Ignite Creation Date:
2024-05-06 @ 8:26 PM
Last Modification Date:
2024-10-26 @ 3:27 PM
Study NCT ID:
NCT06381531
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-04-24
First Post:
2024-04-18
Brief Title:
Tracing Brain Tumors Through Deep Time
Sponsor:
Tata Memorial Centre
Organization:
Tata Memorial Centre
Study Overview
Official Title:
Tracing Brain Tumors Through Deep Time
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
TRACE
Brief Summary:
Brain tumors involve different age groups with a wide range of tumor types involving different anatomical compartments of the brain The evolution of the brain in vertebrates including the most recent homo species including humans has occurred through increasing structural complexity in more evolved species In the retrospective study we will investigate the location of the tumors and different structural aspects of skull anatomy in patients with brain tumors The information will be compared with the anatomical evolution of the brain and skull in vertebrates to look for possible associations which can provide insights into evolutionary biology
Detailed Description:
Patients pediatric and adults with a diagnosis radiological histopathological of primary brain tumors registered in the neuro-oncology disease management group between January 2005 and December 2023 will be screened Approximately 500-600 patients are expected to be eligible per year with imaging data approximately 500 patients are treated annually with radiation in our center with available CT data for radiation planning and another 100-200 patients having pre-operative or post-operative scans For the above-mentioned time period data is expected to be available from approximately 10000 patients which will be the upper limit of sample size for the current study
The area of the primary tumor or cavity and residual tumor indicating original location for post-operative data will be segmented on CT and or MRI as available The peritumoral edema will be excluded from the segmented region The segmentation will be done manually in an initial cohort of approximately 200-500 patients Subsequently a machine learning algorithm like a 3D U-net or deep learning-based technique will be trained on the initial data and validated on the next 100-200 patients to assess algorithm accuracy and robustness for rapid implementation and segmentation of the large data set Once brain tumor regions are identified across the entire population density maps will be generated to reciprocate the location of tumors on a quantitative scale as per age of the patient during diagnosis age in years as continuous data and categorical data ie age groups eg infants children teens adolescents adults and elderly The generated density maps will be compared with regions of vertebrate brain regions with openly available literature across species with regards to the geological scale deep time units eg in units of 10-50 million years Similarly the skull bony anatomy will be extracted from CT and or MRI data applying techniques like window intensity thresholds without the need for segmentation Patients with major defects in the calvarial skull from increased intracranial pressure or surgical interventions will be excluded from the analysis of calvarial anthropometry however it will be available for skull base anatomy assessment The organizational patterns will be analyzed using machine learning models and other statistical models like Bayesian statistics and compared with other publicly available normal human populations without brain tumors adjusting for age race as applicable fossil data of vertebrates hominids non-human primates for link recognition The density maps and anthropometric data will be compared within the entire cohort of patients with brain tumors from the study stratified by factors like age as mentioned earlier tumor location eg supratentorial vs infratentorial tumor grade benign vs low grade vs high grade The statistical analysis for density maps and anthropometry will be done by sharing anonymized data with collaborators with expertise in similar research from the Indian Statistical Institute Geological Studies Unit and Interdisciplinary Statistical Research Unit
The area of the primary tumor or cavity and residual tumor indicating original location for post-operative data will be segmented on CT and or MRI as available The peritumoral edema will be excluded from the segmented region The segmentation will be done manually in an initial cohort of approximately 200-500 patients Subsequently a machine learning algorithm like a 3D U-net or deep learning-based technique will be trained on the initial data and validated on the next 100-200 patients to assess algorithm accuracy and robustness for rapid implementation and segmentation of the large data set Once brain tumor regions are identified across the entire population density maps will be generated to reciprocate the location of tumors on a quantitative scale as per age of the patient during diagnosis age in years as continuous data and categorical data ie age groups eg infants children teens adolescents adults and elderly The generated density maps will be compared with regions of vertebrate brain regions with openly available literature across species with regards to the geological scale deep time units eg in units of 10-50 million years Similarly the skull bony anatomy will be extracted from CT and or MRI data applying techniques like window intensity thresholds without the need for segmentation Patients with major defects in the calvarial skull from increased intracranial pressure or surgical interventions will be excluded from the analysis of calvarial anthropometry however it will be available for skull base anatomy assessment The organizational patterns will be analyzed using machine learning models and other statistical models like Bayesian statistics and compared with other publicly available normal human populations without brain tumors adjusting for age race as applicable fossil data of vertebrates hominids non-human primates for link recognition The density maps and anthropometric data will be compared within the entire cohort of patients with brain tumors from the study stratified by factors like age as mentioned earlier tumor location eg supratentorial vs infratentorial tumor grade benign vs low grade vs high grade The statistical analysis for density maps and anthropometry will be done by sharing anonymized data with collaborators with expertise in similar research from the Indian Statistical Institute Geological Studies Unit and Interdisciplinary Statistical Research Unit
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None