Early Diagnosis of Breast Cancer

Simultaneous detection of breast cancer biomarkers circROBO1 and BRCA1 based on a CRISPR-Cas13a/Cas12a system

Breast cancer is reported to be one of the most lethal cancers in women, and its multi-target detection can help improve the accuracy of diagnosis. In this work, a cluster regularly interspaced short palindromic repeats (CRISPR)-Cas13a/Cas12a-based system was established for the simultaneous fluorescence detection of breast cancer biomarkers circROBO1 and BRCA1. CRISPR-Cas13a and CRISPR-Cas12a were directly activated by their respective targets, resulting in the cleavage of short RNA and DNA reporters, respectively, thus the signals of 6-carboxyfluorescein (FAM) and 6-carboxy-xrhodamine (ROX) were restored. As the fluorescence intensities of FAM and ROX were dependent on the concentrations of circROBO1 and BRCA1, respectively, synchronous fluorescence scanning could achieve one-step detection of circROBO1 and BRCA1 with detection limits of 0.013 pM and 0.26 pM, respectively. The system was highly sensitive and specific, holding high diagnostic potential for the detection of clinical samples. Furthermore, the competing endogenous RNA mechanism between circROBO1 and BRCA1 was also explored, providing a reliable basis for the intrinsic regulatory mechanism of breast cancer.

Expression and Clinical Significance of NUDCD1, PI3K/AKT/mTOR Signaling Pathway-Related Molecules and Immune Infiltration in Breast Cancer

BACKGROUND

NUDCD1 (NudC Domain Containing 1) performs an essential function in biological processes such as cell progression, migration, cell cycle, and intracellular material transport. Many solid tumors express it highly, which is a prospective biomarker and therapeutic approach. However, the expression and clinical importance of NUDCD1 across breast cancer is unclear.

METHODS

The expressions of NUDCD1 in breast cancers and normal breast tissues were studied utilizing the TIMER database and immunohistochemical analysis. Subsequently, we validate the association between the expression of NUDCD1 and clinicopathologic features and prognosis of breast cancer. The immunohistochemical experiments of pathway-related molecules were done on 214 breast cancer tissue microarrays. The investigation of correlation between NUDCD1 expression and tumor immune infiltration was subsequently conducted.

RESULTS

Through the utilization of bioinformatics analysis and immunohistochemical experiments, it was determined that NUDCD1 exhibited upregulation within breast cancer. Furthermore, it was discovered that an elevated expression of NUDCD1 may potentially be linked to a worse prognosis in breast cancer. Our study reveals that the PI3K/AKT/mTOR signaling pathway may perform a function in NUDCD1 regulating breast cancer progression via enrichment analysis. Furthermore, the expression of NUDCD1 may be associated with the degree of immunological infiltration.

CONCLUSION

The expression of NUDCD1 was explored to be elevated in breast cancer and was observed to be correlated with a poorer prognosis. p-AKT, PI3K, AKT, mTOR, and p-mTOR expression levels underwent significant elevation in breast cancer. The function of NUDCD1 within breast cancer might be associated with the PI3K/AKT/mTOR signaling pathway.

Mass detection in automated three dimensional breast ultrasound using cascaded convolutional neural networks

PURPOSE

Early detection of breast cancer has a significant effect on reducing its mortality rate. For this purpose, automated three-dimensional breast ultrasound (3-D ABUS) has been recently used alongside mammography. The 3-D volume produced in this imaging system includes many slices. The radiologist must review all the slices to find the mass, a time-consuming task with a high probability of mistakes. Therefore, many computer-aided detection (CADe) systems have been developed to assist radiologists in this task. In this paper, we propose a novel CADe system for mass detection in 3-D ABUS images.

METHODS

The proposed system includes two cascaded convolutional neural networks. The goal of the first network is to achieve the highest possible sensitivity, and the second network's goal is to reduce false positives while maintaining high sensitivity. In both networks, an improved version of 3-D U-Net architecture is utilized in which two types of modified Inception modules are used in the encoder section. In the second network, new attention units are also added to the skip connections that receive the results of the first network as saliency maps.

RESULTS

The system was evaluated on a dataset containing 60 3-D ABUS volumes from 43 patients and 55 masses. A sensitivity of 91.48% and a mean false positive of 8.85 per patient were achieved.

CONCLUSIONS

The suggested mass detection system is fully automatic without any user interaction. The results indicate that the sensitivity and the mean FP per patient of the CADe system outperform competing techniques.

Colorimetric detection of circulating tumor cells in breast cancer based on ladder-branch hybridization chain reaction and DFs/AuNCs nanozyme

Breast cancer is the most common malignant tumor in women, which accounts for 6.9% of all cancer-related deaths. Early diagnosis is crucial for making the best clinical decision and improving the prognosis of patients. Circulating tumor cells (CTCs) have been regarded as significant tumor biomarkers. Herein, we designed a colorimetric biosensor for breast cancer CTCs quantification based on ladder-branch hybridization chain reaction (HCR) and DNA flowers/gold nanoclusters (DFs/AuNCs) nanozyme. With the assistance of complementary DNA labeled on magnetic beads (MBs), the cleavage products of RNA-cleaving DNAzymes (RCDs) could be rapidly captured, subsequently triggering ladder-branch HCR. In addition, the DFs/AuNCs nanozyme was applied for colorimetric analysis, which further improved the sensitivity for the detection of target CTCs. Benefiting from specific RCDs, ladder-branch HCR and DFs/AuNCs, we achieved a superior detection limit of 3 cells/mL as well as a broad linear range of 10 cells/mL to 104 cells/mL. Conclusively, this colorimetric biosensor achieved sensitively and selectively detection of breast cancer CTCs without the participation of enzymes at room temperature, which might provide new insight into the early detection of breast cancer.

Biosynthesized metallic nanoparticles: A new era in cancer therapy

Cancer remains a global health crisis, claiming countless lives throughout the years. Traditional cancer treatments like chemotherapy and radiation often bring about severe side effects, underscoring the pressing need for innovative, more efficient, and less toxic therapies. Nanotechnology has emerged as a promising technology capable of producing environmentally friendly anticancer nanoparticles. Among various nanoparticle types, metal-based nanoparticles stand out due to their exceptional performance and ease of use in methods of imaging. The widespread accessibility of biological precursors for synthesis based on plants of metal nanoparticles has made large-scale, eco-friendly production feasible. This evaluation provides a summary of the green strategy for synthesizing metal-based nanoparticles and explores their applications. Moreover, this review delves into the potential of phyto-based metal nanoparticles in combating cancer, shedding light on their probable mechanisms of action. These insights are invaluable for enhancing both biomedical and environmental applications. The study also touches on the numerous potential applications of nanotechnology in the field of medicine. Consequently, this research offers a concise and well-structured summary of nanotechnology, which should prove beneficial to researchers, engineers, and scientists embarking on future research endeavors.

Emerging Trends in Nanomedicine: Carbon-Based Nanomaterials for Healthcare

Carbon-based nanomaterials, such as carbon quantum dots (CQDs) and carbon 2D nanosheets (graphene, graphene oxide, and graphdiyne), have shown remarkable potential in various biological applications. CQDs offer tunable photoluminescence and excellent biocompatibility, making them suitable for bioimaging, drug delivery, biosensing, and photodynamic therapy. Additionally, CQDs' unique properties enable bioimaging-guided therapy and targeted imaging of biomolecules. On the other hand, carbon 2D nanosheets exhibit exceptional physicochemical attributes, with graphene excelling in biosensing and bioimaging, also in drug delivery and antimicrobial applications, and graphdiyne in tissue engineering. Their properties, such as tunable porosity and high surface area, contribute to controlled drug release and enhanced tissue regeneration. However, challenges, including long-term biocompatibility and large-scale synthesis, necessitate further research. Potential future directions encompass theranostics, immunomodulation, neural interfaces, bioelectronic medicine, and expanding bioimaging capabilities. In summary, both CQDs and carbon 2D nanosheets hold promise to revolutionize biomedical sciences, offering innovative solutions and improved therapies in diverse biological contexts. Addressing current challenges will unlock their full potential and can shape the future of medicine and biotechnology.

