RESUMO
BACKGROUND: Major bile duct injury during cholecystectomy often requires surgical reconstruction. The optimal timing of repair is debated. OBJECTIVES: To assess the association between the timing of hepaticojejunostomy and postoperative morbidity, mortality, and anastomotic stricture. METHODS: Systematic review and meta-analysis of observational studies comparing early (<14 days), intermediate (14 days-6 weeks), and late (>6 weeks) repair. Primary outcomes were postoperative morbidity, mortality, and stricture rates. Pooled risk ratios were calculated. A generalized linear model was used to estimate odds per time interval. RESULTS: 20 studies were included in the systematic review. Of these, data from 15 studies was included in the meta-analyses. The 20 included studies comprised a total of 3421 patients who underwent hepaticojejunostomy for bile duct injury. Early repair was associated with lower morbidity versus intermediate repair (RR 0.73, 95% CI 0.54-0.98). Delayed repair had lower morbidity versus intermediate (RR 1.50, 95% CI 1.16-1.93). Delayed repair had a lower stricture rate versus intermediate repair (RR 1.53, 95% CI 1.07-2.20). Mortality was not associated with timing. CONCLUSIONS: Reconstruction between 2 and 6 weeks after bile duct injury should be avoided given the higher morbidity and stricture rates. Delayed repair after 6 weeks may be beneficial.
RESUMO
Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.
