Search Results (1,264)

Background/Objectives: The detection of ovarian cancer remains challenging due to the lack of reliable serum biomarkers that reflect malignant transformation rather than mere tumor presence. We developed a novel biotest using an immortalized human fallopian tube epithelial cell line (TY), which exhibits anchorage-independent growth (AIG) in response to cancer-associated serum factors. Methods: Sera from ovarian and breast cancer patients, non-cancer controls, and ID8 ovarian cancer-bearing mice were tested for AIG-promoting activity in TY cells. Results: TY cells (passage 96) effectively distinguished cancer sera from controls (68.50 ± 2.12 vs. 17.50 ± 3.54 colonies, p < 0.01) and correlated with serum CA125 levels (r = 0.73, p = 0.03) in ovarian cancer patients. Receiver operating characteristic (ROC) analysis showed high diagnostic accuracy (AUC = 0.85, cutoff: 23.75 colonies). The AIG-promoting activity was mediated by HGF/c-MET and IGF/IGF-1R signaling, as inhibition of these pathways reduced phosphorylation and AIG. In an ID8 mouse ovarian cancer model, TY-AIG colonies strongly correlated with tumor burden (r = 0.95, p < 0.01). Conclusions: Our findings demonstrate that the TY cell-based AIG assay is a sensitive and specific biotest for detecting ovarian cancer and potentially other malignancies, leveraging the fundamental hallmark of malignant transformation. Full article

Female cancers such as breast and gynaecological cancers contribute to a significant global health burden and are a leading cause of fatality among women. With current treatment options often limited by resistance to cytotoxic drugs, side effects and lack of specificity to the cancer, there is a pressing need for alternative treatments. Recent research has highlighted the promising role of non-coding RNAs (ncRNA) in regulating these issues and providing more targeted approaches to suppressing key cancer pathways. This review explores the involvement of the various types of non-coding RNAs in regulating key oncogenic pathways, namely, the MAPK, PI3K/Akt/mTOR, Wnt/β-catenin and p53 pathways, in a range of female cancers such as breast, cervical, ovarian and endometrial cancers. Evidence from a multitude of studies suggests that non-coding RNAs function as double-edged swords, serving as both oncogenes and tumour suppressors, depending on their expression and cellular interactions. By mapping and investigating these regulatory interactions, this review demonstrates the complexity and dual functionality of ncRNAs in cancer. Understanding these complex mechanisms is essential for the development of new and effective ncRNA-based diagnostic methods and targeted therapies in female cancer treatment. Full article

Background: Breast cancer is the most prevalent malignancy among women globally. In Romania, it is the most frequent form of cancer affecting women, with approximately 12,000 new cases diagnosed annually, and the second most common cause of cancer-related mortality, second only to lung cancer. Methods: This study looked at 79 breast cancer patients from Oltenia, concentrating on epidemiology, histology, diagnostic features, and treatments. Patients were chosen based on inclusion criteria such as histopathologically verified diagnosis, availability of clinical and treatment data, and follow-up information. The analyzed biological material consisted of tissue samples taken from the breast parenchyma and axillary lymph nodes. Even though not the primary subject of this paper, all patients underwent immunohistochemical (IHC) evaluation both preoperatively and postoperatively. Results: We found invasive ductal carcinoma to be the predominant type, while ductal carcinoma in situ (DCIS) and mixed types were rare. We performed cross-tabulations of metastasis versus nodal status and age versus therapy type; none reached significance (all p > 0.05), suggesting observed differences were likely due to chance. A chi-square test comparing surgical interventions (breast-conserving vs. mastectomy) in patients who did or did not receive chemotherapy showed, χ² = 3.17, p = 0.367, indicating that chemotherapy did not significantly influence surgical choice. Importantly, adjuvant chemotherapy and radiotherapy were used at similar rates across age groups, whereas neoadjuvant hormonal (endocrine) therapy was more common in older patients (but without statistical significance). Conclusions: Finally, we discussed the consequences of individualized care and early detection. Romania’s shockingly low screening rate, which contributes to delayed diagnosis, emphasizes the importance of improved population medical examination and tailored treatment options. Also, the country has one of the lowest rates of mammography uptake in Europe and no systematic population screening program. Full article

