Trainee Project
Title:
Contribution of spatial statistics in the study of brain tumour localization
Dates:
2023/04/12 - 2023/09/29
Student:
Supervisor(s):
Other supervisor(s):
Dr MEZIERES Sophie (sophie.mezieres@univ-lorraine.fr)
Description:
Context
The master's work proposed here will take place in the context of a multidisciplinary collaboration with the CHRU of Nancy, particularly Dr Fabien Rech (neurosurgeon at the CHRU of Nancy and researcher at the CRAN). This collaboration concerns the treatment of low-grade diffuse gliomas (slow-moving brain tumours).
It will be based on a local database of 184 patients.
General description of the subject
Management of patients with low grade diffuse glioma should be based on regular multimodal MRI assessments (2-4 times per year, with a median duration of 10-15 years). This allows, at each stage and for each patient, an individual adaptation of the therapeutic strategy to delay the anaplastic transformation (high-grade glioma), preserving or even improving the quality of life of patients. The preferred therapeutic strategy consists of surgery in awake mode, during which the neurosurgeon removes the maximum amount of the tumor while stimulating the awake patient, in order to preserve his cognitive abilities and ensure the best quality of life postoperative possible. The location of the tumor is an important factor in surgical management and its success.
This location is based on the analysis of the MRIs mentioned above. It leads to the definition of a tumor barycentre which will be the spatial variable studied here.
The objective of this work is to study the impact of tumor localization in order to help the surgeon in his practice and to better understand the link between the localization and the characteristics of the tumor.
Required work
The requested work will begin with a state of the art
- on the main characteristics of low-grade gliomas
- on spatial statistics tools.
It will continue with a descriptive analysis of the data.
This will be followed by the study of different classification methods on the basis of clinical data. This will involve identifying the most relevant method(s), the most discriminating factors, and groups of patients with common factors.
The master's work proposed here will take place in the context of a multidisciplinary collaboration with the CHRU of Nancy, particularly Dr Fabien Rech (neurosurgeon at the CHRU of Nancy and researcher at the CRAN). This collaboration concerns the treatment of low-grade diffuse gliomas (slow-moving brain tumours).
It will be based on a local database of 184 patients.
General description of the subject
Management of patients with low grade diffuse glioma should be based on regular multimodal MRI assessments (2-4 times per year, with a median duration of 10-15 years). This allows, at each stage and for each patient, an individual adaptation of the therapeutic strategy to delay the anaplastic transformation (high-grade glioma), preserving or even improving the quality of life of patients. The preferred therapeutic strategy consists of surgery in awake mode, during which the neurosurgeon removes the maximum amount of the tumor while stimulating the awake patient, in order to preserve his cognitive abilities and ensure the best quality of life postoperative possible. The location of the tumor is an important factor in surgical management and its success.
This location is based on the analysis of the MRIs mentioned above. It leads to the definition of a tumor barycentre which will be the spatial variable studied here.
The objective of this work is to study the impact of tumor localization in order to help the surgeon in his practice and to better understand the link between the localization and the characteristics of the tumor.
Required work
The requested work will begin with a state of the art
- on the main characteristics of low-grade gliomas
- on spatial statistics tools.
It will continue with a descriptive analysis of the data.
This will be followed by the study of different classification methods on the basis of clinical data. This will involve identifying the most relevant method(s), the most discriminating factors, and groups of patients with common factors.
Keywords:
spatial statistics, clinical database, low grade glioma
Department(s):
| Biology, Signals and Systems in Cancer and Neuroscience |
