Background/Objectives: Cytokines play a fundamental role in the tumor microenvironment, influencing breast cancer progression, metastasis, and therapeutic resistance. The objective of this systematic review and meta-analysis was to evaluate the prognostic impact and therapeutic relevance of key cytokines in breast cancer, based on human studies published between 2015 and 2025. Methods: We systematically searched PubMed, Web of Science, and Scopus for eligible studies reporting on cytokine expression and clinical outcomes in breast cancer. Inclusion criteria were based on the PRISMA framework, focusing on human cohorts and excluding in vitro or animal models. Data were extracted on cytokine types, measurement methods, patient population, and outcomes. Meta-analyses were performed using random-effects models for cytokines with sufficient data, notably IL-6 and TNF-α. Results: Twenty-three studies were included. Elevated IL-6 was consistently associated with poor overall survival (pooled HR = 2.25, 95% CI 1.83–2.76), while high TNF-α levels showed a trend toward worse outcomes but without statistical significance. IL-1β, IL-8, and IL-10 were also linked to increased metastasis and reduced response to therapy. Immunosuppressive cytokines such as IL-10 and TGF-β facilitated tumor immune evasion, while IL-17 promoted inflammation and angiogenesis. Cytokines such as IL-12 and IFN-γ were associated with improved immune responses and a favorable prognosis. Conclusions: Cytokines are central mediators of breast cancer progression and immune regulation. Elevated levels of pro-inflammatory and immunosuppressive cytokines correlate with poor outcomes and may serve as prognostic biomarkers and therapeutic targets. Their integration into personalized treatment strategies holds significant clinical potential but requires further prospective validation and biomarker standardization.

Background and Objectives: Breast cancer is a leading global health challenge, where early detection is essential for improving survival outcomes. Two-dimensional (2D) mammography is the established standard for breast cancer screening; however, its diagnostic accuracy is limited by factors such as breast density and inter-reader variability. Recent advances in artificial intelligence (AI) have shown promise in enhancing radiological interpretation. This study aimed to assess the utility of AI in improving lesion detection and classification in 2D mammography. Materials and Methods: A retrospective analysis was performed on a dataset of 578 mammographic images obtained from a single radiology center. The dataset consisted of 36% pathologic and 64% normal cases, and was partitioned into training (403 images), validation (87 images), and test (88 images) sets. Image preprocessing involved grayscale conversion, contrast-limited adaptive histogram equalization (CLAHE), noise reduction, and sharpening. A convolutional neural network (CNN) model was developed using transfer learning with ResNet50. Model performance was evaluated using sensitivity, specificity, accuracy, and area under the receiver operating characteristic (AUC-ROC) curve. Results: The AI model achieved an overall classification accuracy of 88.5% and an AUC-ROC of 0.93, demonstrating strong discriminative capability between normal and pathologic cases. Notably, the model exhibited a high specificity of 92.7%, contributing to a reduction in false positives and improved screening efficiency. Conclusions: AI-assisted 2D mammography holds potential to enhance breast cancer detection by improving lesion classification and reducing false-positive findings. Although the model achieved high specificity, further optimization is required to minimize false negatives. Future efforts should aim to improve model sensitivity, incorporate multimodal imaging techniques, and validate results across larger, multicenter prospective cohorts to ensure effective integration into clinical radiology workflows.

Background/Objectives: Breast cancer remains a leading cause of cancer-related morbidity worldwide, and refining prognostic tools is essential for individualized patient management. Recent evidence suggests that the systemic immune–inflammation index (SII), derived from routine blood tests, may offer valuable prognostic insights. This study aimed to evaluate whether SII can reliably predict clinical outcomes for patients undergoing curative resection. Methods: We retrospectively analyzed patients with histologically confirmed breast cancer who underwent surgical intervention at a single tertiary center. Preoperative complete blood counts were used to calculate SII. Using receiver operating characteristic (ROC) curve analysis, we identified an optimal SII cutoff. Using statistical tests, including t-tests and ANOVA, we examined differences in clinicopathological factors between low- and high-SII groups. Using univariate and multivariate analyses, we explored associations between SII and variables such as tumor stage and hormone receptor status. Results: Patients with elevated SII levels showed significant associations with more advanced tumor stage and systemic inflammatory profiles. The identified SII cutoff separated patients into distinct risk groups, and high SII values correlated with poorer prognostic features. Multivariate models indicated that SII provided additional predictive value beyond standard markers. Conclusions: Our findings suggest that SII may provide prognostic insights into breast cancer, particularly in stratifying patients based on inflammatory profiles. However, the current study does not support the use of SII as a clinical tool for tailoring treatment strategies. Further preclinical and randomized controlled trials are required to determine its predictive utility and to assess its potential integration into personalized management approaches.