Breast cancer is the leading cause of cancer-related mortality among women world-wide, and early screening is critical for improving patient survival. Medical imaging plays a central role in breast cancer screening, diagnosis, and treatment monitoring. However, conventional imaging modalities—including mammography, ultrasound, and magnetic resonance imaging—face limitations such as low diagnostic specificity, relatively slow imaging speed, ionizing radiation exposure, and dependence on exogenous contrast agents. Photoacoustic imaging (PAI), a novel hybrid imaging technique that combines optical contrast with ultrasonic spatial resolution, has shown great promise in addressing these challenges. By revealing anatomical, functional, and molecular features of the breast tumor microenvironment, PAI offers high spatial resolution, rapid imaging, and minimal operator dependence. This review outlines the fundamental principles of PAI and systematically examines recent advances in its application to breast cancer screening, diagnosis, and therapeutic evaluation. Furthermore, we discuss the translational potential of PAI as an emerging breast imaging modality, complementing existing clinical techniques. Full article

Background: Breast cancer remains a major public health challenge in Latin America, where access to personalized risk assessment tools is still limited. This study aimed to evaluate the implementation of a polygenic risk score (PRS)-based stratification model combined with remote genomic counseling in Colombian women with sporadic breast cancer and healthy women. Methods: In 2023, an embedded mixed-methods observational study was conducted in Medellín involving 1997 women aged 40–75 years who underwent clinical PRS testing. The intervention integrated PRS-based risk categorization with individualized risk factor assessment and lifestyle recommendations delivered through a remote counseling platform. Results: PRS analysis classified 9.7% of women as high risk and 46% as low risk. Healthier lifestyle patterns were significantly associated with lower PRS categories (p = 0.034). Physical activity showed a protective effect (OR = 0.60, 95% CI: 0.5–0.8), while prior smoking, elevated BMI, and sedentary behavior were associated with higher risk. The counseling model achieved high delivery (93%) and satisfaction (85%) rates. Qualitative insights revealed improved understanding of genomic risk and greater engagement in preventive behaviors. Only one new case of breast cancer was detected among intermediate-risk participants, with a diagnostic lead time of 12 months. Conclusions: These findings support the feasibility, acceptability, and potential impact of integrating PRS and genomic counseling in cancer prevention strategies in middle-income settings. Full article

Introduction: Breast density is a well-recognized factor in breast cancer risk assessment, with higher density linked to increased malignancy risk and reduced sensitivity of conventional mammography. Background parenchymal enhancement (BPE), observed in contrast-enhanced imaging, reflects physiological contrast uptake in non-pathologic breast tissue. While extensively characterized in breast MRI, the role of BPE in contrast-enhanced mammography (CEM) remains uncertain due to inconsistent findings regarding its correlation with breast density and cancer risk. Unlike breast density—standardized through the ACR BI-RADS lexicon—BPE lacks a uniform classification system in CEM, leading to variability in clinical interpretation and research outcomes. To address this gap, we introduce the BPE-CEM Standard Scale (BCSS), a structured four-tiered classification system specifically tailored to the two-dimensional characteristics of CEM, aiming to improve consistency and diagnostic alignment in BPE evaluation. Materials and Methods: In this retrospective single-center study, 213 patients who underwent mammography (MG), ultrasound (US), and contrast-enhanced mammography (CEM) between May 2022 and June 2023 at the “A. Perrino” Hospital in Brindisi were included. Breast density was classified according to ACR BI-RADS (categories A–D). BPE was categorized into four levels: Minimal (< 10% enhancement), Light (10–25%), Moderate (25–50%), and Marked (> 50%). Three radiologists independently assessed BPE in a subset of 50 randomly selected cases to evaluate inter-observer agreement using Cohen’s kappa. Correlations between BPE, breast density, and age were examined through regression analysis. Results: BPE was Minimal in 57% of patients, Light in 31%, Moderate in 10%, and Marked in 2%. A significant positive association was found between higher breast density (BI-RADS C–D) and increased BPE (p < 0.05), whereas lower-density breasts (A–B) were predominantly associated with minimal or light BPE. Regression analysis confirmed a modest but statistically significant association between breast density and BPE (R² = 0.144), while age showed no significant effect. Inter-observer agreement for BPE categorization using the BCSS was excellent (κ = 0.85; 95% CI: 0.78–0.92), supporting its reproducibility. Conclusions: Our findings indicate that breast density is a key determinant of BPE in CEM. The proposed BCSS offers a reproducible, four-level framework for standardized BPE assessment tailored to the imaging characteristics of CEM. By reducing variability in interpretation, the BCSS has the potential to improve diagnostic consistency and facilitate integration of BPE into personalized breast cancer risk models. Further prospective multicenter studies are needed to validate this classification and assess its clinical impact. Full article

