Open issues and challenges

Deep learning techniques, which use a massive technology known as convolutional neural networks, have shown excellent results in a variety of areas, including image processing and interpretation. However, as the depth of these networks grows, so does the demand for a large amount of labeled data required to train these networks. In particular, the medical field suffers from a lack of images because the procedure for obtaining labeled medical images in the healthcare field is difficult, expensive, and requires specialized expertise to add labels to images. Moreover, the process may be prone to errors and time-consuming. Current research has revealed transfer learning as a viable solution to this problem. Transfer learning allows us to transfer knowledge gained from a previous process to improve and tackle a new problem. This study aims to conduct a comprehensive survey of recent studies that dealt with solving this problem and the most important metrics used to evaluate these methods. In addition, this study identifies problems in transfer learning techniques and highlights the problems of the medical dataset and potential problems that can be addressed in future research. According to our review, many researchers use pre-trained models on the Imagenet dataset (VGG16, ResNet, Inception v3) in many applications such as skin cancer, breast cancer, and diabetic retinopathy classification tasks. These techniques require further investigation of these models, due to training them on natural, non-medical images. In addition, many researchers use data augmentation techniques to expand their dataset and avoid overfitting. However, not enough studies have shown the effect of performance with or without data augmentation. Accuracy, recall, precision, F1 score, receiver operator characteristic curve, and area under the curve (AUC) were the most widely used measures in these studies. Furthermore, we identified problems in the datasets for melanoma and breast cancer and suggested corresponding solutions.

99mTc- Anionic dendrimer targeted vascular endothelial growth factor as a novel nano-radiotracer for in-vivo breast cancer imaging

Since breast cancer is the commonly cause of death among women around the world, diagnosis at the early stages is significantly important to prevent the metastasis of the cancer. Among the various growth factors that are involved in angiogenesis, vascular endothelial growth factor (VEGF) is believed to be the most important factor. Overexpressed VEGF receptor on tumors surface, is particularly interesting for cancer cells targeting purposes. In this study, citric acid dendrimer conjugated with VEGF antagonist peptide was synthesized. The obtained product was confirmed by FT-IR, TEM, DLS, and EDS. In vitro cytotoxicity assay showed no toxicity on normal cells and indicated the notably dose-dependence toxicity on cancer cells. Box-Behnken software as a computational method was used to determine the optimum amount of radiolabeling parameters. Optimized parameters for reducing agent, dendrimer-anti-VEGF, and time were 1.4 mg, 17.5 mg, and about 30 min respectively. Radiochemical purity of radio-labeled conjugated dendrimer was determined about 90 percent. SPECT imaging was done to observe the in vivo accumulation of dendrimer-anti-VEGF in the tumor site. Images showed high accumulation of radio-tracer in the tumor region. All in all, obtained results confirmed our hypothesis that the dendrimer-anti-VEGF can be a good radio-tracer for diagnosis of cancer.

A Survey on Human Cancer Categorization Based on Deep Learning

In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases. Convolutional neural networks (CNNs) detect the conventional visual features of disease diagnoses, e.g., lung, skin, brain, prostate, and breast cancer. A CNN has a procedure for perfectly investigating medicinal science images. This study assesses the main deep learning concepts relevant to medicinal image investigation and surveys several charities in the field. In addition, it covers the main categories of imaging procedures in medication. The survey comprises the usage of deep learning for object detection, classification, and human cancer categorization. In addition, the most popular cancer types have also been introduced. This article discusses the Vision-Based Deep Learning System among the dissimilar sorts of data mining techniques and networks. It then introduces the most extensively used DL network category, which is convolutional neural networks (CNNs) and investigates how CNN architectures have evolved. Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.

Breast Cancer—How Can Imaging Help?

Breast cancer is the most common malignant disease among women, causing death and suffering worldwide. It is known that, for the improvement of the survival rate and the psychological impact it has on patients, early detection is crucial. For this to happen, the imaging techniques should be used at their full potential. We selected and examined 44 articles that had as subject the use of a specific imaging method in breast cancer management (mammography, ultrasound, MRI, ultrasound-guided biopsy, PET-CT). After analyzing their data, we summarized and concluded which are the best ways to use each one of the mentioned techniques for a good outcome. We created a simplified algorithm with easy steps that can be followed by radiologists when facing this type of neoplasia.