Breast Cancer Molecular Subtype Prediction: A Mammography-Based AI Approach

Breast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy-a specialized, expensive, and time-consuming procedure, often yielding to results that must be supported with additional biopsies due to technique errors or tumor heterogeneity. This study introduces a novel approach for predicting breast cancer molecular subtypes using mammography images and advanced artificial intelligence (AI) methodologies. Using the OPTIMAM imaging database, 1397 images from 660 patients were selected. The pretrained deep learning model ResNet-101 was employed to classify tumors into five subtypes: Luminal A, Luminal B1, Luminal B2, HER2, and Triple Negative. Various classification strategies were studied: binary classifications (one vs. all others, specific combinations) and multi-class classification (evaluating all subtypes simultaneously). To address imbalanced data, strategies like oversampling, undersampling, and data augmentation were explored. Performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Binary classification results showed a maximum average accuracy and AUC of 79.02% and 64.69%, respectively, while multi-class classification achieved an average AUC of 60.62% with oversampling and data augmentation. The most notable binary classification was HER2 vs. non-HER2, with an accuracy of 89.79% and an AUC of 73.31%. Binary classification for specific combinations of subtypes revealed an accuracy of 76.42% for HER2 vs. Luminal A and an AUC of 73.04% for HER2 vs. Luminal B1. These findings highlight the potential of mammography-based AI for non-invasive breast cancer subtype prediction, offering a promising alternative to biopsies and paving the way for personalized treatment plans.

Alginate-Chitosan Biodegradable and Biocompatible Based Hydrogel for Breast Cancer Immunotherapy and Diagnosis: A Comprehensive Review

Breast cancer is the most common type of cancer and the second leading cause of cancer-related mortality in females. There are many side effects due to chemotherapy and traditional surgery, like fatigue, loss of appetite, skin irritation, and drug resistance to cancer cells. Immunotherapy has become a hopeful approach toward cancer treatment, generating long-lasting immune responses in malignant tumor patients. Recently, hydrogel has received more attention toward cancer therapy due to its specific characteristics, such as decreased toxicity, fewer side effects, and better biocompatibility drug delivery to the particular tumor location. Researchers globally reported various investigations on hydrogel research for tumor diagnosis. The hydrogel-based multilayer platform with controlled nanostructure has received more attention for its antitumor effect. Chitosan and alginate play a leading role in the formation of the cross-link in a hydrogel. Also, they help in the stability of the hydrogel. This review discusses the properties, preparation, biocompatibility, and bioavailability of various research and clinical approaches of the multipolymer hydrogel made of alginate and chitosan for breast cancer treatment. With a focus on cases of breast cancer and the recovery rate, there is a need to find out the role of hydrogel in drug delivery for breast cancer treatment.

Critical review of single-cell mechanotyping approaches for biomedical applications

Accurate mechanical measurements of cells has the potential to improve diagnostics, therapeutics and advance understanding of disease mechanisms, where high-resolution mechanical information can be measured by deforming individual cells. Here we evaluate recently developed techniques for measuring cell-scale stiffness properties; while many such techniques have been developed, much of the work examining single-cell stiffness is impacted by difficulties in standardization and comparability, giving rise to large variations in reported mechanical moduli. We highlight the role of underlying mechanical theories driving this variability, and note opportunities to develop novel mechanotyping devices and theoretical models that facilitate convenient and accurate mechanical characterisation. Moreover, many high-throughput approaches are confounded by factors including cell size, surface friction, natural population heterogeneity and convolution of elastic and viscous contributions to cell deformability. We nevertheless identify key approaches based on deformability cytometry as a promising direction for further development, where both high-throughput and accurate single-cell resolutions can be realized.

Precision in cancer diagnostics: ultra-sensitive detection of MCF-7 breast cancer cells by gold nanostructure-enhanced electrochemical biosensing

Timely identification of cancers is pivotal in optimizing treatment efficacy and reducing their widespread impact. This study introduces a novel biosensor for the sensitive electrochemical detection of cancer cells overexpressing mucin 1 (MUC1), a well-established model for breast cancer. The sensor substrate comprises gold columnar nanostructures obtained through glancing angle deposition (GLAD) of copper nanostructures, subsequently replaced by gold via a facile galvanic replacement process. Functionalizing these gold nanostructures with aptamers targeting the MUC1 glycoproteins, a prominent cancer biomarker, enables specific recognition of MCF-7 breast cancer cells. The proposed electrochemical sensing platform offers several advantages, including high selectivity, a wide linear range of detection, a low detection limit of 30 cells per mL, and long-term stability, rendering this sensor highly desirable for definitive breast cancer diagnosis.

Analysis of polarization features of human breast cancer tissue by Mueller matrix visualization

Significance

Breast cancer ranks second in the world in terms of the number of women diagnosed. Effective methods for its early-stage detection are critical for facilitating timely intervention and lowering the mortality rate.

Aim

Polarimetry provides much useful information on the structural properties of breast cancer tissue samples and is a valuable diagnostic tool. The present study classifies human breast tissue samples as healthy or cancerous utilizing a surface-illuminated backscatter polarization imaging technique.

Approach

The viability of the proposed approach is demonstrated using 95 breast tissue samples, including 35 healthy samples, 20 benign cancer samples, 20 grade-2 malignant samples, and 20 grade-3 malignant samples.

Results

The observation results reveal that element m 23 in the Mueller matrix of the healthy samples has a deeper color and greater intensity than that in the breast cancer samples. Conversely, element m 32 shows a lighter color and reduced intensity. Finally, element m 44 has a darker color in the healthy samples than in the cancer samples. The analysis of variance test results and frequency distribution histograms confirm that elements m 23 , m 32 , and m 44 provide an effective means of detecting and classifying human breast tissue samples.

Conclusions

Overall, the results indicate that surface-illuminated backscatter polarization imaging has significant potential as an assistive tool for breast cancer diagnosis and classification.

Investigating the Difference in Quality of Life Between Immediate and Delayed Breast Cancer Reconstruction: A Systematic Review

Background/Aim

Immediate and delayed breast reconstruction remains a controversial topic. Apart from the clinical outcomes that have to be considered, there is a discrepancy of opinions regarding the potential differences in quality of life. It is essential to clarify this issue, in order to contribute to the reconstruction of the decision-making process. The aim of the study was to investigate potential differences in quality of life between immediate and delayed breast reconstruction patients.

Materials and Methods

A systematic review of the literature was carried out, searching for case-control studies that have comparatively examined the effects of delayed and immediate reconstruction on quality of life. A literature search was carried out using the Medline, Scopus and Web of Science databases. The quality of the studies was evaluated using the STROBE checklist for case-control studies.

Results

Three studies were found showing that immediate reconstruction led to benefits for the quality of life of patients, while one study showed the opposite. A relevant study revealed no statistically significant differences between the two groups, while another study, showed that immediate reconstruction entailed short-term benefits to quality of life. Four of the studies had debatable methodological approaches.

Conclusion

The inconsistent study findings cannot lead to any reliable conclusions regarding differences between immediate and delayed reconstruction in quality of life. It is possible that the results vary due to the time point that the comparisons made. Future research to this direction is warranted.

P53 Gene as a Promising Biomarker and Potential Target for the Early Diagnosis of Reproductive Cancers

One of the crucial aspects of cancer research is diagnosis with specificity and accuracy. Early cancer detection mostly helps make appropriate decisions regarding treatment and metastasis. The well-studied transcription factor tumor suppressor protein p53 is essential for maintaining genetic integrity. p53 is a key tumor suppressor that recognizes the carcinogenic biological pathways and eradicates them by apoptosis. A wide range of carcinomas, especially gynecological such as ovarian, cervical, and endometrial cancers, frequently undergo TP53 gene mutations. This study evaluates the potential of the p53 gene as a biological marker for the diagnosis of reproductive system neoplasms. Immunohistochemistry of p53 is rapid, easy to accomplish, cost-effective, and preferred by pathologists as a surrogate for the analysis of TP53 mutation. Thus, this review lays a groundwork for future efforts to develop techniques using p53 for the early diagnosis of cancer.

Multiplex Digital Spatial Profiling in Breast Cancer Research: State-of-the-Art Technologies and Applications across the Translational Science Spectrum

While RNA sequencing and multi-omic approaches have significantly advanced cancer diagnosis and treatment, their limitation in preserving critical spatial information has been a notable drawback. This spatial context is essential for understanding cellular interactions and tissue dynamics. Multiplex digital spatial profiling (MDSP) technologies overcome this limitation by enabling the simultaneous analysis of transcriptome and proteome data within the intact spatial architecture of tissues. In breast cancer research, MDSP has emerged as a promising tool, revealing complex biological questions related to disease evolution, identifying biomarkers, and discovering drug targets. This review highlights the potential of MDSP to revolutionize clinical applications, ranging from risk assessment and diagnostics to prognostics, patient monitoring, and the customization of treatment strategies, including clinical trial guidance. We discuss the major MDSP techniques, their applications in breast cancer research, and their integration in clinical practice, addressing both their potential and current limitations. Emphasizing the strategic use of MDSP in risk stratification for women with benign breast disease, we also highlight its transformative potential in reshaping the landscape of breast cancer research and treatment.

