Current State of Breast Cancer Diagnosis, Treatment, and Theranostics

Evaluation of CircHIPK3 biomarker potential in breast cancer

Background

Nowadays, the investigation of circular RNAs (circRNAs) in various cancers is of great interest. In this research, we evaluated circHIPK3 biomarker potential in breast cancer (BC).

Methods

The studied samples were 100 cancer and adjacent normal tissues, plasma from 95 cancer patients, 42 patients with fibroadenomatosis, and 93 healthy donors. Illumina high-throughput Hi Seq 2000 sequencing performed expression profiling on 4 pairs of cancerous and normal breast tissues. For expression confirmation, Quantitative real-time fluorescent polymerase chain reaction (qRT-PCR) was used to detect the expression level of circHIPK3. CircHIPK3 diagnostic efficacy was evaluated by the receiver operating characteristic curve (ROC).

Results

Based on high-throughput sequencing and bioinformatics results circHIPK3 had the highest expression in cancer tissues (P = 0.00034). Real-time results showed expression upregulation of circHIPK3 in BC tissues and plasma in comparison to healthy controls (P < 0.0001). For diagnostic potential, the area under the curve (AUC) result was 0.8087 (95 % CI: 0.7309 to 0.8866, P < 0.0001). Also, our results showed high specificity and sensitivity of circHIPK3 when evaluated alongside the CA-15-3 and CEA. Pathologic criteria evaluation showed that upregulation of circHIPK3 correlates with tumor size.

Conclusions

CircHIPK3 is significantly upregulated in BC tissues and plasma compared to healthy controls, demonstrating high diagnostic potential with an AUC of 0.8087. The expression of circHIPK3 correlates with tumor size, indicating its relevance in the pathologic assessment of BC.

The current landscape of aromatase inhibitors for the treatment of estrogen receptor-positive breast carcinoma

Estrogen receptor-positive (ER+) breast carcinoma represents a significant portion of breast cancer cases and is characterized by the presence of estrogen receptors that promote tumor growth upon estrogen binding. ER + breast cancer progression involves hormonal influences, interactions within the tumor microenvironment, and genetic mutations that may lead to treatment resistance. Successful therapeutic options include hormonal therapies, particularly aromatase inhibitors (AIs), which aim to block the effects of estrogen or reduce its synthesis. With higher efficacy than tamoxifen, AIs such as anastrozole, letrozole, and exemestane have become widely employed in adjuvant and first-line treatments for advanced breast cancer. AIs function by inhibiting the enzyme aromatase, which converts androgens into estrogens in the peripheral tissues. Because too much estrogen might promote tumor growth, this decrease in estrogen levels is essential for treating ER+ malignancies. To provide a comprehensive overview of AIs in the treatment of ER+ breast cancer, this study examined the pharmacokinetics, clinical uses, mechanisms of action, and problems with treatment resistance. To maximize therapeutic approaches and enhance patient outcomes in the treatment of ER breast cancer, it is imperative to understand these characteristics.

Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care

BACKGROUND

Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing precision oncology via integrating genomics data and digitized health information. AI applications show promise in cancer prediction, prognosis, and treatment planning, particularly in radiomics, deep learning, and machine learning for early cancer diagnosis. However, widespread adoption requires comprehensive data and clinical validation. While AI has demonstrated advantages in treating common malignancies like lung and breast cancers, challenges remain in managing rare tumors due to limited datasets. AI's role in processing multi-omics data and supporting precision oncology decision-making is critical as genetic and health data become increasingly digitized.

METHOD

This review article presents current knowledge on AI and associated technologies, which are being utilized in the diagnosis and therapy of cancer. The applications of AI in radiomics, deep learning, and machine learning for cancer screening and treatment planning are examined. The study also explores the capabilities and limitations of predictive AI in diagnosis and prognosis, as well as generative AI, such as advanced chatbots, in patient and provider interactions.

RESULTS

AI can improve the early diagnosis and treatment of high-incidence cancers like breast and lung cancer. However, its application in rare cancers is limited by insufficient data for training and validation. AI can effectively process large-scale multi-omics data from DNA and RNA sequencing, enhancing precision oncology. Predictive AI aids in risk assessment and prognosis, while generative AI tools improve patient-provider communication. Despite these advancements, further research and technological progress are needed to overcome existing challenges.

CONCLUSIONS

AI holds transformative potential for cancer therapy, particularly in precision oncology, early detection, and personalized treatment planning. However, challenges such as data limitations in rare cancers, the need for clinical validation, and regulatory considerations must be addressed. Future advancements in AI could significantly improve decision-support systems in oncology, ultimately enhancing patient care and quality of life. The review highlights both the opportunities and obstacles in integrating AI into cancer diagnostics and therapeutics, calling for continued research and regulatory oversight.

Analysis of hydrogen sulfide-related signatures of breast cancer identified 2 distinct subtypes: Implications for individualized therapeutics

Hydrogen sulfide (H2S) is a vital gasotransmitter involved in breast cancer (BC) pathogenesis. This study aims to employ hydrogen sulfide-related genes (HSRGs) for molecular classification of BC and, accordingly, to establish a robust prognostic risk signature. Transcriptomic, clinical, and mutational data of BC patients were collected from the cancer genome atlas and gene expression omnibus databases. Prognostic relevance was evaluated using Cox regression analysis, while consensus clustering analysis was employed for molecular subtyping. Gene expression profiles, prognosis, immune infiltration patterns, drug sensitivity, and response to immunotherapy were compared between subtypes. Multiple-gene prognostic features were developed and assessed along with a nomogram. The gene expression was validated in clinical samples using quantitative polymerase chain reaction. Among 282 HSRGs, 46 exhibited significant correlations with BC prognosis. Consensus clustering identified 2 distinct molecular subtypes (C1 and C2). C1 displayed significantly improved prognosis compared to C2, accompanied by increased infiltration of B cells, T cells, monocytes, and mast cells but decreased macrophage infiltration. Moreover, C1 demonstrated higher drug sensitivity and immunotherapeutic response relative to C2. Enrichment analysis revealed suppressed immune-related processes and pathways in C2 while cell cycle regulation and chromosomal processes were significantly activated. Additionally, a risk feature comprising 6 differentially expressed genes between subtypes was constructed; this feature performed well in prognostic prediction. Integration of this feature with other clinical parameters (radiotherapy/chemotherapy status, clinical stage, N stage) into a nomogram further enhanced prognostic accuracy. Clinical samples further validated the high expression of ATP13A5, LRTM2, MAFA, and SPDYC and the low expression of CYP4F12 and TNN in BC. Our findings highlight the clinical relevance of HSRGs in BC, providing a basis for precise molecular classification and prognosis evaluation. The developed risk feature and nomogram offer practical tools for guiding personalized treatment strategies in clinical practice.

Healthcare-seeking behaviors and factors influencing non-adherence among cervical cancer patients attending Bugando Oncology Clinic in Mwanza, Tanzania: A qualitative Phenomenological study

BACKGROUND

In low- and middle-income countries, particularly in Tanzania, most patients with cervical cancer present with advanced-stage disease and exhibit non-adherence which results in increased numbers of patients with cancer-related deaths. The current study explores the health-seeking behavior of cervical cancer patients and the factors that influence their non-adherence to cancer care.

OBJECTIVE

To explore the healthcare-seeking behaviors and identify factors influencing non-adherence among cervical cancer patients attending Bugando Medical Center's Oncology Clinic in Mwanza, Tanzania.

METHODS

A qualitative phenomenological design was adopted to explore the lived experiences of 15 households with non-adherent cervical cancer patients, after obtaining patients' information from the chemo radiation treatment registries of Bugando Oncology Clinic in Mwanza, Tanzania. Data were generated through in-depth interviews with patients, and NVivo 12 qualitative computer software was used to aid analysis. Thematic content analysis was conducted to uncover underlying meanings and patterns in the data, providing valuable insights into the phenomena under investigation.

FINDINGS

The study revealed poor adherence to treatment-seeking by most of the study informants in the form of delayed health-seeking at nearby health facilities. The limited capacity for correct cervical cancer diagnosis also pushed some of the informants to turn to self-medication including traditional remedies. The findings revealed delayed healthcare-seeking behavior and poor adherence to most of the study participants seeking medical care at nearby health facilities with limited capacity for correct cervical cancer diagnosis. Some turned to self-medications including traditional remedies. Financial constraints emerged as a major obstacle, affecting the affordability of treatments, transportation, and accommodation. Moreover, the malfunctioning radiotherapy machine posed a significant barrier to effective treatment. Limited comprehensive information on their condition, treatment options, and schedules further hindered adherence.

CONCLUSION

There is a need for improved access to an appropriate healthcare system and interventions to improve non-adherence to treatment of cervical cancer services, as well as increase public awareness, initiation of tools for good adherence of chemotherapy treatment and mass screening of cervical cancer risk factors and earlier diagnosis for better survival from the disease in Tanzania.

