Background and Objectives: The objective of this review is to evaluate the current evidence regarding hormone treatments for both premenopausal and postmenopausal women with early-stage hormone receptor (HR) positive breast cancer. Materials and Methods: An in-depth exploration of the existing literature was conducted, with landmark clinical trials such as TEXT, SOFT, ATLAS, and aTTom serving as primary references. Results: Through an extensive review of the literature, our findings indicate that for premenopausal women with HR-positive, HER2-negative BC with a low risk of recurrence, standard 5-year monotherapy with tamoxifen represents the optimal therapeutic management, given its favorable clinical outcomes and lower associated toxicity. In contrast, for premenopausal women with an intermediate to high risk of recurrence with the same tumor characteristics, the most effective approach stated in the literature is a combination of ovarian suppression therapy (chemical/surgical) and an aromatase inhibitor/selective estrogen receptor modulator (tamoxifen), with a possible extension of the standard therapeutic period. In postmenopausal patients with HR-positive, HER2-negative breast cancer with a low recurrence risk, the first line of treatment is usually a standard 5-year period of treatment with aromatase inhibitors (AIs)(letrozole, anastrozole, or exemestane). On the other hand, in postmenopausal women with an intermediate to high risk, combination therapy might be needed, as well as an extension of the standard therapeutic time. Conclusions: Treatment consensus depends on pre- vs. postmenopausal status and recurrence risk.

Background/Objectives: Chronic inflammation plays a critical role in pelvic pain among endometriosis patients. This study examines the association between inflammatory markers—specifically the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR)—and pelvic pain in endometriosis. Methods: We conducted a retrospective analysis of endometriosis patients, assessing NLR and PLR levels in those with and without pelvic pain. Diagnostic utility was evaluated using ROC curves, and logistic regression determined associations between these markers, pain presence, and endometriosis severity. Results: Patients with pelvic pain had significantly higher median levels of both NLR and PLR (p < 0.05). NLR demonstrated moderate diagnostic accuracy with an AUC of 0.63, sensitivity of 59%, and specificity of 71% at a cut-off of 1.85. PLR, with a cut-off of 139.77, showed an AUC of 0.60, with a specificity of 82% and sensitivity of 40%, indicating better utility for excluding pain. Logistic regression analysis revealed that NLR > 1.85 was significantly associated with pelvic pain (OR = 3.06, 95% CI: 1.45–6.49, p = 0.003), as was PLR > 139.77 (OR = 2.84, 95% CI: 1.18–6.82, p = 0.02). Advanced rASRM stages (III and IV) also correlated with elevated NLR and PLR values. Conclusions: Elevated NLR and PLR are associated with pelvic pain and advanced stages of endometriosis, suggesting these ratios are potential markers for assessing inflammation and disease severity. Further studies should explore combining NLR and PLR with other biomarkers to improve diagnostic accuracy in endometriosis.

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.

Prostate cancer is the second most common cancer in men and a leading cause of death worldwide. Early detection is vital, as it often presents with vague symptoms such as nocturia and poor urinary stream. Diagnostic tools like PSA tests, ultrasound, PET-CT, and mpMRI are essential for prostate cancer management. The PI-RADS system helps assess malignancy risk based on imaging. While mpMRI, which includes T1, T2, DWI, and dynamic contrast-enhanced imaging (DCE), is the standard, bpMRI offers a contrast-free alternative using only T2 and DWI. This reduces costs, acquisition time, and the risk of contrast-related side effects but has limitations in detecting higher-risk PI-RADS 3 and 4 lesions. This study compared bpMRI’s diagnostic accuracy to mpMRI, focusing on prostate volume and PI-RADS scoring. Both methods showed strong inter-rater agreement for prostate volume (ICC 0.9963), confirming bpMRI’s reliability in this aspect. However, mpMRI detected more complex conditions, such as periprostatic fat infiltration and iliac lymphadenopathy, which bpMRI missed. While bpMRI offers advantages like reduced cost and no contrast use, it is less effective for higher-risk lesions, making mpMRI more comprehensive.

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.

Background: Breast cancer remains the most prevalent malignancy in women worldwide, characterized by remarkable genetic, molecular, and clinical heterogeneity. Traditional preclinical models have significantly advanced our understanding of tumor biology, yet consistently fall short in recapitulating the complexity of the human tumor microenvironment (TME), immune, and metastatic behavior. In recent years, breast cancer-on-a-chip (BCOC) have emerged as powerful microengineered systems that integrate patient-derived cells, stromal and immune components, and physiological stimuli such as perfusion, hypoxia, and acidic milieu within controlled three-dimensional microenvironments. Aim: To comprehensively review the BCOC development and application, encompassing fabrication materials, biological modeling of key subtypes (DCIS, luminal A, triple-negative), dynamic tumor–stroma–immune crosstalk, and organotropic metastasis to bone, liver, brain, lungs, and lymph nodes. Methods: We selected papers from academic trusted databases (PubMed, Web of Science, Google Scholar) by using Breast Cancer, Microfluidic System, and Breast Cancer on a Chip as the main search terms. Results: We critically discuss and highlight how microfluidic systems replicate essential features of disease progression—such as epithelial-to-mesenchymal transition, vascular invasion, immune evasion, and therapy resistance—with unprecedented physiological relevance. Special attention has been paid to the integration of liquid biopsy technologies within microfluidic platforms for non-invasive, real-time analysis of circulating tumor cells, cell-free nucleic acids, and exosomes. Conclusions: In light of regulatory momentum toward reducing animal use in drug development, BCOC platforms stand at the forefront of a new era in precision oncology. By bridging biological fidelity with engineering innovation, these systems hold immense potential to transform cancer research, therapy screening, and personalized medicine.