3D amide proton transfer-weighted imaging may be useful for diagnosing early-stage breast cancer: a prospective monocentric study

BACKGROUND

We investigated the value of three-dimensional amide proton transfer-weighted imaging (3D-APTWI) in the diagnosis of early-stage breast cancer (BC) and its correlation with the immunohistochemical characteristics of malignant lesions.

METHODS

Seventy-eight women underwent APTWI and dynamic contrast-enhanced (DCE)-MRI. Pathological results were categorized as either benign (n = 43) or malignant (n = 37) lesions. The parameters of APTWI and DCE-MRI were compared between the benign and malignant groups. The diagnostic value of 3D-APTWI was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) to establish a diagnostic threshold. Pearson's correlation was used to analyze the correlation between the magnetization transfer asymmetry (MTRasym) and immunohistochemical characteristics.

RESULTS

The MTRasym and time-to-peak of malignancies were significantly lower than those of benign lesions (all p < 0.010). The volume transfer constant, rate constant, and wash-in and wash-out rates of malignancies were all significantly greater than those of benign lesions (all p < 0.010). ROC-AUCs of 3D-APTWI, DCE-MRI, and 3D-APTWI+DCE to differential diagnosis between early-stage BC and benign lesions were 0.816, 0.745, and 0.858, respectively. Only the difference between AUCAPT+DCE and AUCDCE was significant (p < 0.010). When a threshold of MTRasym for malignancy for 2.42%, the sensitivity and specificity of 3D-APTWI for BC diagnosis were 86.5% and 67.6%, respectively; MTRasym was modestly positively correlated with pathological grade (r = 0.476, p = 0.003) and Ki-67 (r = 0.419, p = 0.020).

CONCLUSIONS

3D-APTWI may be used as a supplementary method for patients with contraindications of DCE-MRI. MTRasym can imply the proliferation activities of early-stage BC.

RELEVANCE STATEMENT

3D-APTWI can be an alternative diagnostic method for patients with early-stage BC who are not suitable for contrast injection.

KEY POINTS

• 3D-APTWI reflects the changes in the microenvironment of early-stage breast cancer. • Combined 3D-APTWI is superior to DCE-MRI alone for early-stage breast cancer diagnosis. • 3D-APTWI improves the diagnostic accuracy of early-stage breast cancer.

A novel visible and near-infrared hyperspectral imaging platform for automated breast-cancer detection

BACKGROUND

Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early and accurate detection is crucial for improving patient outcomes. Our study utilizes Visible and Near-Infrared Hyperspectral Imaging (VIS-NIR HSI), a promising non-invasive technique, to detect cancerous regions in ex-vivo breast specimens based on their hyperspectral response.

METHODS

In this paper, we present a novel HSI platform integrated with fuzzy c-means clustering for automated breast cancer detection. We acquire hyperspectral data from breast tissue samples, and preprocess it to reduce noise and enhance hyperspectral features. Fuzzy c-means clustering is then applied to segment cancerous regions based on their spectral characteristics.

RESULTS

Our approach demonstrates promising results. We evaluated the quality of the clustering using metrics like Silhouette Index (SI), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The clustering metrics results revealed an optimal number of 6 clusters for breast tissue classification, and the SI values ranged from 0.68 to 0.72, indicating well-separated clusters. Moreover, the CHI values showed that the clusters were well-defined, and the DBI values demonstrated low cluster dispersion. Additionally, the sensitivity, specificity, and accuracy of our system were evaluated on a dataset of breast tissue samples. We achieved an average sensitivity of 96.83%, specificity of 93.39%, and accuracy of 95.12%. These results indicate the effectiveness of our HSI-based approach in distinguishing cancerous and non-cancerous regions.

CONCLUSIONS

The paper introduces a robust hyperspectral imaging platform coupled with fuzzy c-means clustering for automated breast cancer detection. The clustering metrics results support the reliability of our approach in effectively segmenting breast tissue samples. In addition, the system shows high sensitivity and specificity, making it a valuable tool for early-stage breast cancer diagnosis. This innovative approach holds great potential for improving breast cancer screening and, thereby, enhancing our understanding of the disease and its detection patterns.

Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review

Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited imaging resources. This review assesses the feasibility of employing breast ultrasound as the primary breast cancer screening method, particularly in resource-constrained regions. Following the PRISMA guidelines, this study examines 52 publications from the last five years. Breast ultrasound, distinct from mammography, offers advantages like radiation-free imaging, suitability for repeated screenings, and preference for younger populations. Real-time imaging and dense breast tissue evaluation enhance sensitivity, accessibility, and cost-effectiveness. However, limitations include reduced specificity, operator dependence, and challenges in detecting microcalcifications. Automatic breast ultrasound (ABUS) addresses some issues but faces constraints like potential inaccuracies and limited microcalcification detection. The analysis underscores the need for a comprehensive approach to breast cancer screening, emphasizing international collaboration and addressing limitations, especially in resource-constrained settings. Despite advancements, notably with ABUS, the primary goal is to contribute insights for optimizing breast cancer screening globally, improving outcomes, and mitigating the impact of this debilitating disease.

Biomarker-specific biosensors revolutionise breast cancer diagnosis

Breast cancer is the most common cancer among women across the globe. In order to treat breast cancer successfully, it is crucial to conduct a comprehensive assessment of the condition during its initial stages. Although mammogram screening has long been a common method of breast cancer screening, high rates of type I error and type II error results as well as radiation exposure have always been of concern. The outgrowth cancer mortality rate is primarily due to delayed diagnosis, which occurs most frequently in a metastatic III or IV stage, resulting in a poor prognosis after therapy. Traditional detection techniques require identifying carcinogenic properties of cells, such as DNA or RNA alterations, conformational changes and overexpression of certain proteins, and cell shape, which are referred to as biomarkers or analytes. These procedures are complex, long-drawn-out, and expensive. Biosensors have recently acquired appeal as low-cost, simple, and super sensitive detection methods for analysis. The biosensor approach requires the existence of biomarkers in the sample. Thus, the development of novel molecular markers for diverse forms of cancer is a rising complementary affair. These biosensor devices offer two major advantages: (1) a tiny amount of blood collected from the patient is sufficient for analysis, and (2) it could help clinicians swiftly select and decide on the best therapy routine for the individual. This review will include updates on prospective cancer markers and biosensors in cancer diagnosis, as well as the associated detection limitations, with a focus on biosensor development for marker detection.

Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor Progression In Vivo: A Preclinical Study

Breast cancer is a dreaded disease affecting women the most in cancer-related deaths over other cancers. However, early diagnosis of the disease can help increase survival rates. The existing breast cancer diagnosis tools do not support the early diagnosis of the disease. Therefore, there is a great need to develop early diagnostic tools for this cancer. Photoacoustic spectroscopy (PAS), being very sensitive to biochemical changes, can be relied upon for its application in detecting breast tumors in vivo. With this motivation, in the current study, an aseptic chamber integrated photoacoustic (PA) probe was designed and developed to monitor breast tumor progression in vivo, established in nude mice. The device served the dual purpose of transporting tumor-bearing animals to the laboratory from the animal house and performing PA experiments in the same chamber, maintaining sterility. In the current study, breast tumor was induced in the nude mice by MCF-7 cells injection and the corresponding PA spectra at different time points (day 0, 5, 10, 15, and 20) of tumor progression in vivo in the same animals. The recorded photoacoustic spectra were subsequently preprocessed, wavelet-transformed, and subjected to filter-based feature selection algorithm. The selected top 20 features, by minimum redundancy maximum relevance (mRMR) algorithm, were then used to build an input feature matrix for machine learning (ML)-based classification of the data. The performance of classification models demonstrated 100% specificity, whereas the sensitivity of 95, 100, 92.5, and 85% for the time points, day 5, 10, 15, and 20, respectively. These results suggest the potential of PA signal-based classification of breast tumor progression in a preclinical model. The PA signal contains information on the biochemical changes associated with disease progression, emphasizing its translational strength toward early disease diagnosis.

