View clinical trials related to Sarcoma.
Filter by:This is a single-arm trial that will evaluate the safety and feasibility of the Tumor-infiltrating lymphocyte (TIL) treatment and the persistence of TIL survival in vivo following treatment
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.
Well-designed observational studies of individuals with rare tumors are needed to improve patient care, clinical investigations, and the education of healthcare professionals. The patterns of care, outcomes, and prognostic factors of a cohort of 2225 patients with metastatic soft tissue sarcomas who were diagnosed between 1990 and 2013 and documented in the prospectively maintained database of the French Sarcoma Group will be analyzed with a focus on : number/frequency of systemic treatments per patient, number/frequency of patients with locoregional treatment of the metastasis, number/frequency of patients with chemotherapy, number/frequency of patients with an off-label drug. Outcome (time-to-next treatment [TNT] and overall survival [OS]) will be reported according to histological subtype, as well as the association between TNT and OS. Prognostic factors of OS will be investigated.
This is single institution cases series review of histological and clinical data
This clinical trial studies how well a nurse-driven telephone intervention improves side effects in patients with cancer who are undergoing chemotherapy. Receiving calls from a nurse at home while receiving chemotherapy may improve the management of side effects and overall care in cancer patients.
There is a need for better visualization of resection margins during surgery for soft tissue sarcoma. Optical molecular imaging of soft tissue sarcoma associated biomarkers is a promising technique to accommodate this need. The biomarker Vascular Endothelial Growth Factor (VEGF-A) is overexpressed in soft tissue sarcoma versus normal tissue and has proven to be a valid target for molecular imaging. VEGF-A can be targeted by the monoclonal antibody bevacizumab. Monoclonal antibodies can be labeled by the near-infrared (NIR) fluorescent dye IRDye800CW (800CW). The investigators hypothesize that bevacizumab-800CW accumulates in VEGF expressing cancer, enabling soft tissue sarcoma visualization using a NIR intraoperative camera system. In this pilot intervention study the investigators will determine the optimal dosage of bevacizumab-800CW (10, 25 or 50mg) to detect soft tissue sarcoma intraoperatively.
This study is aimed to examine the value of incisional negative pressure therapy after resection of soft tissue tumors. Its a prospective randomized trial comparing incisional negative pressure to standard wound dressings.
Prospective feasibility study of perioperative nutrition in patients affected by primary retroperitoneal sarcoma
This study is a prospective, non-randomized feasibility study. Freshly isolated tumor cells from patients will be screened using state-of-the-art viability assay designed for ex vivo high-throughput drug sensitivity testing (DST). In addition, genetic information will be obtained from cancer and normal (germline) tissue and correlated with drug response. This study will provide the platform for informing treating physician about individualized treatment options. The main outcome of this study will be the proportions of the patients whose treatment was guided by the personalized medicine approach.
This study is a multi-center, non-interventional retrospective medical records review. The study will involve identification of medical records of patients with confirmed locally advanced unresectable or metastatic ES, who initiated systemic therapy between January 1, 2000 and December 31, 2017. Data for the chart review will be extracted retrospectively from eligible subjects' charts (electronic or paper). Information on prior surgical treatment and neoadjuvant/adjuvant therapies for ES will also be collected for ineligible subjects with a locally advanced or metastatic ES diagnosis who did not initiated systemic therapy. Data collected will be anonymized by the investigators and will not be traceable back to individual subjects by the sponsor (i.e., no protected health information [PHI] will be collected).