CALD is an inflammatory demyelinating disease that causes severe motor and cognitive deficit leading to rapid death. Hematopoietic stem cell transplantation (HSCT) can halt neuroinflammation in CALD through the replacement of microglia (i.e., brain immune system) but only if performed during its early phase. Using standard brain MRI, it is estimated that only 30% of adult CALD patients are identified. Complex and lengthy clinical evaluations together with MRI reading from experts improve CALD detection but are not available in routine clinical practice. Diffusion tensor imaging is a quantitative microstructural technique that can identify neuroinflammation at a very early stage. Still, its implementation in clinical practice has been very limited due to high inter-center measurements variability and bias due to data quality issues. The approach we will use solves these problems by introducing an automatic calibration and standardization with systematic quality control enabling the use of all MRI scanners in clinical settings. The innovative aspect of this project lies on the validation of an expert-independent prognosis biomarker able to specifically identify patients at high-risk to convert to CALD so that treatment can be initiated at the early stage of neuroinflammation. We aim to demonstrate that this tool has at least a 2-fold sensitivity compared to the current standard of care.
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Sensitivity of RD-index19 change of at least 1 point between two consecutive annual measurements to diagnose CALD before or at the same time than the reference diagnostic made by an expert committee
Timeframe: 1 year, 2 years, 3 years
Specificity of RD-index19 change of at least 1 point between two consecutive annual measurements with repect to CALD conversion before or at the same time than the reference diagnostic made by an expert committee
Timeframe: 1 year, 2 years, 3 years