8 Clinical Trials for Various Conditions
This pilot clinical trial studies beta-tricalcium phosphate bone graft in treating patients undergoing surgery for metastatic spine cancer. A bone graft may help healing and bone growth in patients undergoing surgery for spine cancer
This randomized clinical trial studies minimally invasive surgery in treating patients with spinal tumors. Posterior spinal tumor resection and anterior and posterior spinal tumor resection are less invasive types of surgery for spinal tumors and may have fewer side effects and improve recovery
The purpose of this study is to measure the benefit of adding abemaciclib to the chemotherapy, temozolomide, for newly diagnosed high-grade glioma following radiotherapy. Your participation could last approximately 11 months and possibly longer depending upon how you and your tumor respond.
The purpose of this study is to examine the use of a single dose of tozuleristide (24 or 36 mg) and the Canvas imaging system during surgical resection of primary central nervous system (CNS) tumors: Primary gadolinium enhancing (high grade) CNS tumors, primary non-gadolinium enhancing CNS tumors, and primary vestibular schwannoma. The primary objectives of the study is to see how well tozuleristide and the Canvas imaging system during surgical resection will show fluorescence among primary enhancing/high grade CNS tumors; and among the tumors that demonstrate tozuleristide fluorescence, to estimate the true positive rate and true negative rate of fluorescence in tissue biopsies, as well as sensitivity and specificity of tozuleristide fluorescence for distinguishing tumor from non-tumoral tissue. The secondary objectives of the study include evaluating the safety of tozuleristide and the Canvas imaging system, and to determine if the presence of remaining fluorescence at the time of surgery corresponds to remaining tumor evident on post-operative MRI images, or if the absence of fluorescence corresponds to evidence of no gross residual tumor on post-operative magnetic resonance imaging (MRI).
RATIONALE: Drugs used in chemotherapy, such as carboplatin and topotecan, work in different ways to stop the growth of tumor cells, either by killing the cells or by stopping them from dividing. Tamoxifen may help carboplatin work better by making tumor cells more sensitive to the drug. PURPOSE: This phase II trial is studying the side effects of giving carboplatin and topotecan together with tamoxifen and to see how well it works in treating patients with central nervous system metastases or recurrent brain or spinal cord tumors.
RATIONALE: Radiation therapy uses high-energy x-rays to kill tumor cells. Erlotinib may stop the growth of tumor cells by blocking some of the enzymes needed for cell growth and by blocking blood flow to the tumor. It may also make tumor cells more sensitive to radiation therapy. Giving radiation therapy together with erlotinib may kill more tumor cells. PURPOSE: This phase I/II trial is studying the side effects and best dose of erlotinib when given together with radiation therapy and to see how well they work in treating young patients with newly diagnosed glioma.
RATIONALE: Stereotactic body radiation therapy may be able to send x-rays directly to the tumor and cause less damage to normal tissue. Vertebroplasty may help prevent fractures and spinal cord compression caused by spinal metastasis. Giving stereotactic body radiation therapy together with vertebroplasty may help lessen pain and improve quality of life of patients with spinal metastasis. PURPOSE: This phase II trial is studying how well giving stereotactic body radiation therapy together with vertebroplasty works in treating patients with localized spinal metastasis.
This project adds to non-invasive BCIs for communication for adults with severe speech and physical impairments due to neurodegenerative diseases. Researchers will optimize \& adapt BCI signal acquisition, signal processing, natural language processing, \& clinical implementation. BCI-FIT relies on active inference and transfer learning to customize a completely adaptive intent estimation classifier to each user's multi-modality signals simultaneously. 3 specific aims are: 1. develop \& evaluate methods for on-line \& robust adaptation of multi-modal signal models to infer user intent; 2. develop \& evaluate methods for efficient user intent inference through active querying, and 3. integrate partner \& environment-supported language interaction \& letter/word supplementation as input modality. The same 4 dependent variables are measured in each SA: typing speed, typing accuracy, information transfer rate (ITR), \& user experience (UX) feedback. Four alternating-treatments single case experimental research designs will test hypotheses about optimizing user performance and technology performance for each aim.Tasks include copy-spelling with BCI-FIT to explore the effects of multi-modal access method configurations (SA1.3a), adaptive signal modeling (SA1.3b), \& active querying (SA2.2), and story retell to examine the effects of language model enhancements. Five people with SSPI will be recruited for each study. Control participants will be recruited for experiments in SA2.2 and SA3.4. Study hypotheses are: (SA1.3a) A customized BCI-FIT configuration based on multi-modal input will improve typing accuracy on a copy-spelling task compared to the standard P300 matrix speller. (SA1.3b) Adaptive signal modeling will allow people with SSPI to typing accurately during a copy-spelling task with BCI-FIT without training a new model before each use. (SA2.2) Either of two methods of adaptive querying will improve BCI-FIT typing accuracy for users with mediocre AUC scores. (SA3.4) Language model enhancements, including a combination of partner and environmental input and word completion during typing, will improve typing performance with BCI-FIT, as measured by ITR during a story-retell task. Optimized recommendations for a multi-modal BCI for each end user will be established, based on an innovative combination of clinical expertise, user feedback, customized multi-modal sensor fusion, and reinforcement learning.