Neoplasms

All Conditions

Nervous System Diseases

Musculoskeletal Diseases

Ear, Nose, and Throat Diseases

Rare Diseases

Glioblastoma

Glioma

Neurilemmoma

Brain Neoplasms

Central Nervous System Neoplasms

Medulloblastoma

Meningioma

Nervous System Neoplasms

Spinal Cord Neoplasms

Pinealoma

Astrocytoma

Ependymoma

Craniopharyngioma

Adamantinoma

Neuroma, Acoustic

Germ Cells Tumors

Schwannoma

Pineocytoma

Acoustic Neuroma

Pineoblastoma

Oligoastrocytoma

tozuleristide 24 or 36 mg administered intravenously 1-24 hours prior to surgery

tozuleristide

imaging device attached to surgical microscope

Canvas imaging system

Standard of care surgical resection of tumor

Surgical resection of tumor

Clinical Trial Navigator

Ray Chu, MD

Chirag Patil, MD

Jeremy Rudnick, MD

Jethro Hu, MD

Serguei Bannykh, MD, PhD

John Yu, MD

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).

Fluorescence Detection of Adult Primary Central Nervous System Tumors With Tozuleristide and the Canvas System

A Phase 2 Study of Fluorescence Detection of Adult Primary Central Nervous System Tumors in Subjects Receiving Tozuleristide and Imaged With the Canvas System

Recorded and graded according to the National Cancer Institute's Common Terminology Criteria for Adverse Events Version 5

Blaze Bioscience Inc.

John Yu

Director, Surgical Neuro-Oncology

Symptoms and General Pathology

Recurrence

Disease Attributes

Neoplasms, Neuroepithelial

Neuroectodermal Tumors

Neuroectodermal Tumors, Primitive

Neoplasms, Germ Cell and Embryonal

Neoplasms by Histologic Type

Neoplasms, Glandular and Epithelial

Neoplasms, Nerve Tissue

Neuroepithelioma

Activated T cells (ATC) administered intravenously at one timepoint

Activated T cells

Kortnee Calkins

Justin Darrah, MD

The purpose of this study is to examine the use of activated T cells (ATCs) to assess the safety and tolerability of autologous activated T cells, as measured by the number of Grade 3 or higher toxicities, the number of serious adverse events, and treatment-related toxicities, according to National Cancer Institute Common Toxicity Criteria for Adverse Events (NCI CTCAE) Version 5, to find the maximum tolerated dose. The secondary objectives include evaluating the rate of overall survival, rate of progression-free survival, health-related quality of life parameters, overall response rate, immune response, and tumor stem cell antigen expression.

Activated Autologous T Cells Against Glioma Cancer Stem Cell Antigens for Patients With Recurrent Glioblastoma

A Phase I Trial of Activated Autologous T Cells Against Glioma Cancer Stem Cell Antigens for Patients With Recurrent Glioblastoma

Recorded and graded according to National Cancer Institute Common Toxicity Criteria for Adverse Events (NCI CTCAEs) Version 5

Measured by change in the Functional Assessment of Cancer Therapy - Brain (FACT-Br) survey score. The FACT-Br total score has a range of 0-200 and higher scores indicate better quality of life

Percentage of patients showing either partial response or complete response, in patients with subtotal resection, will be measured using Magnetic Resonance Imaging (MRI) and Immunotherapy Response Assessment in Neuro-Oncology (iRANO) Response Criteria

Assessed by cytotoxic T cell activity in vitro pre- vs post-infusion.

Assessed by quantitative PCR for expression of CD133, housekeeping genes, and tumor associated antigens (including HER2, TRP-2, gp100, MAGE-1, IL13Rα2, AIM-2).

Kairos Pharma

Jeremy Rudnick, M.D

Co-Director, Neuro-Oncology

Antineoplastic Agents

All Drugs and Chemicals

Pembrolizumab

Immunomodulating Agents

Antineoplastic Agents, Immunological

Immune Checkpoint Inhibitors

Pembrolizumab 400mg administered intravenously on Day 1, and then beginning 6 weeks post-surgery, subjects will receive 400 mg pembrolizumab every 6 weeks

Standard of care stereotactic radiation of 24 grays over 3 days, administered beginning on Day 7

Stereotactic Radiation Therapy

Standard of care surgical resection of tumor on Day 10-28

Surgical Resection

Clinical Trial Recruitment Navigator

Stephen Shiao, MD, PhD

Keith Black, MD

Adam Mamelak, MD

Amin Mirhadi, MD

Behrooz Hakimian, MD

Chirag G Patil, MD, MS

The purpose of this study is to assess the safety/tolerability/feasibility of pembrolizumab and radiation therapy before surgical resection in patients with recurrent glioblastoma as defined by treatment-related AEs and the number of patients who do not necessitate a delay in surgical resection, and to assess overall survival. The secondary objectives are to assess progression free survival, and to assess the T cell clonality, CD8 T cell activation and Tumor Infiltrating Lymphocyte (TIL) score after treatment

Trial of Anti-PD-1 Immunotherapy and Stereotactic Radiation in Patients With Recurrent Glioblastoma

Phase Ib/II Trial of Anti-PD-1 Immunotherapy and Stereotactic Radiation in Patients With Recurrent Glioblastoma

Safety and tolerability measured by the incidence of adverse events, serious adverse events and grade 3 or above treatment related adverse events as assessed per CTCAE, Version 5.0.

From start of study treatment until death, loss to follow-up, or withdrawal of consent. Subjects who are lost to survival follow-up will not be replaced, and public records may be accessed to assess Overall Survival.