Predictive value of ultrasound assessment of axillary and brachial artery parameters for lymph node metastasis in breast cancer patients

OBJECTIVE

This study aimed to assess the predictive value of ultrasound assessment of axillary and brachial artery parameters for lymph node metastasis (LNM) in breast cancer (BRCA) patients.

METHODS

The clinical data of 172 cancer patients were reviewed, and the patients were stratified into two groups based on the presence or absence of axillary LNM. Ultrasound assessment was employed to evaluate axillary and brachial artery parameters using specific techniques, and arterial characteristics were analyzed.

RESULTS

Significant differences were observed in the ultrasound parameters of both axillary and brachial arteries between the non-LNM and LNM groups. Specifically, axillary and brachial artery diameters and resistive index exhibited significant differences and correlations with axillary LNM. Furthermore, molecular markers such as human epidermal growth factor receptor 2 (HER2) status, estrogen receptor (ER) status, and progesterone receptor (PR) status were found to be significantly correlated with LNM. Additionally, a nomogram was constructed, demonstrating the predictive value of the integrated arterial parameters. The combined model, incorporating axillary and brachial artery parameters, exhibited a higher predictive capability for axillary LNM compared to individual arterial parameters (AUC = 0.984).

CONCLUSION

Ultrasound assessment of axillary and brachial artery parameters, in conjunction with molecular markers, holds promise as a non-invasive tool for predicting LNM in BRCA patients. The observed correlations provide insights into the potential clinical relevance of arterial parameters in risk stratification and treatment planning. Further research in larger, prospective cohorts is warranted to validate the findings and enhance the precision of BRCA management.

Critical Diagnostic Delay Thresholds in Breast Cancer: A Molecular Subtype-Based Causal Analysis From Saudi Arabia

Introduction Diagnostic delays in breast cancer can significantly affect treatment outcomes. Currently, the causal mechanisms and critical time thresholds remain poorly defined across the different molecular subtypes of breast cancer. We investigated the relationship between diagnostic delays and breast cancer outcomes based on the data from our center, with a focus on identifying actionable intervention points within the diagnostic pathway. Methods We conducted a retrospective cohort study of 802 breast cancer patients treated at King Fahad Specialist Hospital in Qassim Province, Saudi Arabia (2017-2024). Using directed acyclic graphs and mediation analysis, we quantified the causal pathways through which delays impact the outcomes. Markov chain modeling was utilized to determine the molecular subtype-specific critical thresholds where stage migration probability exceeds 10%. Results We found that 589 patients (73.5%) experienced high-risk delays (over two months). Stage migration emerged as the primary mediator, accounting for 67.3% (95% CI: 58.4-76.1%) of the total effect of delays on survival. We have identified multiple critical thresholds across molecular subtypes: 38 days for triple-negative, 52 days for HER2-positive, and 85 days for ER+/PR+/HER2- tumors. Hazard ratios for mortality increased progressively with delay duration, from 1.18 (95% CI: 1.05-1.32) for delays of two weeks to one month to 2.35 (95% CI: 2.06-2.67) for delays that are equal to or more than one year, translating to an average 3.40 life years lost per patient. Conclusions The impact of diagnostic delays on breast cancer outcomes is fundamentally governed by tumor biology, with significant vulnerability thresholds aligned with molecular aggressiveness. Our findings support applying a biologically informed triage system where molecular characteristics determine maximum acceptable diagnostic intervals. Using the suggested approach, we may achieve a better balance in the resource constraints with biological imperatives, and possibly improve survival outcomes without proportional increases in healthcare expenditure.

Design Principles of an Engineered Metastatic Niche for Monitoring of Cancer Progression

Across many types of cancer, metastatic disease is associated with a substantial decrease in 5-year survival rates relative to only a localized primary tumor. Many patients self-report metastatic disease due to disruption of normal organ or tissue function, and earlier detection could enable treatment with a lower burden of disease. We have previously reported a subcutaneous biomaterial implant for early detection by serving as an engineered metastatic niche, which has been reported to recruit tumor cells before colonization of solid organs. In this report, we investigated the design principles of the scaffold and defined the conditions for use in disease detection. Using the metastatic 4T1 triple-negative breast cancer model, we identified that a porous structure was essential to capture tumor and immune cells. Scaffolds of multiple diameters were investigated for their ability to serve as a metastatic niche, with a porous scaffold with a diameter as small as 2 mm identifying disease accurately. Additionally, scaffolds that had been in vivo for 1-5 weeks were able to identify disease accurately. Finally, the sensitivity of the scaffold relative to liquid biopsies was analyzed, with scaffolds accurately detecting disease at earlier time points than liquid biopsy. Collectively, these studies inform the design principles and use conditions for porous scaffolds to detect metastatic disease.

LAT1 transporter as a target for breast cancer diagnosis and therapy

Breast cancer is the main cause of female malignant tumor death in China. Numerous cellular molecules are associated with the onset and progression of breast cancer. However, these molecules have proven ineffective for the diagnosis and treatment of the disease, indicating a need for the identification of new biomarkers. LAT1 (SLC7A5) plays a crucial role in mediating the uptake of amino acids into breast cancer cells, influencing proliferation, invasion, migration, drug resistance, and prognosis through the mTOR signaling pathway. Notably, LAT1 exhibits differential expression across various types of breast cancer, positioning it as a promising candidate for diagnostic and therapeutic applications. Recent advancements in LAT1-targeting strategies for breast cancer have been made, particularly with the rapid developments in small molecular inhibitors and nanotechnology. In this article, we review the structure and function of LAT1, its relationship with breast cancer, and LAT1-mediated diagnostic and treatment strategies. This article specifically focuses on the LAT1-targeting strategy in breast tumors, aiming to evaluate its potential role as a novel biomarker for diagnosis and treatment.

Scaffolds functionalized with matrix metalloproteinase-responsive release of miRNA for synergistic magnetic hyperthermia and sensitizing chemotherapy of drug-tolerant breast cancer

Combining hyperthermia and chemotherapy for maximum anticancer efficacy remains a challenge because drug-tolerant cancer cells often evade this synergistic treatment due to drug resistance and asynchronous drug release. In this study, multifunctional scaffolds were designed to efficiently treat drug-tolerant breast cancer by improving the sensitization of breast cancer cells and synchronizing anticancer drug release with magnetic hyperthermia. The scaffolds contained microRNA-encapsulated matrix metalloproteinase-cleavable liposomes, doxorubicin-encapsulated thermoresponsive liposomes and Fe3O4 nanoparticles. The scaffolds could release microRNA specifically to improve the sensitization of breast cancer cells to anticancer drugs. The scaffolds also showed excellent hyperthermia effects under alternating magnetic field irradiation. Moreover, doxorubicin release was synchronized with magnetic hyperthermia. In vitro and in vivo studies demonstrated that the scaffolds effectively reduced drug resistance and eliminated doxorubicin-tolerant MDA-MB-231 cells through the synergistic effect of magnetic hyperthermia and sensitizing chemotherapy. Additionally, the scaffolds could support the proliferation and adipogenic differentiation of stem cells for adipose tissue regeneration after killing cancer cells at a late therapeutic stage. These composite scaffolds offer an innovative strategy for treating breast cancer, with synergistic anticancer effects and regenerative functions.

Investigating the anticancer properties of urolithin B in triple negative breast cancer: In vivo and in vitro insights

Breast cancer (BC) is a leading cause of cancer-related mortality among women worldwide, with incidence rates rising globally. Urolithin B (UB), a bioactive metabolite of ellagic acid, has demonstrated promising anticancer effects in various cancer models. This study aimed to evaluate the effects of UB on the growth, angiogenesis, and metastasis of BC cells using both in vivo and in vitro approaches. Cytotoxic effects of UB were assessed on MDA-MB-231 cells and normal HFF cells using the MTT assay. Scratch assays and gelatin zymography demonstrated UB's suppression of cell migration and reduced enzymatic activities of MMP-2 and MMP-9. In a xenograft mouse model, UB significantly reduced tumor growth, enhanced necrosis, and decreased vascularity in tumor tissues. It downregulated mRNA expression levels of VEGF, VEGFR, MMP-2, and MMP-9, indicating potent anti-angiogenic and anti-metastatic properties. Additionally, UB exhibited antioxidant effects by increasing total thiol content and the activities of superoxide dismutase (SOD) and catalase (CAT) while reducing malondialdehyde (MDA) levels in tumor tissues. In conclusion, our results highlight the anticancer potential of UB, through its ability to suppress the proliferation, angiogenesis, and metastatic properties of BC both in vitro and in vivo. Coupled with its antioxidant properties, UB emerges as a promising and safe candidate for further pre-clinical and clinical research and therapeutic applications in BC management.

Asymptotic analysis of mathematical model describing a new treatment of breast cancer using AZD9496 and palbociclib

Introduction

Cancer is a collective name for a group of diseases consisting of dozens of different types of malignant tumors, characterized by rapid and uncontrolled proliferation of cells in the body. Cancer can start almost anywhere in the human body such as the breast, prostate, colorectal, brain, bones, lungs, bladder etc. The main differences between the different types of cancer are related to the organ in which the tumor develops and the type of cells that compose the tumor.

