Eurasian Journal of Chemical, Medicinal and Petroleum Research

Abstract Introduction: Lumpectomy is a standard breast-conserving procedure, yet acute postoperative complications may delay recovery and adjuvant treatment. Beyond clinical risk factors, tumor biology may influence wound healing and inflammatory responses. This study aimed to evaluate the association between progesterone receptor expression and the incidence of acute postoperative complications following lumpectomy.

Material and methods: This descriptive cross-sectional study was conducted at Tabriz University of Medical Sciences in 2025 on 54 women undergoing lumpectomy, with sample size estimated using the Cochran formula and participants enrolled by convenience sampling. Progesterone receptor expression was assessed by immunohistochemistry on surgical specimens, and its association with acute postoperative complications was analyzed alongside demographic, clinical, pathological, and perioperative variables using appropriate statistical tests.

Results: Results summary: Among 54 patients undergoing lumpectomy, PR-negative status was associated with poorer early postoperative outcomes. Compared with PR-positive patients, PR-negative patients had higher postoperative VAS scores (4.06 ± 1.43 vs. 3.14 ± 1.21; p = 0.017), more acute complications (50.00% vs. 22.22%; p = 0.037), and more unplanned visits (33.33% vs. 11.11%; p = 0.048). PR negativity independently predicted complications after adjustment (OR = 3.84; p = 0.039).

Conclusion: PR-negative status was associated with a higher risk of acute postoperative complications after lumpectomy. Despite broadly comparable operative variables, PR-negative patients experienced greater postoperative morbidity and more unplanned visits, suggesting that receptor status may help identify patients requiring closer early postoperative monitoring.

Abstract Introduction: Breast cancer remains a major global health burden, and lymph node involvement is a key pathologic indicator of tumor spread, prognosis, and postoperative treatment planning in patients undergoing mastectomy. Accurate nodal assessment also improves disease staging and therapeutic decision-making. This study aims to evaluate the pathologic status of lymph node involvement in patients undergoing mastectomy.

Material and methods: This retrospective descriptive cross-sectional study was conducted at Shahid Madani Hospital in Tabriz on 250 patients selected by convenience sampling, with sample size estimated using Cochran’s formula. Data were extracted from archived medical and pathology records, and clinicopathologic variables, particularly lymph node status and related breast tumor characteristics, were systematically collected and analyzed to evaluate the pattern of nodal involvement in mastectomy patients.

Results: Among 250 mastectomy patients, invasive ductal carcinoma was the predominant subtype (79.2%), most tumors were grade II (57.2%), and mean tumor size was 3.41 ± 1.67 cm. Lymph node involvement was present in 58.4%, with macrometastasis in 51.2% and extranodal extension in 15.6%. Nodal positivity was significantly associated with larger tumor size (3.98 ± 1.78 vs. 2.61 ± 1.14 cm, P < 0.001), higher grade (P = 0.002), and lymphovascular invasion (60.3% vs. 23.1%, P < 0.001).

Conclusion: These findings indicate that lymph node metastasis is common in mastectomy specimens and is closely linked to adverse pathologic features. Larger tumors, higher histologic grade, and lymphovascular invasion appear to be the strongest correlates of nodal spread, underscoring the importance of careful pathologic lymph node assessment for accurate staging, prognostic stratification, and postoperative treatment planning in breast cancer patients.

Abstract Breast cancer remains one of the leading causes of cancer-related mortality among women globally, highlighting the critical need for early detection and accurate diagnosis. Recent advances in artificial intelligence (AI), encompassing both machine learning (ML) and deep learning (DL) approaches, have demonstrated significant potential in enhancing diagnostic accuracy, reducing human error, and supporting clinical decision-making. This systematic review critically analyzes existing studies that employ AI for breast cancer detection, focusing on methodological approaches, dataset characteristics, model performance, and interpretability. ML-based techniques, including support vector machines, random forests, and gradient boosting, show promising results in structured datasets, particularly where dataset sizes are limited, and interpretability is essential. In contrast, DL approaches, primarily convolutional neural networks and their variants, outperform ML in raw image analysis, multi-modal imaging, and complex feature extraction, achieving higher accuracy and sensitivity. Hybrid models integrating ML and DL, often augmented with radiomics features, offer a balanced framework, combining high predictive performance with improved interpretability. Additionally, explainable AI (XAI) techniques are increasingly applied to DL models, mitigating the “black-box” problem and fostering clinical trust. Despite these advancements, challenges remain, including the need for large, high-quality, multi-institutional datasets, computational resource demands, and generalizability across diverse populations. Low-resource and portable AI solutions offer potential for broader accessibility, though with modest reductions in predictive performance. Overall, AI demonstrates transformative potential in early breast cancer detection, particularly when combined with hybrid and explainable frameworks. Future research should prioritize multi-modal integration, rigorous cross-center validation, and deployment strategies that balance accuracy, interpretability, and accessibility, ultimately facilitating clinical adoption and improving patient outcomes.