Clinical Trial Details
— Status: Not yet recruiting
Administrative data
| NCT number |
NCT06381531 |
| Other study ID # |
4410 |
| Secondary ID |
|
| Status |
Not yet recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
June 10, 2024 |
| Est. completion date |
June 10, 2027 |
Study information
| Verified date |
April 2024 |
| Source |
Tata Memorial Centre |
| Contact |
ARCHYA DASGUPTA, MD |
| Phone |
91-22-24177000 |
| Email |
archya1010[@]gmail.com |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial 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.
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 10,000 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, i.e., age groups, e.g., 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, e.g., 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 (e.g., 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).