Method

This paper focused on the breast cancer. Breast cancer is a malignant tumor that originates in the breast tissue. It is the most common malignant tumor in women. There are several types of breast cancer, but in all types early diagnosis and treatment is crucial. In this study, the treatment of breast cancer involving a combination of two drugs was investigated: the oral estrogen receptor inhibitor AZD9496 and the CDK4/6 protein inhibitor Palbociclib. The mathematical model that described the interaction between the cancer cells, the treatment, and the immune system cells includes a system of nonlinear ordinary differential equations of the firs order. In general, dynamic variables of a given system change each at a different rate. And it is not possible to know from the mathematical model which variable is fast and which is slow. Therefore, in order to reveal the hierarchy of the system of equations ,a numerical algorithm called the singularly perturbed vector field (SPVF) was applied. This algorithm transform the mathematical model to a new coordinate system in which the rate of change of each dynamic variable of the system can be known.

Results and Discussion

After writing the mathematical model in new coordinates, the equilibrium point was obtained analytically. The stability of the equilibrium points is investigated, which is essential from a practical perspective. Investigating the stability of the equilibrium points allows determination of when the tumor does not continue to develop and thereby allows adjustment of treatment continuation.

Rapid, Non-Invasive, Accurate Diagnosis and Efficient Clearance of Metastatic Lymph Nodes

Sentinel lymph node (SLN) biopsy is currently the standard procedure for clinical cancer diagnosis and treatment, but still faces the risks of false negatives and tumor metastasis, as well as time-consuming pathological evaluation procedure. Herein, we proposed a near-infrared-II (NIR-II, 1000-1700 nm) theranostic nanosystem (FLAGC) for rapid, non-invasive, accurate diagnosis and efficient clearance of metastatic lymph nodes in breast cancer. Initialized by chlorin e6 (Ce6), a pH-sensitive amphiphilic amino acid fluorenylmethoxycarbonyl-L-histidine (Fmoc-His) was assembled with Gd3+, luminol, and AgAuSe quantum dots (AAS QDs) to form FLAGC. In FLAGC, luminol and AAS QDs form a NIR-II chemical resonance energy transfer (CRET) system (Luminol-AAS); Ce6 initiates the assembly and also serves as a photosensitizer. Upon subcutaneous injection, FLAGC is easily drained into SLNs, achieving their precise localization. Subsequently, the acidity of tumor microenvironment triggers the rapid disassembly of FLAGC, exposing Luminol-AAS. myeloperoxidase (MPO) secreted by tumor-associated macrophages and neutrophils in SLNs mediates the oxidation of luminol, lighting up AAS QDs through the CRET process for precise diagnosis of metastatic lymph nodes. Moreover, highly efficient clearance of positive lymph nodes is achieved through Ce6-mediated photodynamic therapy. Our strategy provides a new paradigm for identifying and eliminating clinically metastatic lymph nodes.

CD44-Receptors-Mediated Multiprong Targeting Strategy Against Breast Cancer and Tumor-Associated Macrophages: Design, Optimization, Characterization, and Cytologic Evaluation

Introduction

Owing to its high prevalence, colossal potential of chemoresistance, metastasis, and relapse, breast cancer (BC) is the second leading cause of cancer-related fatalities in women. Several treatments (eg, chemotherapy, surgery, radiations, hormonal therapy, etc.) are conventionally prescribed for the treatment of BC; however, these are associated with serious systemic aftermaths. In this research, we aimed to design a multiprong targeting strategy for concurrent action against different phenotypes of BC (MCF-7 and SK-BR-3) and tumor-associated macrophages (TAMs) for relapse-free treatment of BC.

Methods

Paclitaxel (PTX) and tamoxifen (TMX) co-loaded chitosan (CS) nanoparticles (NPs) were prepared using the ionic-gelation method and optimized using the Design Expert® software by controlling different material attributes. For selective targeting through CD44-receptors that are heavily expressed on the BC cells and TAMs, the fabricated NPs (PTX-TMX-CS-NPs) were functionalized with hyaluronic acid (HA) as a targeting ligand.

Results

The optimized HA-PTX-TMX-CS-NPs exhibited desired physicochemical properties (PS ~230 nm, PDI 0.30, zeta potential ~21.5 mV), smooth spherical morphology, high encapsulation efficiency (PTX ~72% and TMX ~97%), good colloidal stability, and biphasic release kinetics. Moreover, the lowest cell viability depicted in MCF-7 (~25%), SK-BR-3 (~20%), and RAW 264.7 cells (~20%), induction of apoptosis, cell cycle arrest, enhanced cell internalization, and alleviation of MCF-7 and SK-BR-3 migration proved the superior anticancer potential of HA-PTX-TMX-CS-NPs compared to unfunctionalized NPs and other control medicines.

Conclusion

HA-functionalization of NPs is a promising multiprong strategy for CD44-receptors-mediated targeting of BC cells and TAMs to mitigate the progression, metastasis, and relapse in the BC.

XGBoost Enhances the Performance of SAFE: A Novel Microwave Imaging System for Early Detection of Malignant Breast Cancer

BACKGROUND/OBJECTIVES

Breast cancer is a significant global health concern, and early detection is crucial for improving patient outcomes. Mammography is widely used but has limitations, particularly for younger women with denser breasts. These include reduced sensitivity, false positives, and radiation risks. This highlights the need for alternative screening methods. In this study, we assess the performance of SAFE (Scan and Find Early), a novel microwave imaging device, in detecting breast cancer in a larger patient cohort. Unlike previous studies that predominantly relied on cross-validation, this study employs a more reliable, independent evaluation methodology to enhance generalizability.

METHODS

We developed an XGBoost model to classify breast cancer cases into positive (malignant) and negative (benign or healthy) groups. The model was analyzed with respect to key factors such as breast size, density, age, tumor size, and histopathological findings. This approach provides a better understanding of how these factors influence the model's performance, using an independent evaluation methodology for increased reliability.

RESULTS

Our results demonstrate that SAFE exhibits high sensitivity, particularly in dense breasts (91%) and younger patients (83%), suggesting its potential as a supplemental screening tool. Additionally, the system shows high detection accuracy for both small (<2 cm) and larger lesions, proving effective in early cancer detection.

CONCLUSIONS

This study reinforces the potential of SAFE to complement existing screening methods, particularly for patients with dense breasts, where mammography's sensitivity is reduced. The promising results warrant further research to solidify SAFE's clinical application as an alternative screening tool for breast cancer detection.

Revolutionizing early breast cancer screening: Advanced multi-spectral transmission imaging classification with improved Otsu's method and K-means clustering

Breast cancer stands as a formidable danger to the health of women worldwide, underscoring the critical need for effective screening methods. Multispectral transmission imaging offers a promising avenue due to its non-invasive potential for early screening. Some researchers already suggested registration to solve the problem of jitters due to respiration and movement and frame accumulation technology to solve the low grayscale problem. However, the classification of blood vessels and breast tissue in breast images often suffers from low signal-to-noise ratio (SNR) and low contrast, hindering accurate classification. This paper proposes a novel improved Otsu's method with K-Means clustering to address this challenge. The proposed method aims to enhance the foundation for classification using multispectral transmission images. The study utilizes multispectral transmission images captured at four wavelengths, representing an innovative avenue for early, affordable breast cancer screening research. Initially, 300 images are registered and accumulated to prove the efficiency of the suggested methodology. Then, median filtering is applied to reduce noise in the images. Improved Otsu's segmentation method is then employed to separate blood vessels from breast tissue. After that, K-means clustering is utilized to accurately classify these components. The results of the proposed method demonstrate significant improvements in classification accuracy and grayscale contrast of multispectral breast images. By effectively distinguishing blood vessels and breast tissue, the proposed methodology addresses the inherent challenges of low contrast in multispectral transmission imaging. This advancement offers a clearer pathway for early breast cancer screening.

Electrochemical biosensors for early detection of breast cancer

Breast cancer continues to be a significant contributor to global cancer deaths, particularly among women. This highlights the critical role of early detection and treatment in boosting survival rates. While conventional diagnostic methods like mammograms, biopsies, ultrasounds, and MRIs are valuable tools, limitations exist in terms of cost, invasiveness, and the requirement for specialized equipment and trained personnel. Recent shifts towards biosensor technologies offer a promising alternative for monitoring biological processes and providing accurate health diagnostics in a cost-effective, non-invasive manner. These biosensors are particularly advantageous for early detection of primary tumors, metastases, and recurrent diseases, contributing to more effective breast cancer management. The integration of biosensor technology into medical devices has led to the development of low-cost, adaptable, and efficient diagnostic tools. In this framework, electrochemical screening platforms have garnered significant attention due to their selectivity, affordability, and ease of result interpretation. The current review discusses various breast cancer biomarkers and the potential of electrochemical biosensors to revolutionize early cancer detection, making provision for new diagnostic platforms and personalized healthcare solutions.

Level of Awareness, Screening Practices, and Self-Detection Among Breast Cancer Patients

Objective

Having good knowledge and performing regular pre-tests under physician supervision play a crucial role in the early detection of breast cancer. The aim of this study was to investigate the level of awareness, frequency of performing routine screening, types of screening methods prior to detection, and who detected the case, among women diagnosed with breast cancer.