From start of study treatment until confirmation of disease progression (per iRANO Criteria), intolerable toxicities, withdrawal of consent, or up to 2 years or 18 cycles of pembrolizumab, whichever comes first.

To assess the T cell clonality, CD8 T cell activation and Tumor Infiltrating Lymphocyte (TIL) score after treatment. Blood draws for research purposes performed after obtaining consent but prior to neoadjuvant pembrolizumab administration, and at the time of initiation of SRT and at the time of surgery

Merck Sharp & Dohme LLC

National Cancer Institute (NCI)

Chirag G. Patil

Associate Professor of Neurosurgery Program Director, Neurosurgery Residency Training

All patients age from 18-75 years with CNS tumors are included and count as one group

Jinsong Wu, Ph.D. & M.D.

Lei Jin, B.S.

Jinsong Wu, Ph.D. & M.D

CNS tumor requires biopsy for pathological diagnosis, which is known as the "golden standard". We would like to achieve automated classification of brain tumors based on deep learning in digital histopathology images and molecular pathology results. We expect to develop an assistant system (including software and hardware), to help pathologists during their diagnosis for CNS tumor.

The aim of the study is to develop an automated pathological diagnosis system for CNS tumors based on deep learning technique. It is designed to firstly develop the best deep learning model for pathological diagnosis of CNS tumors, in order to improve the accuracy of pathological diagnosis. Then to be used clinically, reduce the workload and stress of neuropathologists and obtain the benefits for CNS tumor patients.

Different CNS tumors including meningioma, glioma, lymphoma and other various tumors have their own different treatment principles and plans. For example, high grade glioma requires operational resection and post-operational chemo-radiotherapy. However, operational resection is not significant for improving prognosis in lymphoma patients, systematic chemotherapy will be performed after specific diagnosis based on biopsy. Therefore, in this study, an automated CNS tumor pathological diagnosis system will be developed to classify the different type of those tumors.

At present, pathological diagnosis of CNS tumors is based on histopathological characteristics and molecular information after a systematic analyzed by pathologists. The accuracy of the diagnosis very much relies on the experience of the pathologists. However, to become a experienced and qualified pathologist requires years of training. Pathologists may give completely different diagnose outcome for the same patient. Thus, it is essential to develop a system that can assist pathologists.

Deep learning is one of the most advanced techniques of artificial intelligence. In particular, the ability of image recognition is extremely powerful. Therefore, we are able to develop a model for histopathological section images based on deep learning. WHO Classification of CNS Tumors 2016 has included molecular markers as the important part of diagnosis. Hence, there will be an additional model of molecular pathology to be added to the system.

Huashan Hospital has one of the largest CNS tumor biobank in China, which is the key part for deep learning, as it needs large amount of data. The case load of this study is able to show the representative and authoritative of those data.

There will be three stages of the study. Stage 1 and 2 are supervised learning process. Stage 1 is to develop the best deep learning model for histopathological diagnosis of CNS tumors, we anticipate the accuracy for the first model to achieve at least 70%. The training data (pathological sections) will be provided by Huashan Hospital CNS tumor biobank. In the mean time, a micro-positioning platform is under investigation for the use of image collection. At the end of stage 1, we anticipate to integrate the model (software) and the platform (hardware) as the whole diagnose system for histopathological images. Stage 2 is to design a model for molecular pathological diagnosis for CNS tumors. The model will be trained by numerous amount of related molecular information extracted from those pathological sections. At the end of stage 2, we anticipate to combine stage 1 system and stage 2 model as the primary prototype. Stage 3 is known as the unsupervised learning process. By using the prototype developed after previous stages, the system will be used clinically. With the incoming of more patients and data, together with pathologists in the hospital, it will give its diagnosis. By comparing the results with pathologists, it will be able to self-learn and improve the accuracy as the time goes on. By the end of stage 3, we anticipate to have the system ready for independent clinical pathological diagnosis ability with the accuracy greater than 90%.

Artificial Intelligence Neuropathologist

Artificial Intelligence Neuropathologist - Automated CNS Tumor Pathological Diagnosis Based on Deep Learning

After supervised training, the software of the histopathological diagnosis of CNS tumor achieve at least 70% accuracy

Hardware investigation for pathology section image collection, to automatically scan the section images.

Molecular information being added to the histopathological diagnosis regarding to WHO 2016 CNS Tumor guide. Combine histopathology and molecular to give final diagnosis

Improve diagnosis accuracy of the system by continuous collection with a large number of CNS tumor cases from Huashan Hospital, which can be done without supervision.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Fluorescence Detection of Adult Primary Central Nervous System Tumors With Tozuleristide and the Canvas System	NCT04743310	Central Nervous...	tozuleristide Canvas imaging ... Surgical resect...	18 Years -	Cedars-Sinai Medical Center
Activated Autologous T Cells Against Glioma Cancer Stem Cell Antigens for Patients With Recurrent Glioblastoma	NCT05341947	Recurrent Gliob...	Activated T cel...	18 Years -	Cedars-Sinai Medical Center
Fluorescence Detection of Adult Primary Central Nervous System Tumors With Tozuleristide and the Canvas System	NCT04743310	Central Nervous...	tozuleristide Canvas imaging ... Surgical resect...	18 Years -	Cedars-Sinai Medical Center
Trial of Anti-PD-1 Immunotherapy and Stereotactic Radiation in Patients With Recurrent Glioblastoma	NCT04977375	Glioblastoma Mu...	Pembrolizumab Stereotactic Ra... Surgical Resect...	18 Years -	Cedars-Sinai Medical Center
Artificial Intelligence Neuropathologist	NCT05300113	CNS Tumor Neuropathology		18 Years - 75 Years	Huashan Hospital

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