An Electrochemical Immunosensor Based on Gold-Graphene Oxide Nanocomposites with Ionic Liquid for Detecting the Breast Cancer CD44 Biomarker

We develop a highly sensitive electrochemical immunosensor for the detection of a cluster of differentiation-44 (CD44) antigen, a breast cancer biomarker. The hybrid nanocomposite consists of graphene oxide, ionic liquid, and gold nanoparticles (GO-IL-AuNPs) immobilized on a glassy carbon electrode. GO favors the immobilization of antibodies because of the availability of oxygen functionalities. However, 1-butyl-3-methylimidazolium tetrafluoroborate (BMIM.BF₄) and AuNPs facilitate electron transfer and increase the effective surface area, which enhances the performance of the immunosensor. Furthermore, UV-visible, fourier transform infrared and Raman spectroscopy, X-ray diffraction, field emission scanning electron microscopy, transmission electron microscopy, voltammetry, and electrochemical impedance spectroscopy characterization techniques have been employed to investigate the structural and chemical properties of the nanomaterials. The quantitative detection of CD44 antigen has been accomplished via differential pulse voltammetry and EIS detection techniques. It has been quantified that the proposed immunosensor offers excellent detection ability in both phosphate-buffered saline (PBS) and serum samples. Under optimum conditions, the linear detection range of the immunosensor for CD44 antigen is 5.0 fg mL^-1 to 50.0 μg mL^-1 and the limit of detection is 2.0 and 1.90 fg mL^-1 as observed via DPV and EIS, respectively, in PBS. Additionally, the immunosensor has high sensitivity and specificity and can be successfully applied for the detection of CD44 antigen in clinical samples.

BC-DETECT: combined detection of serum HE4 and TFF3 improves breast cancer diagnostic efficacy

BACKGROUND

Early accurate breast cancer (BC) diagnosis is critical in disease management. Mammography has been widely used. However, its radiation, and high false-negative and -positive results have always been a concern. We evaluated combined detection of human epididymal protein 4 (HE4) and trefoil factor 3 (TFF3) as substitute method to enhance BC diagnosis.

METHODS

HE4 and TFF3 blood levels were determined by ELISA in sera of 120 BC patients and 80 women (40 healthy and 40 benign breast disease) as controls. Receiver-operating characteristic curve was applied for evaluation diagnostic power of each biomarker and their combination.

RESULTS

In BC patients, serum HE4 [5 (2-11.9) vs. 3.1 (1.8-5.4) and 1 (1-3.5); P = 0.022] and TFF3 [5.3 (4.5-6.7) vs. 4.7 (4-4.8) and 3.9 (3-4.4); P = 0.027] were significantly higher than that in benign and healthy groups, respectively. Both HE4 (AUC = 0.783; P < 0.0001) and TFF3 (AUC = 0.759; P < 0.0001) had superior BC diagnostic ability compared to CEA and CA-15.3. Logistic regression analysis revealed simplified index BC-DETECT = HE4 + TFF3, and its values were significantly (P = 0.0132) elevated in BC (10.9 (8.4-17.2) compared to benign (7.2 (5.4-10.1)) and healthy (5.1 (4-6.3)) controls. AUC of BC-DETECT for BC prediction was (AUC = 0.850; P < 0.0001) with sensitivity, specificity, and positive and negative predictive values and accuracy of 84.2%, 70%, 80.8%, 74.7%, and 78.5%, respectively. High BC-DETECT levels were associated with tumor non-luminal subtypes, late stage, high grade, large size, lymph-node invasion, and multiple lesions.

CONCLUSIONS

BC-DETECT is inexpensive, rapid, and easy to perform and reliably guides BC early detection. Moreover, the association between elevated BC-DETECT values and disease severity may propose its potential role as prognostic marker.

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences

Breast cancer needs to be detected early to reduce mortality rate. Ultrasound imaging (US) could significantly enhance diagnosing cases with dense breasts. Most of the existing computer-aided diagnosis (CAD) systems employ a single ultrasound image for the breast tumor to extract features to classify it as benign or malignant. However, the accuracy of such CAD system is limited due to the large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio in ultrasound images due to their noisy nature and the significant similarity between normal and abnormal tissues. To handle these issues, we propose a deep-learning-based radiomics method based on breast US sequences in this paper. The proposed approach involves three main components: radiomic features extraction based on a deep learning network, so-called ConvNeXt, a malignancy score pooling mechanism, and visual interpretations. Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. We also propose an efficient pooling mechanism to fuse the malignancy scores of each breast US sequence frame based on image-quality statistics. The ablation study and experimental results demonstrate that our method achieves competitive results compared to other CNN-based methods.