Materials and Methods

A cross-sectional study that used a designed questionnaire applied to investigate demographic data and four other aspects: level of awareness, screening practices, type of screening methods used, and who detected the case for the first time. Women who were diagnosed with breast cancer and registered at Nanakali Hospital were included.

Results

A total of 150 women participated. Most of the participants (80%) had no previous knowledge regarding causes, signs and symptoms, or detection methods, while only 20% had little information. Among the participants, most (87.3%) did not undergo any pre-tests before the time of diagnosis, while only 12.7% did pre-test at least once. The screening methods used prior to the diagnosis were: breast self-exam (n=9); ultrasonography (n=8), and only two had mammography. Detecting the case for the first time, 68.7% of the cases were detected by chance or accidentally, and 31.3% were detected by physicians.

Conclusion

The level of awareness and performance of routine screening differ greatly among different populations and countries. Women in Erbil, generally have a low level of awareness and insufficient knowledge regarding breast cancer; most women do not undertake any regular screening for early detection of this cancer compared to Western countries. Having previous knowledge and doing pre-tests regularly play a key role in the early detection of this cancer, which minimizes the consequences.

Clinical applications of volatilomic assays

The study of metabolomics is revealing immense potential for diagnosis, therapy monitoring, and understanding of pathogenesis processes. Volatilomics is a subcategory of metabolomics interested in the detection of molecules that are small enough to be released in the gas phase. Volatile compounds produced by cellular processes are released into the blood and lymph, and can reach the external environment through different pathways, such as the blood-air interface in the lung that are detected in breath, or the blood-water interface in the kidney that leads to volatile compounds detected in urine. Besides breath and urine, additional sources of volatile compounds such as saliva, blood, feces, and skin are available. Volatilomics traces its roots back over fifty years to the pioneering investigations in the 1970s. Despite extensive research, the field remains in its infancy, hindered by a lack of standardization despite ample experimental evidence. The proliferation of analytical instrumentations, sample preparations and methods of volatilome sampling still make it difficult to compare results from different studies and to establish a common standard approach to volatilomics. This review aims to provide an overview of volatilomics' diagnostic potential, focusing on two key technical aspects: sampling and analysis. Sampling poses a challenge due to the susceptibility of human samples to contamination and confounding factors from various sources like the environment and lifestyle. The discussion then delves into targeted and untargeted approaches in volatilomics. Some case studies are presented to exemplify the results obtained so far. Finally, the review concludes with a discussion on the necessary steps to fully integrate volatilomics into clinical practice.

CircRNAs and miRNAs: Key Player Duo in Breast Cancer Dynamics and Biomarkers for Breast Cancer Early Detection and Prevention

Breast cancer (BC) remains a significant global health issue, necessitating advanced molecular approaches for early detection and prevention. This review delves into the roles of microRNAs (miRNAs) and circular RNAs (circRNAs) in BC, highlighting their potential as non-invasive biomarkers. Utilizing in silico tools and databases, we propose a novel methodology to establish mRNA/circRNA/miRNA axes possibly indicative of early detection and possible prevention. We propose that during early tumor initiation, some changes in oncogene or tumor suppressor gene expression (mRNA) are mirrored by alterations in corresponding circRNAs and reciprocal changes in sponged miRNAs affecting tumorigenesis pathways. We used two Gene Expression Omnibus (GEO) datasets and identified five mRNA/circRNA/miRNA axes as early possible tumor initiation biomarkers. We further validated the proposed axes through a Kaplan-Meier (KM) plot and enrichment analysis of miRNA expression using patient data. Evaluating coupled differential expression of circRNAs and miRNAs in body fluids or exosomes provides greater confidence than assessing either, with more axes providing even greater confidence. The proposed methodology not only improves early BC detection reliability but also has applications for other cancers, enhancing preventive measures.

Immuno-PET Imaging of CD93 Expression with 64Cu-Radiolabeled NOTA-mCD93 ([64Cu]Cu-NOTA-mCD93) and Insulin-Like Growth Factor Binding Protein 7 ([64Cu]Cu-NOTA-IGFBP7)

CD93 is overexpressed in multiple solid tumor types, serving as a novel target for antiangiogenic therapy. The goal of this study was to develop a 64Cu-based positron emission tomography (PET) tracer for noninvasive imaging of CD93 expression. Antimouse-CD93 mAb (mCD93) and the CD93 ligand IGFBP7 were conjugated to a bifunctional chelator, p-isothiocyanatobenzyl-1,4,7-triazacyclononane-1,4,7-triacetic acid (p-SCN-NOTA) and labeled with 64Cu. To evaluate the pharmacokinetic properties and tumor-targeting efficacy of [64Cu]Cu-NOTA-mCD93 and [64Cu]Cu-NOTA-IGFBP7, PET imaging and biodistribution were performed on both 4T1 murine breast tumor-bearing mice and MDA-MB-231 human breast tumor-bearing mice. The tumor model HT1080-FAP, which does not overexpress CD93, was used as a negative control. Fluorescent immunostaining was conducted on different tissues to correlate radiotracer uptake with CD93 expression. 64Cu-labeling was achieved with high yield and specific activity. Serial PET imaging revealed that the in vivo performance of [64Cu]Cu-NOTA-IGFBP7 was superior to that of [64Cu]Cu-NOTA-mCD93, and that the tracer [64Cu]Cu-NOTA-IGFBP7 exhibited elevated tumor uptake values and excellent tumor retention in MDA-MB-231 mice, rather than in 4T1 murine mice. The MDA-MB-231 tumor uptake of [64Cu]Cu-NOTA-IGFBP7 was 2.85 ± 0.15, 3.69 ± 0.60, 6.91 ± 0.88, and 6.35 ± 0.55%ID/g at 1, 4, 24, and 48 h p.i., respectively, which were significantly higher than that in the CD93-negative HT1080-FAP tumor (0.73 ± 0.15, 0.97 ± 0.31, 1.00 ± 0.07, and 1.02 ± 0.11%ID/g, respectively). The significant difference between positive and negative tumors indicated [64Cu]Cu-NOTA-IGFBP7 was specifically binding to CD93. Biodistribution data as measured by gamma counting were consistent with the PET analysis. Ex vivo histology further confirmed the high CD93 expression on MDA-MB-231 tumor tissues. Herein, we prepared two novel radiotracers, [64Cu]Cu-NOTA-mCD93 and [64Cu]Cu-NOTA-IGFBP7, for the first immune-PET imaging of CD93 expression. Our results suggest that [64Cu]Cu-NOTA-IGFBP7 is a more potential radiotracer for visualizing angiogenesis due to its sensitive, persistent, and CD93-specific characteristics.

Breast cancer epidemiology, diagnostic barriers, and contemporary trends in breast nanotheranostics and mechanisms of targeting

INTRODUCTION

Breast cancer is one of the main causes of mortality in women globally. Early and accurate diagnosis represents a milestone in cancer management. Several breast cancer diagnostic agents are available. Many chemotherapeutic agents in conventional dosage forms are approved; nevertheless, they lack cancer cell specificity, resulting in improper treatment and undesirable side effects. Recently, nanotheranostics has emerged as a new paradigm to achieve safe and effective cancer diagnosis and management.

AREA COVERED

This review provides insight into breast cancer epidemiology, barriers hindering the early diagnosis, and effective delivery of chemotherapeutics. Also, conventional diagnostic agents and recent nanotheranostic platforms have been used in breast cancer. In addition, mechanisms of cancer cell targeting and nano-carrier surface functionalization as an effective approach for chemotherapeutic targeting were reviewed along with future perspectives.

EXPERT OPINION

We proposed that modified nano-carriers may provide an efficacious approach for breast cancer drug targeting. These nanotheranostics need more clinical evaluations to confirm their efficacy in cancer management. In addition, we recommend the use of artificial intelligence (AI) as a promising approach for early and efficient assessment of breast lesions. AI allows better interpretation and analysis of nanotheranostic data, which minimizes misdiagnosis and avoids the belated intervention of health care providers.

Inhibitors of the mTOR signaling pathway can play an important role in breast cancer immunopathogenesis

This study explores the critical role of inhibitors targeting the mammalian target of rapamycin (mTOR) signaling pathway in breast cancer research and treatment. The mTOR pathway, a central regulator of cellular processes, has been identified as a crucial factor in the development and progression of breast cancer. The essay explains the complex molecular mechanisms through which mTOR inhibitors, such as rapamycin and its analogs, exert their anticancer effects. These inhibitors can stop cell growth, proliferation, and survival in breast cancer cells by blocking critical signaling pathways within the mTOR pathway. Furthermore, the essay discusses the implications of using mTOR inhibitors as a comprehensive therapeutic strategy. It emphasizes the potential benefits of combining mTOR inhibitors with other treatment approaches to enhance the effectiveness of breast cancer treatment. The evolving landscape of breast cancer research underscores the significance of mTOR as a therapeutic target and highlights ongoing efforts to improve and optimize mTOR inhibitors for clinical use. In conclusion, the essay asserts that inhibitors of the mTOR signaling pathway offer a promising approach in the fight against breast cancer. These inhibitors provide a focused and effective intervention targeting specific dysregulations within the mTOR pathway. As research advances, the integration of mTOR inhibitors into customized combination therapies holds excellent potential for shaping a more effective and personalized approach to breast cancer treatment, ultimately leading to improved outcomes for individuals affected by this complex and diverse disease.

