Active clinical trials for "Sarcoma"

The purpose of this project was to use multi-omics technology to screen the key factors for the occurrence and development of uterine sarcoma.

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

This study investigates the surgical management of lymph node basins for extremity and trunk soft tissue sarcoma (ETSTS) to identify and better understand the surgical practice patterns of sarcoma surgeons. ETSTS has been known to spread to distant locations including lymph nodes, with some subtypes of the disease spreading to lymph nodes more than others. This has led to sarcoma surgeons to treat patients differently from one another, including those with more lymph node involvement. The purpose of this study is to investigate the practice patterns of ETSTS surgeons.

To date, the radiation oncologist are missing biomarkers predictive of response/resistance to RT in order to identify patients who may benefit from RT and personalize the RT schedule. Our proposal is to conduct a cohort study aiming at identifying transcriptomic biomarkers predictive of sensitivity and/or resistance to RT in limbs STS patients

SarcBOP - An interdisciplinary and translational registry SarcBOP aims to establish a database that integrates every aspect possibly relevant to sarcoma treatment and research. SarcBOP thus will not be limited to specific questions or patient groups, but instead will build a comprehensive database including clinical, pathologic, and radiologic information, multi-layered molecular data, and patient-reported outcomes, combined with a dedicated biobank for tissue samples and liquid biopsies. As the study integrates seamlessly with the clinical activities of the Heidelberg Sarcoma Center, the Molecular Diagnostics Program of NCT Heidelberg, including the NCT/DKTK MASTER Program, and with the NCT Trial Center, including the PMO Clinical Trials Program, SarcBOP will generate a comprehensive and continuously growing resource for clinicians, researchers, and, finally, patients.

Undifferentiated embryonal sarcoma of the liver is the 3rd most common malignant liver tumor after hepatoblastoma and hepatocellular carcinoma with a peak incidence between 6 and 10 years of age. Historically, it is a tumor treated only by surgery with a poor prognosis. In the last decade, the combination of more intensive chemotherapy and, more randomly, radiotherapy, has significantly improved the survival rate of these patients. Due to its low incidence, there are few series reported in the literature and to date there is no specific treatment protocol for the management of these tumors. It seems appropriate to review the management of these tumors in France in order to discuss the best therapeutic strategy.

The aim of this study was to developed and validated models to predict therapeutic responses and patients' survivals in patients with soft tissue sarcoma and compared these models with currently available models.

This pilot clinical trial studies real-time contrast-enhanced ultrasonography and shear wave elastography in predicting treatment response in patients with soft tissue sarcomas. Ultrasonography and elastography are diagnostic imaging tests that use sound waves to make pictures of the body without using radiation (x-rays). Real-time contrast-enhanced ultrasonography and shear wave elastography may help measure a patient's response to treatment given before surgery in patients with soft tissue sarcoma.

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.

The primary significance of this project is to perform a detailed analysis to assess the diagnostics, treatment and follow-up care, to develop a prediction model for future patient selection regarding various diagnostic and treatment procedures.