68 Ga-labeled, imatinib encapsulated, theranostic liposomes: Formulation, characterization, and in vitro evaluation of anticancer activity

Cancer is still a global health problem. Among cancer types, breast cancer is the most frequently diagnosed one, and it causes a high mortality rate if not diagnosed in the early stages. In our study, imatinib encapsulated, nanosized, neutral/cationic liposome formulations were prepared as theranostic agents for breast cancer. After the characterization studies in which all liposomes exhibited proper profile owing to their particle size between 133 and 250 nm, polydispersity index values lower than 0.4, neutral and cationic zeta potential values, and high drug encapsulation efficiency, controlled drug release behaviors with zero-order kinetic were obtained. The higher than 90% radiolabeling efficiency values were obtained thanks to the determination of optimum radiolabeling condition (80°C temperature, 5 mCi radioactivity, and 10 min incubation period). According to the resazurin assay evaluating the cytotoxic profile of liposomes on MCF7 cells, neutral empty liposome was found as biocompatible, while both cationic liposomes (empty and drug-loaded ones) exhibited high nonspecific cytotoxicity at even low drug concentration due to the existence of stearyl amine in the formulations. However, dose-dependent cytotoxic effect and the highest cellular binding capacity were obtained by imatinib loaded neutral liposomes. In conclusion, 68 Ga-radiolabeled, imatinib-loaded, neutral, nanosized liposome formulation is the most promising one as a theranostic agent among all formulations.

Knowledge and practice of breast self-examination among breast cancer patients in Damascus, Syria

BACKGROUND

Breast cancer (BC) represents an important cause of cancer death, its incidence rate has been rising gradually in the Arab world, and in Syria, BC is the most common cancer and the leading cause of cancer death; its prognosis gets better as we detect it early in its first stages. So, it is very important to implement one or more early detection methods such as Breast Self-Examination (BSE), Clinical Breast Examination (CBE), and mammography. BSE represents an effective method to find out changes in breast structure when they happen. This study investigates the knowledge of BSE and its practice in BC patients.

METHODS

A quantitative cross-sectional study in Al-Bairouni hospital in Damascus-Syria was carried out using face-to-face interviews based on a structured questionnaire, which consisted of 4 sections. The data were subjected to statistical analysis using various analytical tests, including the independent t-test, one-way analysis of variance (ANOVA), and Chi-square test.

RESULTS

Five hundred patients were interviewed. Only 27.4% of patients had a good knowledge of BSE, 17.4% had average knowledge, and 55.2% had low knowledge of BSE. The factors that have an impact on the knowledge of BSE were: family breast cancer history (first and second-degree relatives), education, and the region of living (between governorates). The effect of knowledge of BSE on its practice was positive. However, only 24.8% of patients have been practicing BSE; the reasons for not practicing BSE were: no one has told the patient about it (64.8% of cases), and the patient does not have any symptoms relating to the breast (21.4%).Breast cancer was identified through breast self-examination (BSE) in 15.6% of cases.

CONCLUSION

There is a low degree of knowledge and little practice of BSE among Syrian breast cancer patients. Family breast cancer history, governate, occupation, and level of education had a statistically significant effect on knowledge scores of BSE, unlike age and social status. So, some steps should be taken to increase awareness about BSE among Syrian females.

Clinical importance of serum miRNA levels in breast cancer patients

There is limited data on the relationship of miRNAs with parameters that may affect surgical management or reflect tumour prognosis. It was aimed to evaluate serum miRNA levels in breast carcinoma cases and reveal the relationship between these levels and prognosis-related factors such as the histological type of the tumour, estrogen receptor, progesterone receptor, Ki-67 index, HER-2neu, E-cadherin, tumour size, CK5/6, CA15.3 levels, number of tumour foci, number of metastatic lymph nodes, and status of receiving neoadjuvant therapy. Thirty-five patients with a histopathologically confirmed breast carcinoma diagnosis in the case group and 35 healthy individuals in the control group were examined. miR-206, miR-17-5p, miR-125a, miR-125b, miR-200a, Let-7a, miR-34a, miR-31, miR-21, miR-155, miR-10b, miR-373, miR-520c, miR-210, miR-145, miR-139-5p, miR-195, miR-99a, miR-497 and miR-205 expression levels in the serum of participants were determined using the Polymerase Chain Reaction method. While serum miR-125b and Let-7a expression levels were significantly higher in breast cancer patients, miR-17-5p, miR-125a, miR-200a, miR-34a, miR-21, miR-99a and miR-497 levels were significantly lower in them. The Let-7a expression level had a statistically significant relationship with breast cancer histological type and HER-2neu parameters, miR-17-5p, miR-125b, Let-7a, miR-34a, miR-21 and miR-99a levels with E-cadherin, miR-34a, miR-99a and miR-497 with CA15.3, miR-125b, miR-200a and miR-34a with the number of metastatic lymph nodes, miR-125a with the number of tumour foci and miR-200a with the status of having the neoadjuvant therapy. Serum miR-17-5p, miR-125a, miR-125b, miR-200a, Let-7a, miR-34a, miR-21, miR-99a and miR-497 expression levels were determined to have predictive and prognostic importance in breast cancer.

Exploring Biomarkers in Breast Cancer: Hallmarks of Diagnosis, Treatment, and Follow-Up in Clinical Practice

Breast cancer is a prevalent malignancy in the present day, particularly affecting women as one of the most common forms of cancer. A significant portion of patients initially present with localized disease, for which curative treatments are pursued. Conversely, another substantial segment is diagnosed with metastatic disease, which has a worse prognosis. Recent years have witnessed a profound transformation in the prognosis for this latter group, primarily due to the discovery of various biomarkers and the emergence of targeted therapies. These biomarkers, encompassing serological, histological, and genetic indicators, have demonstrated their value across multiple aspects of breast cancer management. They play crucial roles in initial diagnosis, aiding in the detection of relapses during follow-up, guiding the application of targeted treatments, and offering valuable insights for prognostic stratification, especially for highly aggressive tumor types. Molecular markers have now become the keystone of metastatic breast cancer diagnosis, given the diverse array of chemotherapy options and treatment modalities available. These markers signify a transformative shift in the arsenal of therapeutic options against breast cancer. Their diagnostic precision enables the categorization of tumors with elevated risks of recurrence, increased aggressiveness, and heightened mortality. Furthermore, the existence of therapies tailored to target specific molecular anomalies triggers a cascade of changes in tumor behavior. Therefore, the primary objective of this article is to offer a comprehensive review of the clinical, diagnostic, prognostic, and therapeutic utility of the principal biomarkers currently in use, as well as of their clinical impact on metastatic breast cancer. In doing so, our goal is to contribute to a more profound comprehension of this complex disease and, ultimately, to enhance patient outcomes through more precise and effective treatment strategies.

A comprehensive review on the applications of ferrite nanoparticles in the diagnosis and treatment of breast cancer

Various conventional treatments including endocrine therapy, radiotherapy, surgery, and chemotherapy have been used for several decades to treat breast cancer; however, these therapies exhibit various life-threatening and debilitating adverse effects in patients. Additionally, combination therapies are required for prompt action as well as to prevent drug resistance toward standard breast cancer medications. Ferrite nanoparticles (NPs) are increasingly gaining momentum for their application in the diagnosis and treatment of breast cancer. Spinel ferrites are particularly used against breast cancer and have shown in vitro and in vivo better efficacy as compared to conventional cancer therapies. Magnetic resonance imaging contrast agents, magnetic particle imaging tracers, cell separation, and immune assays are some aspects related to the diagnosis of breast cancer against which different ferrite NPs have been successfully evaluated. Moreover, citrate-coated nickel ferrite, Mg/Zn ferrites, poly amidoamine dendrimers, cobalt ferrites, graphene oxide cobalt ferrites, doxorubicin functionalized cobalt ferrites, chitosan-coated zinc ferrites, PEG-coated cobalt ferrite, and copper ferrite NPs have demonstrated antiproliferative action against different breast cancer cells. Oxaliplatin-loaded polydopamine/BSA-copper ferrites, functionalized cobalt and zinc ferrites of curcumin, oxaliplatin-copper ferrite NPs, tamoxifen/diosgenin encapsulated ZnO/Mn ferrites, and fabricated core-shell fibers of doxorubicin have been developed to increase the bioavailability and anti-proliferative effect and decrease the toxicity of anticancer drugs. These ferrite NPs showed an anticancer effect at different doses in the presence or absence of an external magnetic field. The present review covers the in-depth investigations of ferrite NPs for the diagnosis and management of breast cancer.