Diffuse correlation tomography: a technique to characterize tissue blood flow abnormalities in benign and malignant breast lesions

Accurate assessment and quantification of neoangiogenesis associated with breast cancer could be potentially used to improve the sensitivity and specificity of non-invasive diagnosis, as well as predict outcomes and monitor treatment effects. In this study, we adapted an emerging technology, namely diffuse correlation tomography (DCT), to image microvascular blood flow in breast tissues and evaluate the potential for discriminating between benign and malignant lesions. A custom-made DCT system was designed for breast blood flow imaging, with both the source-detector array and reconstruction algorithm optimized to ensure precise imaging of breast blood flow. The global features and local features of three-dimensional blood flow images were extracted from the relative blood flow index (rBFI), which was obtained from most of the breasts targeted to the lesion. A total of 37 women with 19 benign and 18 malignant lesions were included in the study. Significant differences between malignant and benign groups were found in 12 image features. Moreover, when selecting the lesion mean relative blood flow index (MrBFI) as a single indicator, the malignant and benign tumors were discriminated with an accuracy of 89.2%. The blood flow features were found to successfully identify malignant and benign tumors, suggesting that DCT, as an alternate functional imaging modality, has the potential to be translated into clinical practice for diagnosis and assessment of breast cancers. There is potential to reduce the need for biopsy of benign lesions by improving the specificity of diagnostic imaging, as well as monitoring response to breast cancer treatment.

Alginate-based hydrogels mediated biomedical applications: A review

With the development in the field of biomaterials, research on alternative biocompatible materials has been initiated, and alginate in polysaccharides has become one of the research hotspots due to its advantages of biocompatibility, biodegradability and low cost. In recent years, with the further understanding of microscopic molecular structure and properties of alginate, various physicochemical methods of cross-linking strategies, as well as organic and inorganic materials, have led to the development of different properties of alginate hydrogels for greatly expanded applications. In view of the potential application prospects of alginate-based hydrogels, this paper reviews the properties and preparation of alginate-based hydrogels and their major achievements in delivery carrier, dressings, tissue engineering and other applications are also summarized. In addition, the combination of alginate-based hydrogel and new technology such as 3D printing are also involved, which will contribute to further research and exploration.

Her2-positive breast cancer in a young patient with Li-Fraumeni syndrome: A comprehensive case study

INTRODUCTION AND IMPORTANCE

Li-Fraumeni syndrome (LFS) is a rare hereditary disorder caused by mutations in the TP53 gene, leading to a significantly increased risk of developing various cancers at a young age, including breast cancer.

CLINICAL PRESENTATION

This case report details the clinical journey of a 21-year-old female diagnosed with Grade 3 invasive ductal carcinoma, which was estrogen receptor low positive and progesterone receptor negative but positive for Her2 (3+) with a high Ki67 proliferation index.

CLINICAL DISCUSSION

Genetic testing confirmed a TP53 mutation, establishing the diagnosis of LFS. The patient underwent neoadjuvant chemotherapy with TCHP (docetaxel, carboplatin, trastuzumab, pertuzumab), resulting in a complete clinical response. This was followed by bilateral skin-sparing and nipple-sparing mastectomy with sentinel lymph node biopsy and immediate reconstruction. Postoperative pathology confirmed a complete response to neoadjuvant therapy. The patient's treatment plan includes 12 cycles of trastuzumab and pertuzumab, with regular echocardiograms to monitor cardiac function and fertility preservation strategies involving monthly Lupron injections. Given the association of LFS with a high risk of multiple primary cancers, a rigorous surveillance strategy is essential. The psychological impact of a cancer diagnosis and the burden of living with a hereditary cancer syndrome were significant, necessitating comprehensive psychosocial support.

CONCLUSION

Managing Li-Fraumeni syndrome (LFS) and its associated cancers, particularly in young patients, necessitates a comprehensive and multidisciplinary approach. Early genetic testing for TP53 mutations is crucial in identifying LFS, enabling personalized treatment plans and proactive surveillance strategies.

A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis

BACKGROUND/AIM

Breast cancer remains a major global health concern. This study aimed to develop a deep-learning-based artificial intelligence (AI) model that predicts the malignancy of mammographic lesions and reduces unnecessary biopsies in patients with breast cancer.

PATIENTS AND METHODS

In this retrospective study, we used deep-learning-based AI to predict whether lesions in mammographic images are malignant. The AI model learned the malignancy as well as margins and shapes of mass lesions through multi-label training, similar to the diagnostic process of a radiologist. We used the Curated Breast Imaging Subset of Digital Database for Screening Mammography. This dataset includes annotations for mass lesions, and we developed an algorithm to determine the exact location of the lesions for accurate classification. A multi-label classification approach enabled the model to recognize malignancy and lesion attributes.

RESULTS

Our multi-label classification model, trained on both lesion shape and margin, demonstrated superior performance compared with models trained solely on malignancy. Gradient-weighted class activation mapping analysis revealed that by considering the margin and shape, the model assigned higher importance to border areas and analyzed pixels more uniformly when classifying malignant lesions. This approach improved diagnostic accuracy, particularly in challenging cases, such as American College of Radiology Breast Imaging-Reporting and Data System categories 3 and 4, where the breast density exceeded 50%.

CONCLUSION

This study highlights the potential of AI in improving the diagnosis of breast cancer. By integrating advanced techniques and modern neural network designs, we developed an AI model with enhanced accuracy for mammographic image analysis.

The Role of the Microbiome and of Radiotherapy-Derived Metabolites in Breast Cancer

The gut microbiome has emerged as a crucial player in modulating cancer therapies, including radiotherapy. In the case of breast cancer, the interplay between the microbiome and radiotherapy-derived metabolites may enhance therapeutic outcomes and minimize adverse effects. In this review, we explore the bidirectional relationship between the gut microbiome and breast cancer. We explain how gut microbiome composition influences cancer progression and treatment response, and how breast cancer and its treatments influence microbiome composition. A dual role for radiotherapy-derived metabolites is explored in this article, highlighting both their therapeutic benefits and potential hazards. By integrating genomics, metabolomics, and bioinformatics tools, we present a comprehensive overview of these interactions. The study provides real-world insight through case studies and clinical trials, while therapeutic innovations such as probiotics, and dietary interventions are examined for their potential to modulate the microbiome and enhance treatment effectiveness. Moreover, ethical considerations and patient perspectives are discussed, ensuring a comprehensive understanding of the subject. Towards revolutionizing treatment strategies and improving patient outcomes, the review concludes with future research directions. It also envisions integrating microbiome and metabolite research into personalized breast cancer therapy.

A rare case of a solitary osseous metastasis from breast carcinoma presenting with fluid-fluid levels on MRI

Osseous metastatic disease is commonly encountered in breast carcinoma, which typically presents as either osteolytic, osteoblastic, or mixed lesions on imaging. Osseous metastasis presenting as a multiloculated cystic lesion with fluid-fluid levels resembling that of an aneurysmal bone cyst (ABC) is sparsely described in the literature, and even less so in the case of breast carcinoma. We report an unusual case of fluid-fluid levels in a bone metastasis to the spine in a 66-year-old female with a prior history of breast carcinoma. Magnetic resonance imaging (MRI) demonstrated a cystic lesion with fluid levels resembling that of an ABC. Computed tomography (CT)-guided biopsy revealed the lesion to be a metastasis from breast carcinoma. The management of ABCs and osseous metastases differ drastically. Accurate diagnosis and distinction between these lesions is paramount as the management of metastatic disease can have a significant impact on the quality and length of life. The presentation, differential diagnoses and imaging features of this atypical case are discussed.

Economics of Antibody Drug Conjugates (ADCs): Innovation, Investment and Market Dynamics

PURPOSE OF REVIEW

This review aims to explore the intricate interplay between scientific advancements and economic considerations in the development, production, and commercialization of Antibody Drug Conjugates (ADCs). The focus is on understanding the challenges and opportunities at this unique intersection, highlighting how scientific innovation and economic dynamics mutually influence the trajectory of ADCs in the pharmaceutical landscape.

RECENT FINDINGS

There has been a significant increase in interest and investment in the development of ADCs. Initially focused on hematological malignancies, ADCs are now being researched for use in treating solid tumors as well. Pharmaceutical companies are heavily investing to broaden the range of indications for which ADCs can be effective. According to a report from the end of 2023, the global ADCs market grew from USD 1.4 billion in 2016 to USD 11.3 billion in 2023, with projections estimating a value of USD 23.9 billion by 2032, growing at a CAGR of 10.7%. ADCs represent a promising class of biopharmaceuticals in oncology, with expanding applications beyond hematological malignancies to solid tumors. The significant growth in the ADC market underscores the impact of scientific and economic factors on their development. This review provides valuable insights into how these factors drive innovation and commercialization, shaping the future of ADCs in cancer treatment.