A modified source-detector configuration for the discrimination between normal and diseased human breast based on the continuous-wave diffuse optical imaging approach: a simulation study

Breast tumors are among the most common types of tumors that can affect both genders. It may spread to the whole breast without any symptoms. Therefore, the early detection and accurate diagnosis of breast tumors are significantly important. Current approaches for breast cancer screening such as positron emission tomography (PET) and magnetic resonance imaging (MRI) have some limitations of being time and money-consuming. In addition, mammography screening is not recommended for women under forty. Consequently, optical techniques have been introduced as safe and functional alternatives. Diffuse optical imaging is a non-invasive imaging technique that utilizes near-infrared light to examine biological tissues based on measuring the optical transmission and/or reflection at various locations on the tissue surface. In this paper, we propose a modified arrangement between the laser source and the detectors for distinguishing tumors from normal breast tissue. A three-dimensional model of the normal human breast with three types of tumors is developed using a COMSOL simulation software based on the finite element solution of Helmholtz equation to estimate optical fluence distribution. The breast model consists of four layers: skin, fat, glandular, and muscle, where the tumor is included in the glandular layer. Different wavelengths were used to determine the most proper wavelength for the discrimination between the normal tissue and tumor. The obtained results were verified using the receiver operating characteristic (ROC) method. The resultant fluence images show different features between normal breast and breast with tumor especially using 600-nm incident laser as demonstrated by the obtained ROC curves.

Sensing mammographic density using single-sided portable Nuclear Magnetic Resonance

This research paper presents a quantitative approach to sensing mammographic density (MD) using single-sided portable Nuclear Magnetic Resonance (NMR). It focuses on three main techniques: spin-lattice relaxation (recovery) time (T1), spin-spin relaxation (decay) time (T2), and Diffusion (D) techniques by testing whether or not the aforementioned techniques are in agreement with the gold standard and with each other when used for scanning breast tissue specimens with a variety of mammographic densities (MDs). The high mammographic density (HMD), intermediate MD, and low mammographic density (LMD) regions of each slice were identified according to the mammogram images. Subsequently, the grayscale values for these regions were quantified. One region was measured from the first sample while the remaining ones were measured from the second sample. The same areas were then exposed to portable NMR, and the sequences used as following: the stimulated echo sequence for diffusion (D), the Carr-Purcell-Meiboom-Gill (CPMG) sequence for T2, and saturation recovery sequence for T1. The correlations between the grayscale values and NMR techniques were strongly correlated. The Pearson correlation coefficient, R, of T1 (%) versus grayscale value, D (%) versus grayscale value, and T2 (%) versus grayscale value, was 0.91, 0.91, and 0.93, respectively. Furthermore, the relative water content of the breast slices based on T1, T2, and diffusion (D) measurements were strongly in agreement with each other. The Pearson correlation coefficient, R, of D (%) versus T1 (%), D (%) versus T2 (%), and T1 (%) versus T2 (%), was 0.984, 0.966, and 0.9868, respectively. The three pulse sequences can be employed in a portable NMR device to deliver continuous quantitative measurements of MD in breast tissue samples. As a result, the method demonstrated to be acceptable for determining the distribution of MDs among breast tissue samples without the need for additional qualitative analysis.

Consistency Analysis of CTLM Imaging and Mammography in the Diagnosis of Breast Tumor Lesions

Objective. To analyze the consistency of preoperative CTLM imaging in the diagnosis of breast cancer lesions and postoperative pathological examination. Methods. The clinical data of 225 patients with breast tumor in our breast surgery department were collected. All patients underwent mammography, CTLM, and pathological examination. To analyze the image characteristics of breast CTLM imaging, calculate the diagnostic efficacy of CTLM imaging for breast tumors, and compare the image characteristics of CTLM imaging for benign and malignant tumors. Results. (1) Postoperative pathological examination showed that 136 cases (60.44%) of lesions were benign tumors, and 89 cases (39.56%) were malignant tumors. (2) The “spokes distribution” of normal breast CTLM images was interrupted. In the 3D reconstructed images, the morphology of the abnormal angiogenesis area is mostly irregular nonbanded structure, which is manifested as slab structure, spindle structure, spherical structure, diverticulum structure, inverted conical structure, rings structure, branched structure, and dumbbell structure. (3) The detection rate of breast tumor by CTLM imaging was 84.44%. The specificity and coincidence rate of CTLM imaging were higher than that of mammography (P < 0.05). (4) The features of CTLM imaging images of breast malignant tumors are mostly bright white locally, with irregular edges and obvious attenuation of laser signal, and the reconstructed shape of 3D images is mostly like a slab structure. Conclusion. CTLM imaging can provide related information of neovascularization in breast cancer lesions, which is basically consistent with pathologically confirmed lesions.

