Active clinical trials for "Osteosarcoma"

The purpose of this study is to look at the amount of function that returns in participants that have reconstruction with bone graft or artificial device and in participants who have tumor surgery plus regenerative osseous surgery. The study will look at the level of function for a period of 3 years after the surgery. Another purpose of this study is to look at how well the bone heals in participants undergoing regenerative surgery

The purpose of this study is to investigate tumors in the laboratory to determine how and why they respond, or fail to respond to different drug therapies. This study will also investigate why high pressure develops within tumors and how this affects how well drugs work. We will also take blood samples before and/or after your procedure to measure biochemical factors that may help us predict the behavior of osteogenic sarcoma and other solid tumors.

The aim of this study is to investigate the opinions of patients who have undergone bone tumor resection surgery about post-op exercises.

Longitudinal cohort study; measurements before start of systemic therapy and one year later.

Hypothesis: Tumor infiltrating neutrophils are present in osteosarcomas and chondrosarcomas and Experimental design: Exploratory observational research Population: Adult patients with osteosarcoma or chondrosarcoma Assessment criteria: Determination of the presence of TANs within tumors and their N1 or N2 profile, the presence of other immune cells, and the PDL-1 status of the tumor. Investigation plan: Proposal to participate at all eligible patient during the preoperative consultation. If the patient agrees to participate in the study he/she will be included. Participation in the study will not affect its coverage. Patient participation will lead to: Taking samples from an operative part, The collection of demographic data (age, sex, height, weight, body mass index), family history of cancer, history of bone trauma, sports practice, clinical and anatomopathology reports and data, medical history (Diabetes, Cardiovascular Diseases, Infectious Diseases, Autoimmune Diseases, Bone Metabolism Disorders, Allergies, Menopause, Drug treatments followed, Psychiatric disorders). Statistical analysis plan: Description in frequency of the presence of TANs, of N1 and N2 profiles, of the presence of other immune cells both qualitatively and quantitatively. Analysis in subgroups on relevant criteria (age, sex, location, type of tumor).

Data involving orthopaedic conditions and rehabilitation aspects of musculoskeletal and neuromuscular disorders will be collected and stored as part of the normal clinical care of patients seen in the University of Florida (UF) and Shands Orthopaedics and Sports Medicine Institute.

Osteosarcoma is regarded as most common malignant bone tumor in children and adolescents. Approximately 15% to 20% of patients with osteosarcoma present with detectable metastatic disease, and the majority of whom (85%) have pulmonary lesions as the sole site of metastasis. Previous studies have shown that the overall survival rate among patients with localized osteosarcoma without metastatic disease is approximately 60% to 70% whereas survival rate reduces to 10% to 30% in patients with metastatic disease. Though lately, neoadjuvant and adjuvant chemotherapeutic regimens can decline the mortality rate, 30% to 50% of patients still die of pulmonary metastases. Number, distribution and timing of lung metastases are of prognostic value for survival and hence computed tomography (CT) thorax imaging still plays a vital role in disease surveillance. In the last decade, the technology of multidetector CT scanner has enhanced the detection of numerous smaller lung lesions, which on one hand can increase the diagnostic sensitivity for lung metastasis, however, the specificity may be reduced. In recent years, deep-learning artificial intelligence (AI) algorithm in a wide variety of imaging examinations is a hot topic. Currently, an increasing number of Computer-Aided Diagnosis (CAD) systems based on deep learning technologies aiming for faster screening and correct interpretation of pulmonary nodules have been rapidly developed and introduced into the market. So far, the researches concentrating on the improving the accuracy of benign/malignant nodule classification have made substantial progress, inspired by tremendous advancement of deep learning techniques. Consequently, the majority of the existing CAD systems can perform pulmonary nodule classification with accuracy of 90% above. In clinical practice, not only the malignancy determination for pulmonary nodule, but also the distinction between primary carcinoma and intrapulmonary metastasis is crucial for patient management. However, most existing classification of pulmonary nodule applied in CAD system remains to be binary pattern (benign Vs malignant), in the lack of more thorough nodule classification characterized with splitting of primary and metastatic nodule. To the best of our knowledge, only a few studies have focuses on the performance of deep learning-based CAD system for identifying metastatic pulmonary nodule till now. In this proposed study, the investigators sought to determine the accuracy and sensitivity of one computer-aided system based on deep-learning artificial intelligence algorithm for detection and risk stratification of lung nodules in osteogenic sarcoma patients.

This is a phase II, single arm, open-label, interventional trial of pembrolizumab (MK-3475) in patients with osteosarcoma who have experienced disease relapse or progression after at least one line of systemic treatment, and who are not eligible for curative surgery.

The primary objective of this study is to evaluate the toxicity profile of GRID therapy using dose levels of 10Gy, 15 Gy and 20Gy in pediatric osteosarcoma of the extremity.

The primary goal of this study will be to examine tumor response after radiation treatment via a combination of Samarium-153 EDTMP and external beam radiotherapy.