Breast cancer continues to be one of the leading causes of women’s deaths around the world, and this has emphasized the necessity to have novel and interpretable diagnostic models. This work offers a clear learning deep learning model that integrates the mobility of MobileNet and two bio-driven optimization operators, the Firefly Algorithm (FLA) and Dingo Optimization Algorithm (DOA), in an effort to boost classification appreciation and the convergence of the model. The suggested model demonstrated excellent findings as the DOA-optimized MobileNet acquired the highest performance of 98.96 percent accuracy on the fusion test, and the FLA-optimized MobileNet scaled up to 98.06 percent and 95.44 percent accuracies on mammographic and ultrasound tests, respectively. Further to good quantitative results, Grad-CAM visualizations indeed showed clinically consistent localization of the lesions, which strengthened the interpretability and model diagnostic reliability of Grad-CAM. These results show that lightweight, compact CNNs can be used to do high-performance, multimodal breast cancer diagnosis. Full article

Background/Objectives: Magnetic iron oxide nanoparticles (IONPs), human serum albumin (HSA) and folic acid (FA) are prospective components for hybrid nanosystems for various biomedical applications. The magnetic nanosystems FA-HSA@IONPs (FAMs) containing IONPs, HSA, and FA residue are engineered in the study. Methods: Composition, stability and integrity of the coating, and peroxidase-like activity of FAMs are characterized using UV/Vis spectrophotometry (colorimetric test using o-phenylenediamine (OPD), Bradford protein assay, etc.), spectrofluorimetry, dynamic light scattering (DLS) and electron magnetic resonance (EMR). The selectivity of the FAMs accumulation in cancer cells is analyzed using flow cytometry and confocal laser scanning microscopy. Results: FAMs (d_N~55 nm by DLS) as a drug delivery platform have been administered to cancer cells (human breast adenocarcinoma MCF-7 and MDA-MB-231 cell lines) in vitro. Methylene blue, as a model photosensitizer, has been non-covalently bound to FAMs. An increase in photoinduced cytotoxicity has been found upon excitation of the photosensitizer bound to the coating of FAMs compared to the single photosensitizer at equivalent concentrations. The suitability of the nanosystems for photodynamic therapy has been confirmed. Conclusions: FAMs are able to effectively enter cells with increased folate receptor expression and thus allow antitumor photosensitizers to be delivered to cells without any loss of their in vitro photodynamic efficiency. Therapeutic and diagnostic applications of FAMs in oncology are discussed. Full article

Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. This study introduces a deep learning-based framework that leverages a microwave radar system equipped with an arc-shaped array of six antennas to estimate key tumor parameters, including position, size, and depth. This research begins with the evolutionary design of an ultra-wideband octagram ring patch antenna optimized for enhanced tumor detection sensitivity in directional near-field coupling scenarios. The antenna is fabricated and experimentally evaluated, with its performance validated through S-parameter measurements, far-field radiation characterization, and efficiency analysis to ensure effective signal propagation and interaction with breast tissue. Specific Absorption Rate (SAR) distributions within breast tissues are comprehensively assessed, and power adjustment strategies are implemented to comply with electromagnetic exposure safety limits. The dataset for the deep learning model comprises simulated self and mutual S-parameters capturing tumor-induced variations over a broad frequency spectrum. A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. A multi-branch neural network processes these features to achieve precise tumor localization and size estimation. Compared to conventional attention mechanisms, the proposed ABFS architecture demonstrates superior prediction accuracy and interpretability. The proposed approach achieves high estimation accuracy and computational efficiency in simulation studies, underscoring the promise of integrating deep learning with conformal microwave imaging for safe, effective, and non-invasive breast cancer detection. Full article