Predicting HER2 Status Associated with Breast Cancer Aggressiveness Using Four Machine Learning Models

OBJECTIVE

Breast cancer (BC) is a heterogeneous disease with various biological and clinical subtypes. HER2 status (human epidermal growth factor receptor 2) is a crucial biomarker, associated with aggressive tumor behavior and poor prognosis. Advanced algorithmic models can aid in predicting cancer growth and metastasis, serving as valuable clinical tools for classification and treatment. Effective treatment strategies in oncology rely on accurate decision-making and early identification of factors associated with positive outcomes. Breast cancer (BC) presents challenges in understanding its contributing factors and establishing precise diagnostic methods. Our research introduces a novel method utilizing machine learning (ML) techniques to explore the relationship between various clinical and molecular variables focusing on predicting the status of the human epidermal growth factor receptor 2 (HER2), a key aggressiveness biomarker in BC. This objective aligns with leveraging artificial intelligence (AI) to support decision-making and address diagnostic considerations during treatment.

METHODS

Four ML models, namely logistic regression, random forest, LightGBM, and CatBoost, were implemented and evaluated using Python. The dataset was compiled by extracting medical records of BC patients, covering the period from 2018 to 2020. The model's predictive performance was evaluated using accuracy, precision, recall, and F1-score as performance metrics.

RESULT

The models achieved varying accuracies between 86.36 and 95.45%. The logistic regression model achieved an accuracy of 90.90% while the random forest and LightGBM models achieved an accuracy of 86.36%. The CatBoost model outperformed others with a greater accuracy of 95.45%, indicating its superior predictive capabilities for HER2 status. The ML models demonstrated potential in predicting HER2 status, enabling early detection and facilitating personalized treatment strategies.

CONCLUSION

Our findings emphasize the significance of AI and ML techniques in improving BC outcomes and guiding decision-making. Further research is required to explore the broader applications of ML in predicting comprehensive BC outcomes in diverse healthcare settings and among heterogeneous populations.

Mathematical Models for Ultrasound Elastography: Recent Advances to Improve Accuracy and Clinical Utility

Changes in biomechanical properties such as elasticity modulus, viscosity, and poroelastic features are linked to the health status of biological tissues. Ultrasound elastography is a non-invasive imaging tool that quantitatively maps these biomechanical characteristics for diagnostic and treatment monitoring purposes. Mathematical models are essential in ultrasound elastography as they convert the raw data obtained from tissue displacement caused by ultrasound waves into the images observed by clinicians. This article reviews the available mathematical frameworks of continuum mechanics for extracting the biomechanical characteristics of biological tissues in ultrasound elastography. Continuum-mechanics-based approaches such as classical viscoelasticity, elasticity, and poroelasticity models, as well as nonlocal continuum-based models, are described. The accuracy of ultrasound elastography can be increased with the recent advancements in continuum modelling techniques including hyperelasticity, biphasic theory, nonlocal viscoelasticity, inversion-based elasticity, and incorporating scale effects. However, the time taken to convert the data into clinical images increases with more complex models, and this is a major challenge for expanding the clinical utility of ultrasound elastography. As we strive to provide the most accurate imaging for patients, further research is needed to refine mathematical models for incorporation into the clinical workflow.

Assessing Awareness, Attitude, and Practices of Breast Cancer Screening and Prevention Among General Public and Physicians in Pakistan: A Nation With the Highest Breast Cancer Incidence in Asia

Introduction: Breast cancer is a global health challenge with significant mortality, affecting millions worldwide. The current study is aimed at evaluating awareness and practices related to breast cancer screening, prevention, and treatment among the general public and physicians in Lahore, Pakistan, which has a significant incidence of breast cancer. Methodology: The current study adopted a cross-sectional study design conducted in Lahore, Pakistan, between March and August 2023, among 404 participants from the general public and 240 physicians. Data collection and evaluation involved the use of validated questionnaires, and both descriptive and inferential statistics were performed using SPSS Version 25. Result: In Lahore, Pakistan, breast cancer awareness among the public was low, with 80.2% unaware of its global prevalence, 65.3% believing not everyone is at risk, and only 42.1% recognizing symptoms. Females showed greater awareness (OR: 1.020, CI: 0.617-1.686, p = 0.002) and positive attitudes (OR: 2.711, CI: 1.478-6.478, p = 0.045), while the 18-29 age group had higher odds of positive practices (OR: 4.317, CI: 2.678-5.956, p = 0.004). Educational attainment significantly influences knowledge and attitudes. Only 13.9% practiced self-examination. Among physicians, 88.8% were confident in screenings, but patient fear (42.9%) and financial barriers (79.2%) hindered action. Physicians with FCPS qualifications had higher odds of awareness (OR: 1.550, CI: 1.130-2.117, p = 0.007), attitudes (OR: 1.500, CI: 1.050-2.150, p = 0.025), and practices (OR: 1.470, CI: 1.070-2.017, p = 0.020). Those with 11-20 years of experience also showed better awareness (OR: 1.400, CI: 1.050-1.868, p = 0.022) and attitudes (OR: 1.450, CI: 1.045-2.018, p = 0.029). Conclusion: In conclusion, breast cancer awareness among the general public is limited, highlighting the need for tailored education programs. Although most physicians show high awareness, challenges in patient communication and barriers, such as fear and financial constraints, must be addressed to improve screening uptake. These findings emphasize the importance of targeted interventions to enhance public awareness, screening practices, and physician-patient communication.

SEPT9_i1 and Septin Dynamics in Oncogenesis and Cancer Treatment

Despite significant advancements in the field of oncology, cancers still pose one of the greatest challenges of modern healthcare. Given the cytoskeleton's pivotal role in regulating mechanisms critical to cancer development, further studies of the cytoskeletal elements could yield new practical applications. Septins represent a group of relatively well-conserved GTP-binding proteins that constitute the fourth component of the cytoskeleton. Septin 9 (SEPT9) has been linked to a diverse spectrum of malignancies and appears to be the most notable septin member in that category. SEPT9 constitutes a biomarker of colorectal cancer (CRC) and has been positively correlated with a high clinical stage in breast cancer, cervical cancer, and head and neck squamous cell carcinoma. SEPT9_i1 represents the most extensively studied isoform of SEPT9, which substantially contributes to carcinogenesis, metastasis, and treatment resistance. Nevertheless, the mechanistic basis of SEPT9_i1 oncogenicity remains to be fully elucidated. In this review, we highlight SEPT9's and SEPT9_i1's structures and interactions with Hypoxia Inducible Factor α (HIF-1 α) and C-Jun N-Terminal Kinase (JNK), as well as discuss SEPT9_i1's contribution to aneuploidy, cell invasiveness, and taxane resistance-key phenomena in the progression of malignancies. Finally, we emphasize forchlorfenuron and other septin inhibitors as potential chemotherapeutics and migrastatics.

A Breast Tumor Monitoring Vest with Flexible UWB Antennas-A Proof-of-Concept Study Using Realistic Breast Phantoms

Breast cancers can appear and progress rapidly, necessitating more frequent monitoring outside of hospital settings to significantly reduce mortality rates. Recently, there has been considerable interest in developing techniques for portable, user-friendly, and low-cost breast tumor monitoring applications, enabling frequent and cost-efficient examinations. Microwave technique-based breast cancer detection, which is based on differential dielectric properties of malignant and healthy tissues, is regarded as a promising solution for cost-effective breast tumor monitoring. This paper presents the development process of the first proof-of-concept of a breast tumor monitoring vest which is based on the microwave technique. Two unique vests are designed and evaluated on realistic 3D human tissue phantoms having different breast density types. Additionally, the measured results are verified using simulations carried out on anatomically realistic voxel models of the electromagnetic simulations. The radio channel characteristics are evaluated and analyzed between the antennas embedded in the vest in tumor cases and reference cases. Both measurements and simulation results show that the proposed vest can detect tumors even if only 1 cm in diameter. Additionally, simulation results show detectability with 0.5 cm tumors. It is observed that the detectability of breast tumors depends on the frequency, antenna selection, size of the tumors, and breast types, causing differences of 0.5-30 dB in channel responses between the tumorous and reference cases. Due to simplicity and cost-efficiency, the proposed channel analysis-based breast monitoring vests can be used for breast health checks in smaller healthcare centers and for user-friendly home monitoring which can prove beneficial in rural areas and developing countries.

