Introduction: The use of bilateral mastectomy with immediate reconstruction is increasing. Radiotherapy increases complications; however, its uses have been extended. We evaluate the profile of the complications and long-term failure of reconstruction through a comparative analysis with a cohort without radiotherapy.
Methods: Retrospective analysis of patients with breast cancer who underwent mastectomy with immediate reconstruction during 2000-2016. Three groups were evaluated: 1) patients who received radiotherapy and posterior breast reconstruction; 2) patients with bilateral mastectomy and immediate reconstruction following adjuvant radiotherapy; 3) patients who did not receive radiotherapy at all. Demographic variables, surgical techniques and postoperative morbidity were assessed. Outcomes and complications were compared between cohorts. Analysis was done with SPSS Statistics.
Results: 296 bilateral mastectomies with immediate reconstruction. Mean age 48.4 ± 9. No differences in comorbidity in the different groups. Group 1: 125 patients. Radiotherapy given 21.69 months before, on average. Complication rate: 20%. Failure of reconstruction rate: 20%. Reoperation rate: 33.6%. Group 2: 71 patients. Radiotherapy after reconstruction: mean 134.2 days. Complication rate: 36.7%. Failure of reconstruction rate: 21.1%. Reoperation rate: 16.9%. Group 3: 100 patients. Complication rate: 25%. Failure of reconstruction rate: 21%. Reoperation rate: 20%. Morbidity published in patients after radiotherapy before or after reconstruction is higher than complications in patients who did not receive radiotherapy. Even so, in our series they were similar. We found a higher sequelae rate in group 1, with almost double the rate of reoperation.
Conclusions: Patients who underwent radiotherapy before reconstruction had a higher risk of developing failure of reconstruction and needing reoperation than those patients who received radiotherapy after breast reconstruction or did not receive radiotherapy at all.
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http://dx.doi.org/10.1016/j.ciresp.2018.10.014 | DOI Listing |
Am J Sports Med
January 2025
Department of Orthopaedic Surgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Background: Anterior cruciate ligament (ACL) injuries are common in pediatric and adolescent patients. Understanding this population's injury characteristics and treatment strategies is vital for managing this high-risk group.
Purpose: To report the descriptive epidemiology and treatment strategies of a large cohort of skeletally immature patients with complete ACL tears.
BMC Surg
January 2025
General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China.
Background And Aim: Hepatocellular carcinoma (HCC) is a highly immunogenic tumor and the third leading cause of cancer-related deaths worldwide with an increasing incidence. Therefore, the combination of immunotherapy with other approaches, such as anti-angiogenic agents and local area therapy, has become a new strategy for HCC treatment.
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BMC Musculoskelet Disord
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Department of Spine Surgery, Ameos Clinic Eutin, Eutin, Germany.
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Methods: SD undergoing PSF were retrospectively analyzed. Clinical outcome was determined using SRS-22- and Eq.
BMC Musculoskelet Disord
January 2025
Biomedical Engineering Department, Faculty of Engineering, Helwan University, Cairo, Egypt.
BMC Bioinformatics
January 2025
Mathematics and Computer Science Department, University of the Balearic Islands, Ctra Valldemossa, Km 7.5, Palma, 07122, Balearic Islands, Spain.
Background: MetaDAG is a web-based tool developed to address challenges posed by big data from omics technologies, particularly in metabolic network reconstruction and analysis. The tool is capable of constructing metabolic networks for specific organisms, sets of organisms, reactions, enzymes, or KEGG Orthology (KO) identifiers. By retrieving data from the KEGG database, MetaDAG helps users visualize and analyze complex metabolic interactions efficiently.
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