PEGylation of Metal Oxide Nanoparticles Modulates Neutrophil Extracellular Trap Formation

Novel metal oxide nanoparticle (NP) contrast agents may offer safety and functionality advantages over conventional gadolinium-based contrast agents (GBCAs) for cancer diagnosis by magnetic resonance imaging. However, little is known about the behavior of metal oxide NPs, or of their effect, upon coming into contact with the innate immune system. As neutrophils are the body's first line of defense, we sought to understand how manganese oxide and iron oxide NPs impact leukocyte functionality. Specifically, we evaluated whether contrast agents caused neutrophils to release web-like fibers of DNA known as neutrophil extracellular traps (NETs), which are known to enhance metastasis and thrombosis in cancer patients. Murine neutrophils were treated with GBCA, bare manganese oxide or iron oxide NPs, or poly(lactic-co-glycolic acid) (PLGA)-coated metal oxide NPs with different incorporated levels of poly(ethylene glycol) (PEG). Manganese oxide NPs elicited the highest NETosis rates and had enhanced neutrophil uptake properties compared to iron oxide NPs. Interestingly, NPs with low levels of PEGylation produced more NETs than those with higher PEGylation. Despite generating a low rate of NETosis, GBCA altered neutrophil cytokine expression more than NP treatments. This study is the first to investigate whether manganese oxide NPs and GBCAs modulate NETosis and reveals that contrast agents may have unintended off-target effects which warrant further investigation.

Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review

The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.

Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review

Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.

The utility of radiolabeled PSMA ligands for tumor imaging

Prostate-specific membrane antigen (PSMA) is a glycosylated type-II transmembrane protein expressed in prostatic tissue and significantly overexpressed in several prostate cancer cells. Despite its name, PSMA has also been reported to be overexpressed in endothelial cells of benign and malignant non-prostate disease. So its clinical use was extended to detection, staging, and therapy of various tumor types. Recently small molecules targeting PSMA have been developed as imaging probes for diagnosis of several malignancies. Preliminary studies are emerging improved diagnostic sensitivity and specificity of PSMA imaging, leading to a change in patient management. In this review, we evaluated the first preclinical and clinical studies on PSMA ligands resulting future perspectives radiolabeled PSMA in staging and molecular characterization, based on histopathologic examinations of PSMA expression.

Novel manganese carbon quantum dots as a nano-probe: Facile synthesis, characterization and their application in naproxen delivery (Mn/CQD/SiO2@naproxen)

This study for the first time pursues two crucial aims of using Naproxen as a non-steroidal anti-inflammatory drug in a better, non-invasive setting and introducing a simple and biocompatible nano-carrier (Mn/CQD/SiO₂) which is a magneto carbon quantum dots modified with mesoporous silica probe which can be served as a drug delivery and tracer system. SiO₂modification was doneby mesoporous silica which improves biocompatibility and provideslow cytotoxicity. Naproxen was conjugated to the nano-probe to form Mn/CQD/SiO₂@naproxen and biodistribution was investigated. Physicochemical characteristics of the Mn/CQD/SiO₂@naproxen were investigated using FT-IR, SEM, TEM, UV-Vis and BET. Antiproliferation assay using MTT assay was performed on HEK-293 cells to determine the cytotoxity of Mn/CQD/SiO₂@naproxen. Relaxivity of Mn/CQD/SiO2 was examined thereafter. To investigate the imaging capability of Mn/CQD/SiO₂@naproxen and biodistribution of Naproxen, fluorescent imaging was done. To confirm the data, then the levels of COX Gene expression was determined. The specific surface area, pore volume, and pore radius were 44.4 m²/g, 10.23 cm³/g, and 25.9 nm respectively. MTT assay showed no cytotoxicity. Relaxivity of Mn/CQD/SiO₂ was higher than conventional Gd-based contrast agent. Fluorescence imaging of Mn/CQD/SiO₂@naproxen showed the biodistribution of naproxen. COX Gene expression confirmed the biodistribution data. By increasing the accumulation in liver COX production reduced. All in all, unique features of Mn/CQD/SiO₂ including biocompatibility, low toxicity, magnetic and fluorescence properties showed that it can be used in biomedical sciences.

Current State of Breast Cancer Diagnosis, Treatment, and Theranostics

Breast cancer is one of the leading causes of cancer-related morbidity and mortality in women worldwide. Early diagnosis and effective treatment of all types of cancers are crucial for a positive prognosis. Patients with small tumor sizes at the time of their diagnosis have a significantly higher survival rate and a significantly reduced probability of the cancer being fatal. Therefore, many novel technologies are being developed for early detection of primary tumors, as well as distant metastases and recurrent disease, for effective breast cancer management. Theranostics has emerged as a new paradigm for the simultaneous diagnosis, imaging, and treatment of cancers. It has the potential to provide timely and improved patient care via personalized therapy. In nanotheranostics, cell-specific targeting moieties, imaging agents, and therapeutic agents can be embedded within a single formulation for effective treatment. In this review, we will highlight the different diagnosis techniques and treatment strategies for breast cancer management and explore recent advances in breast cancer theranostics. Our main focus will be to summarize recent trends and technologies in breast cancer diagnosis and treatment as reported in recent research papers and patents and discuss future perspectives for effective breast cancer therapy.