Background and Objectives: Breast cancer remains a significant global health burden, demanding continuous innovation in diagnostic and prognostic tools. This meta-analysis and systematic review aims to synthesize evidence from 2015 to 2025 regarding the diagnostic utility of artificial intelligence (AI) in mammography and the prognostic value of the Systemic Immune-Inflammation Index (SII) in breast cancer patients. Materials and Methods: A systematic literature search was conducted in PubMed, Google Scholar, EMBASE, Web of Science, and Scopus. Studies evaluating AI performance in mammographic breast cancer detection and those assessing the prognostic significance of SII (based on routine hematologic parameters) were included. The risk of bias was assessed using QUADAS-2 and the Newcastle–Ottawa Scale. Meta-analyses were conducted using bivariate and random-effects models, with subgroup analyses by clinical and methodological variables. Results: Twelve studies were included, five assessing AI and seven assessing SII. AI demonstrated high diagnostic accuracy, frequently matching or surpassing that of human radiologists, with AUCs of up to 0.93 and notable reductions in radiologist reading times (17–91%). Particularly in dense breast tissue, AI improved detection rates and workflow efficiency. SII was significantly associated with poorer outcomes, including reduced overall survival (HR ~1.97) and disease-free survival (HR ~2.07). However, variability in optimal cut-off values for SII limits its immediate clinical standardization. Conclusions: AI enhances diagnostic precision and operational efficiency in mammographic screening, while SII offers a cost-effective prognostic biomarker for systemic inflammation in breast cancer. Their integration holds promise for more personalized care. Nevertheless, challenges persist regarding prospective validation, standardization, and equitable access, which must be addressed through future translational research.

Background and Objectives: The accuracy of breast cancer diagnosis depends on the concordance between imaging features and pathological findings. While BI-RADS (Breast Imaging Reporting and Data System) provides standardized risk stratification, its correlation with histologic grade and immunohistochemical markers remains underexplored. This study assessed the diagnostic performance of BI-RADS 3, 4, and 5 classifications and their association with tumor grade and markers such as ER, PR, HER2, and Ki-67. Materials and Methods: In this prospective study, 67 women aged 33–82 years (mean 56.4) underwent both mammography and ultrasound. All lesions were biopsied using ultrasound-guided 14G core needles. Imaging characteristics (e.g., margins, echogenicity, calcifications), histopathological subtype, and immunohistochemical data were collected. Statistical methods included logistic regression, Chi-square tests, and Spearman’s correlation to assess associations between BI-RADS, histology, and immunohistochemical markers. Results: BI-RADS 5 lesions showed a 91% malignancy rate. Evaluated features included spiculated margins, pleomorphic microcalcifications, and hypoechoic masses with posterior shadowing, and were correlated with histological and immunohistochemical results. Invasive tumors typically appeared as irregular, hypoechoic masses with posterior shadowing, while mucinous carcinomas mimicked benign features. Higher BI-RADS scores correlated significantly with increased Ki-67 index (ρ = 0.76, p < 0.001). Logistic regression yielded an AUC of 0.877, with 93.8% sensitivity and 80.0% specificity. Conclusions: BI-RADS scoring effectively predicts malignancy and correlates with tumor proliferative markers. Integrating imaging, histopathology, and molecular profiling enhances diagnostic precision and supports risk-adapted clinical management in breast oncology.

Chronic heart failure (CHF) carries high morbidity and mortality. Circulating biomarkers of myocardial stretch, injury, and remodelling aids diagnosis and prognosis, but utility varies, especially in HFpEF, where natriuretic peptide (NP) values may be lower or normal in obesity. We systematically searched PubMed, Scopus, and Web of Science (2010–2025) for primary adult chronic-HF studies evaluating blood-based biomarkers: NPs, high-sensitivity troponins (hs-cTn), galectin-3, soluble ST2 (sST2), and microRNAs. Secondary sources (reviews/meta-analyses/guidelines) informed context only. Acute-HF studies were not pooled with chronic-HF analyses. Where appropriate, log hazard ratios were meta-analysed with random effects models. Twenty-nine studies met criteria. NT-proBNP remained the diagnostic/prognostic reference; across five prognostic cohorts, the pooled HR was 1.68 (95% CI 1.54–1.82; I² ≈ 55%). hs-cTn consistently improved risk stratification. Galectin-3 and sST2 were associated with adverse outcomes but typically provided modest incremental value beyond NPs/hs-cTn; galectin-3 is influenced by renal function, and sST2 is commonly interpreted around ~28–35 ng/mL. MicroRNAs (e.g., miR-21, miR-210-3p, miR-22-3p) showed promising yet heterogeneous signals across platforms and preanalytical workflows; therefore, findings were synthesised narratively without pooling. NT-proBNP and hs-cTn form the evidence-based backbone for biomarker-guided assessment in chronic HF. Galectin-3 and sST2 add adjunct prognostic information, while microRNAs remain investigational, pending standardised methods and external validation. Overall, evidence supports a multimarker, phenotype-tailored approach, with core NPs + hs-cTn and selective adjunct use of sST2/galectin-3 in context (HFrEF vs. HFpEF, obesity, renal function) to refine risk stratification and guide clinical decision-making.