Precision oncology, also known as personalized oncology or precision medicine, is the tailoring of cancer treatment to individual patients based on the specific genetic, molecular, and other unique characteristics of their tumor. The goal of precision oncology is to optimize the effectiveness of cancer treatment while minimizing toxicities and improving patient outcomes. Precision oncology recognizes that cancer is a highly heterogeneous disease and that each patient’s tumor has a distinct genetic diversity. Precision medicine individualizes therapy by using information from a patient’s tumor in the context of clinical history to determine optimal therapeutic approaches and increasing numbers of drugs target specific tumor alterations. Several targeted therapies with approved companion diagnostics are commercially available, the haves of precision oncology, where predictive biomarkers guide clinical decision-making and improve outcomes. However, many therapies still lack clear biomarkers, the have nots, posing a challenge to fully realizing the promise of precision oncology. Herein, we describe the current state of the art for breast cancer precision oncology and highlight the therapeutic agents that require a more robust biomarker. Full article

The concept of “more is better” has long dominated cancer treatment, emphasizing aggressive therapies despite their toxicity. However, the rise of personalized medicine has fostered treatment de-escalation strategies aimed at minimizing toxicity, improving quality of life, and reducing costs. This position paper highlights key applications of de-escalation in medical oncology, with a primary focus on breast cancer and notable examples in colorectal, head and neck, ovarian, lung, and prostate cancers. Various approaches, including dose reduction, treatment duration shortening, and regimen optimization, have demonstrated efficacy without compromising clinical outcomes. Advances in molecular diagnostics, such as Oncotype Dx in breast cancer and circulating tumor DNA (ctDNA) analysis in colorectal cancer, have facilitated patient selection for de-escalation. While these strategies present promising results, challenges remain, particularly in balancing treatment intensity with oncologic control. The review underscores the need for further prospective trials to refine de-escalation approaches and ensure their safe integration into standard oncologic care. Full article

Background/Objectives: The implementation of polygenic scores (PGSs) and multifactorial risk assessments (MFRAs) has the potential to enhance breast cancer risk stratification, particularly in carriers of moderate-penetrance pathogenic variants (PVs), whose risk profiles often remain unclear if testing is limited to monogenic risk factors. Methods: To enhance breast cancer risk stratification, we included the BCAC313 polygenic score, together with MFRA, for carriers of moderate-penetrance pathogenic variants (PVs) during routine diagnostics and assessed its effect on the classification of patients’ risk categories in a real-world cohort at our center in its first year of implementation. Seventeen carriers with PVs in moderate-risk breast cancer genes were included in this study. Thirteen of them qualified for analysis for a full MFRA, including PGS, according to ancestry estimation and clinical criteria. The MFRA was performed using the CanRisk tool, which incorporates clinical, lifestyle, familial, and genetic data, including the BCAC313 score. Results: PGS z-scores were significantly higher in breast cancer patients compared to the unaffected control cohort (p = 0.016). The MFRA, including PGS, increased risk estimates for contralateral breast cancer in seven of eight patients with breast cancer and for primary breast cancer in three of five healthy carriers, compared to the risk conferred by the MFRA and moderate-penetrance pathogenic variant alone. Risk estimates varied widely, demonstrating the value of MFRA in personalized care. In five cases, one with a CHEK2-PV and four with an ATM-PV, the modified risk assessment contributed to the surgical decision for a prophylactic mastectomy. Conclusions: The MFRA, including PGS, provides the clinically meaningful refinement of breast cancer risk estimates in individuals with moderate-risk PVs. Personalized risk predictions can inform clinical management and support decision-making, which highlights the utility of this approach in clinical practice. Full article

