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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Quantitative probabilistic functional diffusion mapping in newly diagnosed glioblastoma treated with
Benjamin M Ellingson1, Timothy F Cloughesy, Albert Lai
1Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Ste. 615, Los Angeles, CA 90024, USA. bellingson@mednet.ucla.edu
Neuro-Oncology
|January 1, 2013
Summary
Probabilistic functional diffusion mapping (fDM) improves prediction of survival outcomes in glioblastoma patients. This novel approach enhances the accuracy of assessing treatment response using apparent diffusion coefficients (ADC) compared to traditional methods.
Area of Science:
- Radiology and Imaging
- Oncology
- Medical Physics
Background:
- Functional diffusion mapping (fDM) assesses cancer treatment response using voxel-wise apparent diffusion coefficient (ADC) changes.
- Image registration uncertainty limits clinical adoption of traditional fDM.
- A probabilistic approach to fDM is introduced to enhance quantification and overcome limitations.
Purpose of the Study:
- To introduce and evaluate a probabilistic functional diffusion mapping (fDM) technique.
- To compare the predictive performance of probabilistic fDM against traditional fDM for survival outcomes.
- To assess the utility of probabilistic fDM in newly diagnosed glioblastoma patients undergoing radiochemotherapy.
Main Methods:
- Retrospective analysis of 143 newly diagnosed glioblastoma patients.
- Calculation of traditional and probabilistic fDMs using pre- and post-therapy ADC maps.
- Probabilistic fDM involved applying perturbations to ADC maps to generate voxel-wise probabilities of classification.
- Comparison of fDM methods in predicting progression-free survival (PFS) and overall survival (OS).
Main Results:
- Probabilistic fDM identified correlations between ADC changes and survival outcomes in glioblastoma.
- Patients with decreasing ADC showed shorter PFS and OS; increasing ADC predicted longer survival.
- Probabilistic fDM demonstrated superior prediction accuracy for 12-month PFS and 24-month OS compared to traditional fDM.
- Kaplan-Meier analysis confirmed probabilistic fDM's better patient stratification for PFS and OS.
Conclusions:
- Probabilistic fDM serves as a more accurate biomarker for predicting 12-month PFS and 24-month OS in glioblastoma.
- The probabilistic approach enhances the clinical utility of fDM in cancer treatment response assessment.