Upregulation of BRCA1 and 2 protein expression is associated with dysregulation in amino acids profiles in breast cancer

BACKGROUND

The prevalence of breast cancer (BC) is high among cancers in Egypt, ranking it the most common cause of cancer mortality in women. BRCA1 and BRCA2 tumor suppressors proteins have a specific relationship with BC. Plasma free amino acids levels (PFAAs) have been reported to exhibit altered profiles among cancer patients. Thus, the present study aims to examine the alteration of the PFAAs profiles and investigate their association with BRCA1 and 2 circulating levels in Egyptian females diagnosed with BC and in females with family history of BC to establish potential early detection strategies for BC.

METHODS AND RESULTS

This study included 26 BC patients, 22 females with family history of BC (relatives) in addition to 38 healthy females as control group. Quantitative measurement of PFAAs was determined by the ion exchange separation method through high performance liquid chromatography. BRCA1 and BRCA2 concentrations were determined using ELISA. Our results showed PFAAs profiles in BC patients and in females with BC family history with significant upregulation in mean plasma levels of Alanine, Phenylalanine, Glutamate and Cysteine and downregulation of Taurine, Threonine, Serine, Glycine, Valine, Methionine and Histidine levels compared to controls. Also, a significant positive correlation was observed between plasma BRCA1 and Valine levels while a significant negative correlation was observed between BRCA2 and Lysine plasma levels.

CONCLUSION

PFAAs profile can potentially be used in early screening for BC patients and for susceptible females.

Deciphering a proteomic signature for the early detection of breast cancer from breast milk: the role of quantitative proteomics

INTRODUCTION

Breast cancer is one of the most prevalent cancers among women in the United States. Current research regarding breast milk has been focused on the composition and its role in infant growth and development. There is little information about the proteins, immune cells, and epithelial cells present in breast milk which can be indicative of the emergence of BC cells and tumors.

AREAS COVERED

We summarize all breast milk studies previously done in our group using proteomics. These studies include 1D-PAGE and 2D-PAGE analysis of breast milk samples, which include within woman and across woman comparisons to identify dysregulated proteins in breast milk and the roles of these proteins in both the development of BC and its diagnosis. Our projected outlook for the use of milk for cancer detection is also discussed.

EXPERT OPINION

Analyzing the samples by multiple methods allows one to interrogate a set of samples with various biochemical methods that complement each other, thus providing a more comprehensive proteome. Complementing methods like 1D-PAGE, 2D-PAGE, in-solution digestion and proteomics analysis with PTM-omics, peptidomics, degradomics, or interactomics will provide a better understanding of the dysregulated proteins, but also the modifications or interactions between these proteins.

Knowledge, attitudes, and practices related to breast cancer screening among female Jordanian university employees: A cross-sectional study

INTRODUCTION

To improve participation in breast screening programs, the level of knowledge about BC, attitudes, and practices of women in different sections of society must be understood. This study aimed to measure the level of knowledge of BC risk factors, signs and symptoms and determine current mammography practices among female employees at Jordanian universities.

METHODS

A cross-sectional descriptive study was conducted on female employees at Jordanian government universities. Data was collected using a structured questionnaire that included: sociodemographic characteristics, knowledge of BC risk factors, knowledge of BC symptoms and knowledge, attitude and practice of mammography as an early detection method.

RESULTS

A total of 362 participants completed the questionnaire. Overall, 174 scored ≥50% correct answers regarding BC risk factors, while 231 scored ≥50% correct answers regarding BC signs and symptoms. Half of the participants (n = 184, 50.8%) understood mammography to be an early BC detection method. Among those participants, 95 (51.6%) were eligible for screening and 39 (21.2%) had had a previous mammogram. The main reason for not engaging in mammography was the absence of BC signs and symptoms (37.2%). Profession, educational level and family history of BC were associated with increased knowledge of BC risk factors, signs and symptoms (p = 0.01). Lecturers in medical faculties exhibited the highest level of knowledge about mammography compared to participants in other professions (p = 0.02).

CONCLUSION

Only 79 participants had good to excellent knowledge about BC. Participants' profession was the major indicator for awareness of BC and mammography as an early detection method.

IMPLICATIONS FOR PRACTICE

The findings of this study reinforce the importance of providing BC educational programs for university employees in Jordan to increase awareness of BC and mammography.

Understanding the mechanistic pathways and clinical aspects associated with protein and gene based biomarkers in breast cancer

Cancer is one of the most widespread and severe diseases with a huge mortality rate. In recent years, the second-leading mortality rate of any cancer globally has been breast cancer, which is one of the most common and deadly cancers found in women. Detecting breast cancer in its initial stages simplifies treatment, decreases death risk, and recovers survival rates for patients. The death rate for breast cancer has risen to 0.024 % in some regions. Sensitive and accurate technologies are required for the preclinical detection of BC at an initial stage. Biomarkers play a very crucial role in the early identification as well as diagnosis of women with breast cancer. Currently, a wide variety of cancer biomarkers have been discovered for the diagnosis of cancer. For the identification of these biomarkers from serum or other body fluids at physiological amounts, many detection methods have been developed. In the case of breast cancer, biomarkers are especially helpful in discovering those who are more likely to develop the disease, determining prognosis at the time of initial diagnosis and choosing the best systemic therapy. In this study we have compiled various clinical aspects and signaling pathways associated with protein-based biomarkers and gene-based biomarkers.

Proteomic-based electrochemical non-invasive biosensor for early breast cancer diagnosis

Breast cancer is the second highest cause of cancer-related mortality in women worldwide and in the United States, accounting for around 571,000 deaths per year. Early detection of breast cancer increases treatment results and the possibility of a cure. While existing diagnostic modalities such as mammography, ultrasound, and biopsy exist, some are prohibitively expensive, uncomfortable, time-consuming, and have limited sensitivity, necessitating the development of a cost-effective, rapid, and highly sensitive approach such as an electrochemical biosensor. Our research focuses on detecting breast cancer patients using the ECM1 biomarker, which has higher expression in synthetic urine. Our study has two primary objectives: (i) Diverse ECM1 protein concentrations are measured using electrochemical impedance spectroscopy and ELISA. Establishing a standard curve for the electrochemical biosensor by calibrating ECM-1 protein levels using electrochemical impedance spectroscopy. (ii) Validation of the effectiveness of the electrochemical biosensor. This aim entails testing the unknown concentration of ECM1 in the synthetic urine to ensure the efficiency of the biosensor to detect the biomarker in the early stages. The results show that the synthetic urine solution's ECM-1 detection range ranges from 1 pg/ml to 500 ng/ml. This shows that by detecting changes in ECM-1 protein levels in patient urine, the electrochemical biosensor can consistently diagnose breast cancer in its early stages or during increasing recurrence. Our findings highlight the electrochemical biosensor's efficacy in detecting early-stage breast cancer biomarkers (ECM-1) in patient urine. Further studies will be conducted with patient samples and develop handheld hardware for patient usage.

Multi-MicroRNA Analysis Can Improve the Diagnostic Performance of Mammography in Determining Breast Cancer Risk

The objective of this study was to determine whether multi-microRNA analysis using a combination of four microRNA biomarkers (miR-1246, 202, 21, and 219B) could improve the diagnostic performance of mammography in determining breast cancer risk by age group (under 50 vs. over 50) and distinguish breast cancer from benign breast diseases and other cancers (thyroid, colon, stomach, lung, liver, and cervix cancers). To verify breast cancer classification performance of the four miRNA biomarkers and whether the model providing breast cancer risk score could distinguish between benign breast disease and other cancers, the model was verified using nonlinear support vector machine (SVM) and generalized linear model (GLM) and age and four miRNA qRT-PCR analysis values (dCt) were input to these models. Breast cancer risk scores for each Breast Imaging-Reporting and Data System (BI-RADS) category in multi-microRNA analysis were analyzed to examine the correlation between breast cancer risk scores and mammography categories. We generated two models using two classification algorithms, SVM and GLM, with a combination of four miRNA biomarkers showing high performance and sensitivities of 84.5% and 82.1%, a specificity of 85%, and areas under the curve (AUCs) of 0.967 and 0.965, respectively, which showed consistent performance across all stages of breast cancer and patient ages. The results of this study showed that this multi-microRNA analysis using the four miRNA biomarkers was effective in classifying breast cancer in patients under the age of 50, which is challenging to accurately diagnose. In addition, breast cancer and benign breast diseases can be classified, showing the possibility of helping with diagnosis by mammography. Verification of the performance of the four miRNA biomarkers confirmed that multi-microRNA analysis could be used as a new breast cancer screening aid to improve the accuracy of mammography. However, many factors must be considered for clinical use. Further validation with an appropriate screening population in large clinical trials is required. This trial is registered with (KNUCH 2022-04-036).