Breast cancer (BRCA) remains a significant cause of mortality among women, particularly in developing and underdeveloped regions, where early detection is crucial for effective treatment. This research introduces an innovative hybrid model that combines Improved Grey Wolf Optimizer (IGWO) with the Seagull Optimization Algorithm (SOA), forming the IGWO–SOA technique to enhance BRCA detection accuracy. The hybrid model draws inspiration from the adaptive and strategic behaviors of seagulls, especially their ability to dynamically change attack angles in order to effectively tackle complex global optimization challenges. A deep neural network (DNN) is fine-tuned using this hybrid optimization method to address the challenges of hyperparameter selection and overfitting, which are common in DL approaches for BRCA classification. The proposed IGWO–SOA model demonstrates optimal performance in identifying key attributes that contribute to accurate cancer detection using the CBIS-DDSM dataset. Its effectiveness is validated using performance metrics such as loss, F1-score, precision, accuracy, and recall. Notably, the model achieved an impressive accuracy of 99.4%, outperforming existing methods in the domain. By optimizing both the learning parameters and model structure, this research establishes an advanced deep learning framework built upon the IGWO–SOA approach, presenting a robust and reliable method for early BRCA detection with significant potential to improve diagnostic precision. Full article

Liquid biopsy (LB) has gained attention as a valuable approach for cancer diagnostics, providing a minimally invasive option compared to conventional tissue biopsies and helping to overcome issues related to patient discomfort and procedural invasiveness. Recent advances in biosensor technologies, particularly photonic sensors, have improved the accuracy, speed, and real-time capabilities for detecting circulating biomarkers in biological fluids. Incorporating these tools into clinical practice facilitates more informed therapeutic choices and contributes to tailoring treatments to individual patient profiles. This review highlights the clinical potential of LB, examines technological limitations, and outlines future research directions. Departing from traditional biosensor focused reviews, it adopts a reverse-mapping approach grounded in clinically relevant tumor biomarkers. Specifically, biomarkers associated with prevalent cancers, such as breast, prostate, and lung cancers, serve as the starting point for identifying the most suitable photonic sensing platforms. The analysis underscores the need to align sensor design with the physicochemical properties of each biomarker and the operational requirements of the application. No photonic platform is universally optimal; rather, each exhibits specific strengths depending on performance metrics such as sensitivity, limit of detection, and easy system integration. Within this framework, the review provides a comprehensive assessment of emerging photonic biosensors and outlines key priorities to support their effective clinical translation in cancer diagnostics. Full article

Background: We investigated the role of a high-sensitive cardiac troponin T (hsTnT) increase below the upper limit of normal (ULN) in patients with breast cancer (BC). hsTnT assays accurately quantify very low plasma troponin concentrations and enable the early detection of cardiomyocyte injury before a drop in the left ventricular ejection fraction (LVEF). The increase in hsTnT below the ULN in response to chemotherapy has not previously been studied. Method: This was an open-label pilot study. Female patients with newly diagnosed BC scheduled to receive systemic cancer treatment were recruited. Blood sampling and echocardiography were performed at baseline, at 3 and 6 months of cancer treatment. hsTnT concentrations were measured using the Elecsys TnT hs assay (Roche Diagnostics). The limit of blank and 99th percentile cutoff values for the hsTnT assay were 3 and 14 ng/L. We calculated the rise in hsTnT (ΔhsTnT) by the difference (%) between its baseline level and during follow-up (FU) in each patient. Results: Among eligible subjects, we excluded 4 patients before the start of treatment and 17 patients during the follow-up with values for the hsTnT >14 ng/L. Finally, 60 women with a median age of 48.6 ± 1.3 years were included in the study. The median baseline hsTnT concentration was 5.5 ± 1.4 ng/L. During 6 months of cancer treatment, hsTnT increased in all patients by up to 10–305% from baseline, with an average of 94.2%. LV EF was normal at baseline and decreased significantly compared to the value before cancer treatment (61.9 ± 3.3% vs. 56.3 ± 7.0%; p < 0.045). We correlated the hsTnT rise with a drop in LV EF ≥ 10% from its baseline level. Logistic regression analysis showed that Δ hsTnT has a good predictive value for LV dysfunction, 0.78 (p = 0.05), 95% CI (0.67–0.90). The increase in hsTnT > 81% was determined as the optimal threshold value for detecting early biochemical cardiotoxicity. Conclusion: It was investigated that hsTnT rise within the cutoff < 14 ng/L can be used as a marker of early biochemical cardiotoxicity and is valuable for predicting LV drop in 6 months of FU. We conclude that BC patients with increased hsTnT plasma concentration > 81% from the baseline value should be considered as high-risk patients for cardiotoxicity and need more precise cardiac monitoring and early preventive medical intervention strategies. Full article