Soft tissue sarcomas of the buttock: A systematic review and meta-analysis.

BACKGROUND: Soft tissue sarcomas of the buttock (BSTS) are historically associated with a poor prognosis. The literature includes retrospective studies with small sample size. It is difficult to estimate the rate of local recurrence (LR), distant metastasis (DM) and overall survival (OS). A systematic review and meta-analysis was deployed to summarize the available information on BSTS patients that underwent surgical treatment. METHODS: The PubMed, EMBASE, Scopus and Google Scholar databases were reviewed for eligible studies following PRISMA guidelines. INCLUSION CRITERIA: (1) primary BSTS confirmed by pathological biopsy (2) indication for surgical treatment (3) reporting either the rate of LR, DM, or OS at 5-year (4) articles published up to December 2021 (5) english language. The Methodological Index for Non-Randomized Studies (MINORS) was applied for the quality appraisal. RESULTS: Six eligible studies with 216 BSTS patients were identified. Most patients underwent surgical resection, associated with radiation therapy. The most represented histological types were liposarcoma (n = 43, 19,9%), rhabdomyosarcoma (n = 27, 12.5%), and undifferentiated pleomorphic sarcoma (n = 23, 10.6%). High-grade tumor rates ranged from 37% to 88.2%, marginal resection rates from 20.3% to 50%, LR rates from 0% to 62.5%, DM rates from 37.5% to 62.5%. The pooled 5-year OS from 1941 to 2002 was 41% (IC95%: 33%-49%). The average MINORS score was 9.3 (range: 6 to 11). CONCLUSIONS: Surgical resection of BSTS had on average dissatisfactory outcomes, and the 5-year overall survival was poor, with high rates of local recurrence and distant metastasis. An effort to collect new data with a higher level of evidence is warranted.

Orthopaedic surgeons' perceptions and attitudes on COVID-19 related changes in practice: an international cross-sectional survey.

INTRODUCTION: the purpose of this study was to assess the orthopaedic surgeons' perceptions and attitudes on COVID-19 related changes in their practice. METHODS: an online survey was shared with orthopaedic surgeons practicing in different countries. RESULTS: this study showed that orthopaedic surgery plan management was adapted to respond more effectively to the COVID-19 pandemic while maintaining the continuity of health care and ensuring protection of medical staff and patients. Among the introduced measures, elective surgery was postponed to free-up beds for suspected or COVID-19 positive patients requiring hospitalization. Additionally, the number of outpatient visits was considerably decreased and non-urgent visits were postponed to reduce the flow of patients in and out of hospitals and therefore minimize the risk of contamination. Interestingly, data revealed the willingness of orthopaedic surgeons to take care of COVID-19 positive patients and support their colleagues in intensive care units, if needed. CONCLUSION: orthopaedic surgery departments have adapted their programs to face the unprecedented challenges due to the COVID-19 pandemic. Quick measures were taken to reduce the risk of contamination in patients, medical staff and to allow hospitals to free up beds for treatment of patients with positive or suspected COVID-19.

A novel dataset and deep learning-based approach for marker-less motion capture during gait.

BACKGROUND: The deep learning-based human pose estimation methods, which can estimate joint centers position, have achieved promising results on the publicly available human pose datasets (e.g., Human3.6 M). However, these datasets may be less efficient for gait study, particularly for clinical applications, because of the limited number of subjects, their homogeneity (all asymptomatic adults), and the errors introduced by marker placement on subjects' regular clothing. RESEARCH QUESTION: How a new human pose dataset, adapted for gait study, could contribute to the advancement and evaluation of marker-less motion capture systems? METHODS: A marker-less system, based on deep learning-based pose estimation methods, was proposed. A new dataset (ENSAM dataset) was collected. Twenty-two asymptomatic adults, one adult with scoliosis, one adult with spondylolisthesis, and seven children with bone disease performed ten walking trials, while being recorded both by the proposed marker-less system and a reference system - combining a marker-based motion capture system and a medical imaging system (EOS). The dataset was split into training and test sets. The pose estimation method, already trained on the Human3.6 M dataset, was evaluated on the ENSAM test set, then reevaluated after further training on the ENSAM training set. The joints coordinates were evaluated, using Bland-Altman bias and 95 % confidence interval, and joint position error (the Euclidean distance between the estimated joint centers and the corresponding reference values). RESULTS: The Bland-Altman 95 % confidence intervals were substantially improved after finetuning the pose estimation method on the ENSAM training set (e.g., from 106.9 mm to 17.4 mm for the hip joint). With the new dataset and approach, the mean joint position error varied from 6.2 mm for ankles to 21.1 mm for shoulders. SIGNIFICANCE: The proposed marker-less system achieved promising results in terms of joint position errors. Future studies are necessary to assess the system in terms of gait parameters.