Label-free electrochemical cancer cell detection leveraging hemoglobin-encapsulated silver nanoclusters and Cu-MOF nanohybrids on a graphene-assisted dual-modal probe

Breast cancer detection at an early stage significantly increases the chances of successful treatment and survival. This study presents an electrochemical biosensor for detecting breast cancer cells, utilizing silver nanoclusters encapsulated by hemoglobin and Cu (II)-porphyrin-metal organic framework (BioMOF) in a graphene-incorporated nanohybrid probe. This Hb-AgNCs@MOF-G probe demonstrates high electrochemical activity, superior dispersity, porosity, and a large surface area for effective functionalization. Using a green ultrasonic-assisted stirring method, we fabricate ultra-small 5 nm particles that readily immobilize on a glassy carbon electrode, generating a detection signal when interacting with ferricyanide/ferrocyanide redox probes. The resulting immunosensor detects as few as 2 cells/mL using Electrochemical Impedance Spectroscopy (EIS) "signal on" and 16 cells/mL via Square Wave Voltammetry (SWV) "signal off", within a broad range of cell concentrations (102-5 × 104 cells/mL). Our designed sensor shows improved selectivity (5- to 16-fold) and robust detection in human blood with a recovery efficiency between 94.8-106% (EIS method) and 95.4-111% (SWV method). This sensor could streamline early cancer diagnosis and monitor patient treatment without requiring labelling or signal amplification. As a pioneering endeavor, we've utilized integrated porous MOFs with Hb-encapsulated silver nanoclusters in cancer detection, where these components collectively enhance the overall functionality.

Value of inversion imaging to diagnosis in differentiating malignant from benign breast masses

BACKGROUND

We aimed to evaluate the added value of inversion imaging in differentiating between benign and malignant breast masses when combined with the Breast Imaging Reporting and Data System (BI-RADS).

METHODS

A total of 364 patients with 367 breast masses (151 benign and 216 malignant) who underwent conventional ultrasound and inversion imaging prior to breast surgery were included. A 5-point inversion score (IS) scale was proposed based on the masses' internal echogenicity and distribution characteristics in the inversion images. The combination of IS and BI-RADS was compared with BI-RADS alone to evaluate the value of inversion imaging for breast mass diagnosis. The diagnostic performance of the BI-RADS and its combination with IS for breast masses were analyzed using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

The IS for malignant breast masses (3.96 ± 0.77) was significantly higher than benign masses (2.58 ± 0.98) (P < 0.001). The sensitivity, specificity, accuracy, PPV, and NPV of BI-RADS were 86.1%, 81.5%, 84.2%, 86.9%, and 80.4%, respectively, and an AUC was 0.909. By compared with BI-RADS, 72 breast masses were downgraded from suspected malignancy to benign, and 6 masses were upgraded from benign to suspected malignancy. Thus, the specificity was increased from 81.5 to 84.8%, it allows 72 benign masses avoid biopsy.

CONCLUSION

The combination of inversion imaging with BI-RADS can effectively improve the diagnostic efficacy of breast masses, and inversion imaging could help benign masses avoid biopsy.

Joint localization and classification of breast masses on ultrasound images using an auxiliary attention-based framework

Multi-task learning (MTL) methods have been extensively employed for joint localization and classification of breast lesions on ultrasound images to assist in cancer diagnosis and personalized treatment. One typical paradigm in MTL is a shared trunk network architecture. However, such a model design may suffer information-sharing conflicts and only achieve suboptimal performance for individual tasks. Additionally, the model relies on fully-supervised learning methodologies, imposing heavy burdens on data annotation. In this study, we propose a novel joint localization and classification model based on attention mechanisms and a sequential semi-supervised learning strategy to address these challenges. Our proposed framework offers three primary advantages. First, a lesion-aware network with multiple attention modules is designed to improve model performance on lesion localization. An attention-based classifier explicitly establishes correlations between the two tasks, alleviating information-sharing conflicts while leveraging location information to assist in classification. Second, a two-stage sequential semi-supervised learning strategy is designed for model training to achieve optimal performance on both tasks and substantially reduces the need for data annotation. Third, the asymmetric and modular model architecture allows for the flexible interchangeability of individual components, rendering the model adaptable to various applications. Experimental results from two different breast ultrasound image datasets under varied conditions have demonstrated the effectiveness of the proposed method. Furthermore, we conduct comprehensive investigations into the impacts of various factors on model performance, gaining in-depth insights into the mechanism of our proposed framework. The code is available at https://github.com/comp-imaging-sci/lanet-bus.git.

Probing Aberrantly Glycosylated Mucin 1 in Breast Cancer Extracellular Vesicles

Aberrantly glycosylated mucin 1 is a critical prognostic biomarker in breast epithelial cancers. Hypoglycosylated mucin 1 coats the surface of the cancer cells, where O-glycans are predominantly linked via an N-acetylgalactosamine moiety (GalNAc). Cancer cell-derived extracellular vesicles (EVs) carry biomarkers from parent cancer cells to the recipient cells and, therefore, could potentially be leveraged for diagnostics and noninvasive disease monitoring. We devised a label-free approach for identifying glycoprotein mucin 1 overexpression on breast cancer EVs. While exploring a plethora of biochemical (enzyme-linked immunosorbent assay, flow cytometry, and SDS-PAGE) and label-free biophysical techniques (circular dichroism and infrared spectroscopy (IR)) along with multivariate analysis, we discovered that mucin 1 is significantly overexpressed in breast cancer EVs and aberrant glycosylation in mucin 1 could be critically addressed using IR and multivariate analysis targeting the GalNAc sugar. This approach emerges as a convenient and comprehensive method of distinguishing cancer EVs from normal samples and holds potential for nonintrusive breast cancer liquid biopsy screening.

MicroRNAs as Molecular Biomarkers for the Characterization of Basal-like Breast Tumor Subtype

Breast cancer is a heterogeneous disease highlighted by the presence of multiple tumor variants and the basal-like breast cancer (BLBC) is considered to be the most aggressive variant with limited therapeutics and a poor prognosis. Though the absence of detectable protein and hormonal receptors as biomarkers hinders early detection, the integration of genomic and transcriptomic profiling led to the identification of additional variants in BLBC. The high-throughput analysis of tissue-specific micro-ribonucleic acids (microRNAs/miRNAs) that are deemed to have a significant role in the development of breast cancer also displayed distinct expression profiles in each subtype of breast cancer and thus emerged to be a robust approach for the precise characterization of the BLBC subtypes. The classification schematic of breast cancer is still a fluid entity that continues to evolve alongside technological advancement, and the transcriptomic profiling of tissue-specific microRNAs is projected to aid in the substratification and diagnosis of the BLBC tumor subtype. In this review, we summarize the current knowledge on breast tumor classification, aim to collect comprehensive evidence based on the microRNA expression profiles, and explore their potential as prospective biomarkers of BLBC.

Potential roles of the exosome/microRNA axis in breast cancer

Cancer is one of the most common diseases in the world, and various genetic and environmental factors play a key role in its development. Breast cancer is one of the most common and deadly cancers in women. Exosomes are extracellular vesicles (EVs) with an average size of about 100 nm that contain lipids, proteins, microRNAs (miRNAs), and genetic factors and play a significant role in cell signaling, communication, tumorigenesis, and drug resistance. miRNAs are RNAs with about 22 nucleotides, which are synthesized by RNA polymerase and are involved in regulating gene expression, as well as the prevention or progression of cancer. Many studies have indicated the connection between miRNAs and exosomes. According to their findings, it seems that circulating exosomal miRNAs have not been well evaluated as biomarkers for breast cancer diagnosis or monitoring. Therefore, given the importance of miRNAs in exosomes, the goal of the present study was to clarify the relationship between miRNAs in exosomes and the role they play as biomarkers in breast cancer.

A clinical-radiomics nomogram based on multimodal ultrasound for predicting the malignancy risk in solid hypoechoic breast lesions

Background

In routine clinical examinations, solid hypoechoic breast lesions are frequently encountered, but accurately distinguishing them poses a challenge. This study proposed a clinical-radiomics nomogram based on multimodal ultrasound that enhances the diagnostic accuracy for solid hypoechoic breast lesions.