Theranostics: aptamer-assisted carbon nanotubes as MRI contrast and photothermal agent for breast cancer therapy

Breast cancer is one of the leading causes of death among women globally, making its diagnosis and treatment challenging. The use of nanotechnology for cancer diagnosis and treatment is an emerging area of research. To address this issue, multiwalled carbon nanotubes (MWCNTs) were ligand exchanged with butyric acid (BA) to gain hydrophilic character. The successful functionalization was confirmed by FTIR spectroscopy. Surface morphology changes were observed using SEM, while TEM confirmed the structural integrity of the MWCNTs after functionalization. Particle size, zeta potential, and UV spectroscopy were also performed to further characterize the nanoparticles. The breast cancer aptamer specific to Mucin-1 (MUC-1) was then conjugated with the functionalized MWCNTs. These MWCNTs successfully targeted breast cancer cells (MDA-MB-231) as examined by cellular uptake studies and exhibited a reduction in cancer-induced inflammation, as evidenced by gene transcription (qPCR) and protein expression (immunoblotting) levels. Immunoblot and confocal-based immunofluorescence assay (IFA) indicated the ability of CNTs to induce photothermal cell death of MDA-MB-231 cells. Upon imaging, cancer cells were effectively visualized due to the MWCNTs' ability to act as magnetic resonance imaging (MRI) contrast agents. Additionally, MWCNTs demonstrated photothermal capabilities to eliminate bound cancer cells. Collectively, our findings pave the way for developing aptamer-labeled MWCNTs as viable "theranostic alternatives" for breast cancer treatment.

MRI radiomics and biological correlations for predicting axillary lymph node burden in early-stage breast cancer

BACKGROUND AND AIMS

Preoperative prediction of axillary lymph node (ALN) burden in patients with early-stage breast cancer is pivotal for individualised treatment. This study aimed to develop a MRI radiomics model for evaluating the ALN burden in early-stage breast cancer and to provide biological interpretability to predictions by integrating radiogenomic data.

METHODS

This study retrospectively analyzed 1211 patients with early-stage breast cancer from four centers, supplemented by data from The Cancer Imaging Archive (TCIA) and Duke University (DUKE). MRI radiomic features were extracted from dynamic contrast-enhanced MRI images and an ALN burden-related radscore was constructed by the backpropagation neural network algorithm. Clinical and combined models were developed, integrating ALN-related clinical variables and radscore. The Kaplan-Meier curve and log-rank test were used to assess the prognostic differences between the predicted high- and low-ALN burden groups in both Center I and DUKE cohorts. Gene set enrichment and immune infiltration analyses based on transcriptomic TCIA and TCIA Breast Cancer dataset were used to investigate the biological significance of the ALN-related radscore.

RESULTS

The MRI radiomics model demonstrated an area under the curve of 0.781-0.809 in three validation cohorts. The predicted high-risk population demonstrated a poorer prognosis (log-rank P < .05 in both cohorts). Radiogenomic analysis revealed migration pathway upregulation and cell differentiation pathway downregulation in the high radscore groups. Immune infiltration analysis confirmed the ability of radiological features to reflect the heterogeneity of the tumor microenvironment.

CONCLUSIONS

The MRI radiomics model effectively predicted the ALN burden and prognosis of early-stage breast cancer. Moreover, radiogenomic analysis revealed key cellular and immune patterns associated with the radscore.

Patient journey and timeliness of care for patients with breast cancer in Africa: a scoping review protocol

INTRODUCTION

Cancer is the leading cause of death worldwide, with breast cancer being one of the most commonly diagnosed types. Low-income and middle-income countries account for nearly half of all breast cancer cases and related fatalities. In Africa, mortality rates are higher and survival rates are lower compared with developed countries. Timeliness of care is a critical aspect of healthcare, but various studies and healthcare systems use different criteria and methods to measure it. Assessing the breast cancer care pathway and understanding the determinants of delayed care are essential for effective interventions. Therefore, this scoping review aims to evaluate the methods used to measure the timeliness of breast cancer care, identify specific points in the care pathway where delays are most frequently reported, and examine the factors affecting the timeliness of breast cancer care in Africa.

METHODS AND ANALYSIS

We will conduct this scoping review using the Arksey and O'Malley framework endorsed by the Joanna Briggs Institute. A scoping review of articles written in English concerning the timeliness of breast cancer care in the African context will be undertaken. Six electronic databases (MEDLINE, EMBASE, CINAHL, SCOPUS, WEB Of SCIENCE and PsycINFO) will be searched to identify published literature on timeliness of breast care in Africa. Two reviewers will independently screen the articles at both the abstract and full-text stages, guided by predetermined inclusion and exclusion criteria. The full texts of identified studies will be further examined and charted using a data extraction form guided by the Model of Pathways to Treatment framework. Publications describing the time to diagnosis and its associated factors in the contexts of breast cancer will be considered for inclusion, with no restrictions based on date of publication. Studies that are published in languages other than English and that do not focus on the timeliness of care or time-related aspects within the care pathway will be excluded. Evidence will be narratively synthesised and analysed.

ETHICS AND DISSEMINATION

Ethical approval is not needed as this scoping review does not involve collecting data from human participants. The results produced from this review will be submitted to a scientific peer-reviewed journal for publication and will be presented at scientific meetings.

Exploring Radioiodinated Anastrozole and Epirubicin as AKT1-Targeted Radiopharmaceuticals in Breast Cancer: In Silico Analysis and Potential Therapeutic Effect with Functional Nuclear Imagining Implications

This study evaluates radio-iodinated anastrozole ([125I]anastrozole) and epirubicin ([125I]epirubicin) for AKT1-targeted breast cancer therapy, utilizing radiopharmaceutical therapy (RPT) for personalized treatment. Through molecular docking and dynamics simulations (200 ns), it investigates these compounds' binding affinities and mechanisms to the AKT1 enzyme, compared to the co-crystallized ligand, a known AKT1 inhibitor. Molecular docking results show that [125I]epirubicin has the highest ΔGbind (-11.84 kcal/mol), indicating a superior binding affinity compared to [125I] anastrozole (-10.68 kcal/mol) and the co-crystallized ligand (-9.53 kcal/mol). Molecular dynamics (MD) simulations confirmed a stable interaction with the AKT1 enzyme, with [125I]anastrozole and [125I]epirubicin reaching stability after approximately 68 ns with an average RMSD of around 2.2 Å, while the co-crystallized ligand stabilized at approximately 2.69 Å after 87 ns. RMSF analysis showed no significant shifts in residues or segments, with consistent patterns and differences of less than 2 Å, maintaining enzyme stability. The [125I]epirubicin complex maintained an average of four H-bonds, indicating strong and stable interactions, while [125I]anastrozole consistently formed three H-bonds. The average Rg values for both complexes were ~16.8 ± 0.1 Å, indicating no significant changes in the enzyme's compactness, thus preserving structural integrity. These analyses reveal stable binding and minimal structural perturbations, suggesting the high potential for AKT1 inhibition. MM-PBSA calculations confirm the potential of these radio-iodinated compounds as AKT1 inhibitors, with [125I]epirubicin exhibiting the most favorable binding energy (-23.57 ± 0.14 kcal/mol) compared to [125I]anastrozole (-20.03 ± 0.15 kcal/mol) and the co-crystallized ligand (-16.38 ± 0.14 kcal/mol), highlighting the significant role of electrostatic interactions in stabilizing the complex. The computational analysis shows [125I]anastrozole and [125I]epirubicin may play promising roles as AKT1 inhibitors, especially [125I]epirubicin for its high binding affinity and dynamic receptor interactions. These findings, supported by molecular docking scores and MM-PBSA binding energies, advocate for their potential superior inhibitory capability against the AKT1 enzyme. Nevertheless, it is crucial to validate these computational predictions through in vitro and in vivo studies to thoroughly evaluate the therapeutic potential and viability of these compounds for AKT1-targeted breast cancer treatment.

pH-Sensitive Fluorescent Probe in Nanogel Particles as Theragnostic Agent for Imaging and Elimination of Latent Bacterial Cells Residing Inside Macrophages

Rhodamine 6G (R6G) and 4-nitro-2,1,3-benzoxadiazole (NBD) linked through a spacer molecule spermidine (spd), R6G-spd-NBD, produces a fluorescent probe with pH-sensitive FRET (Förster (fluorescence) resonance energy transfer) effect that can be useful in a variety of diagnostic applications. Specifically, cancer cells can be spotted due to a local decrease in pH (Warburg effect). In this research, we applied this approach to intracellular infectious diseases-namely, leishmaniasis, brucellosis, and tuberculosis, difficult to treat because of their localization inside macrophages. R6G-spd-NBD offers an opportunity to detect such bacteria and potentially deliver therapeutic targets to treat them. The nanogel formulation of the R6G-spd-NBD probe (nanoparticles based on chitosan or heparin grafted with lipoic acid residues, Chit-LA and Hep-LA) was obtained to improve the pH sensitivity in the desired pH range (5.5-7.5), providing selective visualization and targeting of bacterial cells, thereby enhancing the capabilities of CLSM (confocal laser scanning microscopy) imaging. According to AFM (atomic force microscopy) data, nanogel particles containing R6G-spd-NBD of compact structure and spherical shape are formed, with a diameter of 70-100 nm. The nanogel formulation of the R6G-spd-NBD further improves absorption and penetration into bacteria, including those located inside macrophages. Due to the negative charge of the bacteria surface, the absorption of positively charged R6G-spd-NBD, and even more so in the chitosan derivatives' nanogel particles, is pronounced. Additionally, with a pH-sensitive R6G-spd-NBD fluorescent probe, the macrophages' lysosomes can be easily distinguished due to their acidic pH environment. CLSM was used to visualize samples of macrophage cells containing absorbed bacteria. The created nanoparticles showed a significant selectivity to model E. coli vs. Lactobacillus bacterial cells, and the R6G-spd-NBD agent, being a mild bactericide, cleared over 50% E.coli in conditions where Lactobacillus remained almost unaffected. Taken together, our data indicate that R6G-spd-NBD, as well as similar compounds, can have value not only for diagnostic, but also for theranostic applications.

