Clinical Research Directory

Fluorescence-Guided Imaging of Brain Tumors: A Safety Study Using SBK2-ICG

Participants in this research study are people who are likely to have, or have been diagnosed with a brain tumor, for which surgical removal (or "resection") is the standard of care treatment. The purpose of this study is to see whether a drug called SBK2-ICG can be used to locate the true outline or "edges" of the tumor. If the tumor outline could be accurately identified at the time of surgery, the fullest extent of tumor could be removed while sparing the normal brain tissue. Participants will receive SBK2-ICG about an hour before they receive surgery. The extent of surgery to be performed will not be changed in this study. Researchers will only use the information from the study to determine the best SBK2-ICG dose for accurate tumor margin (i.e., the border or edges of the tumor with the normal brain) detection so that no tumor is left behind. The use of SBK2-ICG in brain tumors is experimental, which means that the U.S. Food and Drug Administration (FDA) has not approved it for use to locate brain tumors. However, the use of the drug SBK2-ICG for the purposes of this study is on file with the FDA.

PET Imaging of Patients With Melanoma and Malignant Brain Tumors Using an 124I-labeled cRGDY Silica Nanomolecular Particle Tracer: A Microdosing Study

Current tests to detect cancer, including CAT scans and MRI scans, are limited. PET scans use special dyes that are injected into a vein and can better localize possible cancer. The investigators have developed a new particle that can carry a radioactive dye to a very specific area of the tumor. When using a PET scan the radioactive dye can be viewed in areas of possible disease. This particle has been studied in mice and was safe. The particles will not treat the cancer and any images or information found during this study will not be used for your treatment. The information collected may be used to guide the design of future studies to detect and/or treat tumors.

A Study of the Boron Neutron Capture Therapy (BNCT) Using B10 L-BPA in Malignant Brain Tumors

This Phase I/II study, titled 'A Phase I/II Study to Evaluate Safety and Efficacy of the Boron Neutron Capture Therapy (BNCT) using B10 L-BPA as Boron Carrier in Malignant Brain Tumors.', aims to assess the efficacy of B10 L-BPA with BNCT in patients with malignant brain tumors. The primary objective is to evaluate the safety and efficacy of BNCT with B10 L-BPA for malignant brain tumors treatment, using the Response Evaluation Criteria in Solid Tumors, Version 1.1 (RECIST v1.1) as the standard for assessment.

Phase I Study of HSK42360 in Malignant Brain Tumors With BRAF V600 Mutation

This is a phase I, open-label, dose-escalation and expansion study to evaluate the safety, tolerability, PK of HSK42360 when given orally in pediatric patients with active BRAF V600 mutation recurrent malignant brain tumors.

Expert Consensus and Artificial Intelligence in Medical Decision Making in Patients with Malignant Brain Tumors

Nearly 23,000 adults are diagnosed with primary central nervous system (CNS) malignancy yearly. An additional 200,000 adults are diagnosed with brain metastasis. There are significant variations in CNS tumor treatment. However, due to significant heterogeneity in patient baseline factors, identifying unwarranted variation is challenging. Ghogawala et al have previously demonstrated that, among patients undergoing surgical treatment of cervical myelopathy and lumbar degenerative spinal disease, an expert panel consisting of surgeon experts can identify variations in proposed surgical procedure and demonstrated superior patient outcomes when the surgery performed matched the procedure recommended by expert consensus. Expert panel surveys have not previously been used to identify variations in care among patients with CNS malignancy. The primary aim is to determine whether patient outcomes are superior when treatment aligns with recommendations made by a clinical expert neurosurgical panel. The study also seek to identify patient factors that predispose to variability in care. Our long-term aim is to determine whether predictive artificial learning algorithms can achieve the same outcomes, or better, as clinical expert panels, but with greater efficiency and greater capacity to be available for more patients. The investigators hypothesize that: * When a team of 10 medical experts has greater than 80% consensus regarding optimal treatment and when the doctor and patient select that specific treatment, the outcome is superior than when a patient and doctor select an alternative procedure. * When a team of 10 medical experts has greater than 80% consensus regarding optimal treatment, the structured data used by the experts can be processed and trained by computing algorithms to predict the pattern recognized by the experts - i.e. - the computer can predict how an expert panel would vote. Procedures include the following: 1. Chart review portion of study: Patients will be identified from case logs of the principal investigators from July 2017 through July 2023. Data will be collected retrospectively and will include age, non-identifier demographics, diagnosis details, operative/treatment characteristics, post-treatment characteristics, and follow-up characteristics. Images reviewed will include pre and post-treatment MRIs obtained as part of routine care. Data will be abstracted from the medical record (Epic/Soarian and PACS) and recorded in an excel database. 2. Survey portion of study: De-identified structured radiographic data and a brief clinical vignette without patient identifiers will be uploaded to Acesis Healthcare Process Optimization Platform (http://www.acesis.com/our-platform). A survey will be generated by Acesis and emailed to the subject experts/participants. This portion is prospective. 3. Cohort definitions: 1. Patients will be assigned to either "expert-treatment consensus" or "no expert-treatment consensus" arms based on whether greater than 80% consensus is achieved 2. Patients will be assigned to either "Expert consensus-aligned" or "Expert consensus - unaligned" arms based on whether expert survey results match actual treatment given. 4. Data will then be analyzed using appropriate packages with SAS statistical analysis software. Survival analysis will be performed to determine whether consensus predicts improved progression free survival (PFS). 5. The structured and de-identified radiographic images used by the experts in surveys will be used for training and development of an AI algorithm. The aim of this portion of the study is to determine whether standardized and structured imaging can be used to train an algorithm to predict whether expert consensus is achieved and the recommended treatment.