Method

This retrospective study analyzed ultrasound strain elastography (SE) and automated breast volume scanner images (ABVS) of 423 solid hypoechoic breast lesions from 423 female patients in our hospital between August 2019 and May 2022. They were assigned to the training (n=296) and validation (n=127) groups in a 7:3 ratio by generating random numbers. Radiomics features were extracted and screened from ABVS and SE images, followed by the calculation of the radiomics score (Radscore) based on these features. Subsequently, a nomogram was constructed through multivariate logistic regression to assess the malignancy risk in breast lesions by combining Radscore with Breast Imaging Reporting and Data System (BI-RADS) scores and clinical risk factors associated with breast malignant lesions. The diagnostic performance, calibration performance, and clinical usefulness of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, the calibration curve, and the decision analysis curve, respectively.

Results

The diagnostic performance of the nomogram is significantly superior to that of both the clinical diagnostic model (BI-RADS model) and the multimodal radiomics model (SE+ABVS radiomics model) in training (AUC: 0.972 vs 0.930 vs 0.941) and validation group (AUC:0.964 vs 0.916 vs 0.933). In addition, the nomogram also exhibited a favorable goodness-of-fit and could lead to greater net benefits for patients.

Conclusion

The nomogram enables a more effective assessment of the malignancy risk of solid hypoechoic breast lesions; therefore, it can serve as a new and efficient diagnostic tool for clinical diagnosis.

Value of Molybdenum Target X-Ray and High-Frequency Color Doppler Flow Imaging in Early Diagnosis of Breast Carcinoma: A Comparative Analysis

Background

Breast carcinoma (BC) threatens the physical and mental health of women worldwide, and early diagnosis is important for improving patient outcomes and ensuring successful treatment.

Purpose

This research mainly aims to compare and analyze the value of molybdenum target X-ray and high-frequency color Doppler flow imaging (CDFI) in the early diagnosis of BC.

Methods

First, 102 patients with suspected early-stage BC (ESBC) admitted to Henan Provincial People's Hospital were examined by molybdenum target X-ray and CDFI. Based on the pathological findings, the diagnostic efficiency data of the two diagnostic modalities such as positive detection rate (PDR), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SEN), specificity (SPE), and accuracy (ACC), as well as imaging information like masses, microcalcifications (MCs), axillary lymph node (LN) metastases, and blood flow signal or vascular sign abnormalities were analyzed.

Results

CDFI contributed to higher PDR, PRV, NPV, SEN, and ACC than molybdenum target X-ray in ESBC diagnosis, but similar SPE. The combined diagnosis of molybdenum target X-ray plus CDFI contributed to even higher PDR, PRV, NPV, SEN, and ACC than molybdenum target X-ray alone and higher ACC than CDFI. Imaging inspection revealed that the number of cases of masses, axillary LN metastases, and abnormalities in blood flow signals or vascular signs detected by CDFI was significantly higher than that by molybdenum target X-ray, while the number of MCs was significantly lower.

Conclusion

Molybdenum target X-ray plus CDFI is more effective in the diagnosis of ESBC and plays a complementary role in imaging examination, which can synergistically improve the diagnostic ACC of ESBC and is worthy of clinical promotion.

Delineation and detection of breast cancer using novel label-free fluorescence

BACKGROUND

Accurate diagnosis of breast cancer (BC) plays a crucial role in clinical pathology analysis and ensuring precise surgical margins to prevent recurrence.

METHODS

Laser-induced fluorescence (LIF) technology offers high sensitivity to tissue biochemistry, making it a potential tool for noninvasive BC identification. In this study, we utilized hyperspectral (HS) imaging data of stimulated BC specimens to detect malignancies based on altered fluorescence characteristics compared to normal tissue. Initially, we employed a HS camera and broadband spectrum light to assess the absorbance of BC samples. Notably, significant absorbance differences were observed in the 440-460 nm wavelength range. Subsequently, we developed a specialized LIF system for BC detection, utilizing a low-power blue laser source at 450 nm wavelength for ten BC samples.

RESULTS

Our findings revealed that the fluorescence distribution of breast specimens, which carries molecular-scale structural information, serves as an effective marker for identifying breast tumors. Specifically, the emission at 561 nm exhibited the greatest variation in fluorescence signal intensity for both tumor and normal tissue, serving as an optical predictive biomarker. To enhance BC identification, we propose an advanced image classification technique that combines image segmentation using contour mapping and K-means clustering (K-mc, K = 8) for HS emission image data analysis.

CONCLUSIONS

This exploratory work presents a potential avenue for improving "in-vivo" disease characterization using optical technology, specifically our LIF technique combined with the advanced K-mc approach, facilitating early tumor diagnosis in BC.

Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies

BACKGROUND

Breast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of breast cancer.

PURPOSE

In this review, we examine studies that have used these techniques for breast cancer classification and diagnosis, focusing on five groups of medical images: mammography, ultrasound, MRI, histology, and thermography. We discuss the use of five popular machine learning techniques, including Nearest Neighbor, SVM, Naive Bayesian Network, DT, and ANN, as well as deep learning architectures and convolutional neural networks.

CONCLUSION

Our review finds that machine learning and deep learning techniques have achieved high accuracy rates in breast cancer classification and diagnosis across various medical imaging modalities. Furthermore, these techniques have the potential to improve clinical decision-making and ultimately lead to better patient outcomes.

Quantitative analysis of serum cell-free DNA as a predictive and prognostic marker in breast cancer patients

Introduction

According to the GLOBOCAN (Global Cancer Observatory) 2020 report, 13,028 new cases of breast cancer (19%) were diagnosed in the United States, and 6,783 of them succumbed to the disease, making it the most common cancer among women. The clinical stage at the time of diagnosis is one of the most significant survival predictors in breast cancer. With delayed illness detection comes a lower survival rate. The prognosis of breast cancer may be predicted using circulating cell-free DNA (cfDNA), a non-invasive diagnosis technique.

Objective

This study aimed to determine the most sensitive and effective method for detecting changes in cfDNA levels and for using cfDNA as a diagnostic and prognostic marker of breast cancer.

Methods

The potential function of serum cfDNA levels as a marker for early breast cancer diagnosis was investigated using UV spectrophotometric, fluorometric, and real-time qPCR assays.

Results

This research suggests that the most successful way to measure the amount of cfDNA described decades ago could be used as a "liquid biopsy" to track cancer in real time. The RT-qPCR (ALU115) method produced the most statistically significant results (p=0.000). At the threshold concentration of 395.65 ng/ml of cfDNA, the ROC curve reflects the maximum AUC= 0.7607, with a sensitivity of 0.65 and specificity of 0.80.

Conclusion

For a preliminary assessment of total circulating cfDNA, a combination of all of the above techniques will be most efficacious. Based on our results, we conclude that the RT-qPCR technique combined with fluorometric measurement can identify a statistically significant difference in cfDNA levels between cohorts of breast cancer patients and healthy controls.

Visualization of Metastatic Lung Cancer with TiNIR

The development of efficient biomarkers and probes for monitoring and treating cancer, specifically metastatic cancer, is a critical research area that can have a significant impact on both patient outcomes and drug discovery. In this context, TiNIR has been developed to detect tumor-initiating cells (TICs), with heme oxygenase 2 (HO2) as a promising therapeutic biomarker for tumor-initiating cells. In this study, TiNIR has demonstrated its effectiveness as an in vivo metastatic lung cancer tracker, highlighting its potential as a valuable tool in cancer research and therapy. The development of innovative approaches that selectively target metastatic cancers represents a promising avenue for improving survival rates and enhancing the quality of life of cancer patients.

Immune-related biomarkers predict the prognosis and immune response of breast cancer based on bioinformatic analysis and machine learning

Breast cancer (BC) is the malignancy with the highest mortality rate among women, identification of immune-related biomarkers facilitates precise diagnosis and improvement of the survival rate in early-stage BC patients. 38 hub genes significantly positively correlated with tumor grade were identified based on weighted gene coexpression network analysis (WGCNA) by integrating the clinical traits and transcriptome analysis. Six candidate genes were screened from 38 hub genes basing on least absolute shrinkage and selection operator (LASSO)-Cox and random forest. Four upregulated genes (CDC20, CDCA5, TTK and UBE2C) were identified as biomarkers with the log-rank p < 0.05, in which high expression levels of them showed a poor overall survival (OS) and recurrence-free survival (RFS). A risk model was finally constructed using LASSO-Cox regression coefficients and it possessed superior capability to identify high risk patients and predict OS (p < 0.0001, AUC at 1-, 3- and 5-years are 0.81, 0.73 and 0.79, respectively). Decision curve analysis demonstrated risk score was the best prognostic predictor, and low risk represented a longer survival time and lower tumor grade. Importantly, multiple immune cell types and immunotherapy targets were observed increase in expression levels in high-risk group, most of which were significantly correlated with four genes. In summary, the immune-related biomarkers could accurately predict the prognosis and character the immune responses in BC patients. In addition, the risk model is conducive to the tiered diagnosis and treatment of BC patients.

