Aims. Osseous invasion exhibited in soft-tissue sarcoma (STS) is recognized as a prognostic risk factor. Achieving a wide margin is the default surgical approach for local control. However, for STSs where the tumour is in contact with the adjacent cortex but without clear evidence of osseous invasion, such as medullary invasion, the question of whether bone resection can provide better local control or survival than more conservative sub-periosteal excision remains controversial. The aim of this study was to assess whether bone resection for thigh STS with cortical contact of the adjacent bone results in better local control and survival compared to sub-periosteal dissection, and to investigate the prognostic factors for clinical outcomes in STS. Methods. A retrospective cohort study was conducted on 142 patients with thigh STS exhibiting cortical contact but without medullary invasion, from May 2000 to May 2020. Patients underwent either composite bone resection or sub-periosteal excision.
Over time, the locking mechanism of Modular Universal Tumour and Revision System (MUTARS) knee arthroplasties changed from polyethylene (PE) to polyether-ether-ketone Optima (PEEK) and metal-on-metal (MoM) in an attempt to reduce the risk of mechanical failure. In this study, we aimed to assess the cumulative incidence of locking mechanism revision for symptomatic instability by type of material, and assess potential associated risk factors. The MUTARS Orthopaedic Registry Europe was used for a retrospective review of 316 patients (54% male (n = 170), median age 44 years (IQR 23 to 61)) who underwent a MUTARS knee arthroplasty for oncological indications between December 1995 and January 2023. The minimum follow-up was 12 months, and the median follow-up was 7.9 years (IQR 3.3 to 13.0). A competing risk model was used to estimate the cumulative incidence of first locking mechanism revision with death and revision for any other reason as competing events. Possible risk factors were assessed employing a univariate cause-specific hazards regression model.Aims
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