Correlations of Imaging and Therapy in Breast Cancer Based on Molecular Patterns: An Important Issue in the Diagnosis of Breast Cancer

Breast cancer is a global health issue affecting countries worldwide, imposing a significant economic burden due to expensive treatments and medical procedures, given the increasing incidence. In this review, our focus is on exploring the distinct imaging features of known molecular subtypes of breast cancer, underlining correlations observed in clinical practice and reported in recent studies. The imaging investigations used for assessment include screening modalities such as mammography and ultrasonography, as well as more complex investigations like MRI, which offers high sensitivity for loco-regional evaluation, and PET, which determines tumor metabolic activity using radioactive tracers. The purpose of this review is to provide a better understanding as well as a revision of the imaging differences exhibited by the molecular subtypes and histopathological types of breast cancer.

Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists

PURPOSE

To conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy of this fusion model against the assessments made by experienced radiologists.

MATERIALS AND METHODS

This multi-center study conducted a retrospective analysis of DCE-MRI images from 330 women diagnosed with breast cancer, with 138 cases categorized as benign and 192 as malignant. The training and internal testing sets comprised 270 patients from center 1, while the external testing cohort consisted of 60 patients from center 2. A fusion feature set consisting of radiomics features and deep transfer learning features was constructed from both intratumoral (ITR) and peritumoral (PTR) areas. The Least absolute shrinkage and selection operator (LASSO) based support vector machine was chosen as the classifier by comparing its performance with five other machine learning models. The diagnostic performance and clinical usefulness of fusion model were verified and assessed through the area under the receiver operating characteristics (ROC) and decision curve analysis. Additionally, the performance of the fusion model was compared with the diagnostic assessments of two experienced radiologists to evaluate its relative accuracy. The study strictly adhered to CLEAR and METRICS guidelines for standardization to ensure rigorous and reproducible methods.

RESULTS

The findings show that the fusion model, utilizing radiomics and deep transfer learning features from the ITR and PTR, exhibited exceptional performance in classifying breast tumors, achieving AUCs of 0.950 in the internal testing set and 0.921 in the external testing set. This performance significantly surpasses that of models relying on singular regional radiomics or deep transfer learning features alone. Moreover, the fusion model demonstrated superior diagnostic accuracy compared to the evaluations conducted by two experienced radiologists, thereby highlighting its potential to support and enhance clinical decision-making in the differentiation of benign and malignant breast tumors.

CONCLUSION

The fusion model, combining multi-regional radiomics with deep transfer learning features, not only accurately differentiates between benign and malignant breast tumors but also outperforms the diagnostic assessments made by experienced radiologists. This underscores the model's potential as a valuable tool for improving the accuracy and reliability of breast tumor diagnosis.

Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms

Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.

Lost in Translation: Multilingual Analysis of Online Breast Cancer Information

INTRODUCTION

Patients use the internet to learn more about health conditions. Non-English-speaking patients may face additional challenges. The quality of online breast cancer information, the most common cancer in women, is uncertain. This study aims to examine the quality of online breast cancer information for English and non-English-speaking patients.

METHODS

Three search engines were queried using the terms: "how to do a breast examination," "when do I need a mammogram," and "what are the treatment options for breast cancer" in English, Spanish, and Chinese. For each language, 60 unique websites were included and classified by type and information source. Two language-fluent reviewers evaluated website quality using the Journal of American Medical Association benchmark criteria (0-4) and the DISCERN tool (1-5), with higher scores representing higher quality. Scores were averaged for each language. Health On the Net code presence was noted. Inter-rater reliability between reviewers was assessed.

RESULTS

English and Spanish websites most commonly originated from US sources (92% and 80%, respectively) compared to Chinese websites (33%, P < 0.001). The most common website type was hospital-affiliated for English (43%) and foundation/advocacy for Spanish and Chinese (43% and 45%, respectively). English websites had the highest and Chinese websites the lowest mean the Journal of American Medical Association (2.2 ± 1.4 versus 1.0 ± 0.8, P = 0.002) and DISCERN scores (3.5 ± 0.9 versus 2.3 ± 0.6, P < 0.001). Health On the Net code was present on 16 (8.9%) websites. Inter-rater reliability ranged from moderate to substantial agreement.

CONCLUSIONS

The quality of online information on breast cancer across all three languages is poor. Information quality was poorest for Chinese websites. Improvements to enhance the reliability of breast cancer information across languages are needed.

An Innovative Thermal Imaging Prototype for Precise Breast Cancer Detection: Integrating Compression Techniques and Classification Methods

Breast cancer detection at an early stage is crucial for improving patient survival rates. This work introduces an innovative thermal imaging prototype that incorporates compression techniques inspired by mammography equipment. The prototype offers a radiation-free and precise cancer diagnosis. By integrating compression and illumination methods, thermal picture quality has increased, and the accuracy of classification has improved. Essential components of the suggested thermography device include an equipment body, plates, motors, pressure sensors, light sources, and a thermal camera. We created a 3D model of the gadget using the SolidWorks software 2020 package. Furthermore, the classification research employed both cancer and normal images from the experimental results to validate the efficacy of the suggested system. We employed preprocessing and segmentation methods on the obtained dataset. We successfully categorized the thermal pictures using various classifiers and examined their performance. The logistic regression model showed excellent performance, achieving an accuracy of 0.976, F1 score of 0.977, precision of 1.000, and recall of 0.995. This indicates a high level of accuracy in correctly classifying thermal abnormalities associated with breast cancer. The proposed prototype serves as a highly effective tool for conducting initial investigations into breast cancer detection, offering potential advancements in early-stage diagnosis, and improving patient survival rates.

Global burden and attributable risk factors of breast cancer in young women: historical trends from 1990 to 2019 and forecasts to 2030 by sociodemographic index regions and countries

Background

Breast cancer in young women (BCY) is much less common but has significant health sequelae and societal costs. We aimed to evaluate the global and regional burden of breast cancer in women aged 15-39 years from 1990 to 2019.

Methods

We collected detailed data on breast cancer from the Global Burden of Disease Study 2019 (GBD 2019) Data Resources. The age-standardised incidence rate (ASIR), age-standardised mortality rate (ASMR), age-standardised disability-adjusted life years rate (ASDR), and estimated annual percentage change (EAPC) were used to assess the disease burden of BCY. The Bayesian Age-Period-Cohort model was used to forecast disease burden from 2020 to 2030.

Results

From 1990 to 2019, significant increases in ASIR were found for BCY (EAPC = 0.59, 95% confidence interval (CI) = 0.5 to 0.68), whereas decreases in ASMR (EAPC = -0.41, 95% CI = -0.53 to -0.3) and ASDR (EAPC = -0.35, 95% CI = -0.46 to -0.24). Across countries with varying sociodemographic indexes (SDI), all regions showed an upward trend in BCY morbidity, except for countries with a high SDI. While mortality and DALYs rates have decreased in countries with high, high-middle, and middle SDI, they have increased in countries with low-middle and low SDI. Countries with lower SDIs are projected to bear the greatest burden of BCY over the next decade, including both low and low-middle categories. Alcohol use was the main risk factor attributed to BCY deaths in most countries, while exposure to second hand smoke was the predominant risk factor for BCY deaths in middle and low-middle SDI countries.

Conclusions

The burden of breast cancer in young women is on the rise worldwide, and there are significant regional differences. Countries with a low-middle or low SDI face even more challenges, as they experienced a more significant and increasing BCY burden than countries with higher SDIs.

Public neoantigens in breast cancer immunotherapy (Review)

Among women globally, breast cancer is the most prevalent cancer and the leading cause of cancer‑related death. Interestingly, though genetic mutations contribute to the disease, <15% of women diagnosed with breast cancer have a family history of the disease, suggesting a prevalence of sporadic genetic mutations in breast cancer development. In the rapidly rising field of cancer genomics, neoantigen‑based immunotherapy has come to the fore. The investigation of novel proteins arising from unique somatic mutations or neoantigens have opened a new pathway for both individualized and public cancer treatments. Because they are shared among individuals with similar genetic changes, public neoantigens provide an opportunity for 'off‑the‑shelf' anticancer therapies, potentially extending the benefits to a wider patient group. The present review aimed to highlight the role of shared or public neoantigens as therapeutic targets for patients with breast cancer, emphasizing common hotspot mutations of certain genes identified in breast cancer. The clinical utilization of public neoantigen‑based therapies for breast cancer treatment were also discussed.

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.