Comparison of whole blood and serum samples of breast cancer based on laser-induced breakdown spectroscopy with machine learning

To identify cancer from non-cancer is one of the most challenging issues nowadays in the early diagnosis of cancer. The primary issue of early detection is to choose a suitable type of sample collection to diagnose cancer. A comparison of whole blood and serum samples of breast cancer was studied using laser-induced breakdown spectroscopy (LIBS) with machine learning methods. For LIBS spectra measurement, blood samples were dropped on a substrate of boric acid. For the discrimination of breast cancer and non-cancer samples, eight machine learning models were applied to LIBS spectral data, including decision tree, discrimination analysis, logistic regression, naïve byes, support vector machine, k-nearest neighbor, ensemble and neural networks classifiers. Discrimination between whole blood samples showed that narrow neural networks and trilayer neural networks both provided 91.7% highest prediction accuracy and serum samples showed that all the decision tree models provided 89.7% highest prediction accuracy. However, using whole blood as sample achieved the strong emission lines of spectra, better discrimination results of PCA and maximum prediction accuracy of machine learning models as compared to using serum samples. These merits concluded that whole blood samples could be a good option for the rapid detection of breast cancer. This preliminary research may provide the complementary method for early detection of breast cancer.

Unlocking the Diagnostic Potential of Saliva: A Comprehensive Review of Infrared Spectroscopy and Its Applications in Salivary Analysis

Infrared (IR) spectroscopy is a noninvasive and rapid analytical technique that provides information on the chemical composition, structure, and conformation of biomolecules in saliva. This technique has been widely used to analyze salivary biomolecules, owing to its label-free advantages. Saliva contains a complex mixture of biomolecules including water, electrolytes, lipids, carbohydrates, proteins, and nucleic acids which are potential biomarkers for several diseases. IR spectroscopy has shown great promise for the diagnosis and monitoring of diseases such as dental caries, periodontitis, infectious diseases, cancer, diabetes mellitus, and chronic kidney disease, as well as for drug monitoring. Recent advancements in IR spectroscopy, such as Fourier-transform infrared (FTIR) spectroscopy and attenuated total reflectance (ATR) spectroscopy, have further enhanced its utility in salivary analysis. FTIR spectroscopy enables the collection of a complete IR spectrum of the sample, whereas ATR spectroscopy enables the analysis of samples in their native form, without the need for sample preparation. With the development of standardized protocols for sample collection and analysis and further advancements in IR spectroscopy, the potential for salivary diagnostics using IR spectroscopy is vast.

Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework

We present an analysis and evaluation of breast cancer detection and diagnosis using segmentation models. We used an advanced semantic segmentation method and a deep convolutional neural network to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images. To improve the segmentation results, we used six models to analyse 309 patients, including 151 benign and 158 malignant tumour images. We compared the Unet3+ architecture with several other models, such as FCN, Unet, SegNet, DeeplabV3+ and pspNet. The Unet3+ model is a state-of-the-art, semantic segmentation architecture that showed optimal performance with an average accuracy of 82.53% and an average intersection over union (IU) of 52.57%. The weighted IU was found to be 89.14% with a global accuracy of 90.99%. The application of these types of segmentation models to the detection and diagnosis of breast cancer provides remarkable results. Our proposed method has the potential to provide a more accurate and objective diagnosis of breast cancer, leading to improved patient outcomes.

Clinical and Translational Applications of Serological and Histopathological Biomarkers in Metastatic Breast Cancer: A Comprehensive Review

Breast cancer is one of the most common malignancies worldwide and the most common form of cancer in women. A large proportion of patients begin with localized disease and undergo treatment with curative intent, while another large proportion of patients debuts with disseminated metastatic disease. In the last subgroup of patients, the prognosis in recent years has changed radically, given the existence of different targeted therapies thanks to the discovery of different biomarkers. Serological, histological, and genetic biomarkers have demonstrated their usefulness in the initial diagnosis, in the follow-up to detect relapses, to guide targeted treatment, and to stratify the prognosis of the most aggressive tumors in those with breast cancer. Molecular markers are currently the basis for the diagnosis of metastatic disease, given the wide variety of chemotherapy regions and existing therapies. These markers have been a real revolution in the therapeutic arsenal for breast cancer, and their diagnostic validity allows the classification of tumors with higher rates of relapse, aggressiveness, and mortality. In this sense, the existence of therapies targeting different molecular alterations causes a series of changes in tumor biology that can be assessed throughout the course of the disease to provide information on the underlying pathophysiology of metastatic disease, which allows us to broaden our knowledge of the different mechanisms of tissue invasion. Therefore, the aim of the present article is to review the clinical, diagnostic, predictive, prognostic utility and limitations of the main biomarkers available and under development in metastatic breast cancer.

Frequency of Hepatic Metastatic Disease in Patients with Stage IV Breast Cancer Is Similar for Steatotic and Non-Steatotic Livers

Background

Breast cancer is the most common non-cutaneous malignancy and the second leading cause of cancer mortality in the United States. Breast cancer is a heterogeneous disease; diagnosis at an early stage renders it potentially curable, whereas advanced metastatic disease carries a worse prognosis.

Objectives

To investigate whether hepatic steatosis (HS) is associated with liver metastases in patients with newly diagnosed stage IV female breast cancer patients (either de novo metastatic breast cancer or recurrent metastatic breast cancer) using non-contrast computed tomography (CT) as a marker of HS.

Design

Retrospective analysis.

Methods

We retrospectively identified 168 patients with stage IV breast cancer with suitable imaging from a prospectively maintained oncologic database. Three radiologists manually defined hepatic regions of interest on non-contrast CT images, and attenuation data were extracted. HS was defined as a mean attenuation <48 Hounsfield units. The frequency of hepatic metastatic disease was calculated for patient with and without HS. Relationships between HS and various patient (age, body mass index, race) and tumor (hormone receptor status, HER2 status, tumor grade) characteristics were also analyzed.

Results

There were 4 patients with liver metastasis in the HS group (41 patients) versus 20 patients with liver metastases in the non-HS group (127 patients). The difference in frequencies of liver metastases among patients with (9.8%) versus without (15.7%) hepatic steatosis (odds ratio = 1.72 [0.53-7.39]) was not statistically significant (P = .45). Body mass index was significantly higher (P = .01) among patients with hepatic steatosis (32.2 ± 7.3 vs 28.8 ± 7.1 kg/m2). Otherwise, there were no significant differences between patients with versus without HS with respect to regarding age, race, hormone receptor status, HER2 status, or tumor grade.

Conclusion

The frequency of hepatic metastatic disease in patients with stage IV breast cancer is similar for steatotic and non-steatotic livers.

Comparison of Interleukin-33 Serum Levels in Patients with Breast Cancer and Idiopathic Granulomatous Mastitis

BACKGROUND

Breast cancer (BC) is the main cause of cancer death in women. Idiopathic granulomatous mastitis (IGM), a rare chronic disease that clinically mimics breast carcinoma, and is associated with high mortality and morbidity, but an immediate and accurate diagnosis can substantially decrease these rates. Expressed by numerous human tissues, interleukin-33 (IL-33) has an inductive role in the network of pro-inflammatory cytokines. The aim of this study was to investigate the serum levels of IL-33 in BC and IGM patients in comparison with healthy women.

MATERIALS AND METHODS

This descriptive-analytical study was carried out on 28 patients with BC and 25 patients with IGM as the patient groups and 25 healthy volunteers with normal screening reports as the control group. Histopathological pattern of BC and IGM were confirmed by specialized pathologists. The serum concentration of IL-33 was measured using enzyme-linked immunosorbent assay (ELISA) kit according to the manufacturer's instructions.

RESULTS

The mean age of the patients with BC and IGM and the control group was 49.1, 37.1, and 36.8 years, respectively. There was no significant difference in IL-33 expression among the participants with regard to age, marital status, body mass index (BMI), and menopausal status. IL-33 assay indicated a significant difference between the BC (P=0.011) and IGM (P=0.031) groups compared to the controls, although no substantial differences were observed between the IGM and BC groups.

CONCLUSION

IL-33 can be considered a significant factor distinguishing IGM and BC patients from controls, although it cannot be applied to diagnose and differentiate BC from IGM patients.
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