Clinical performance of a multiparametric MRI-based post concussive syndrome index
Steven P Meyers1, Adnan Hirad2, Patricia Gonzalez3
1Department of Imaging Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States.
Introduction:
Diffusion Tensor Imaging (DTI) has revealed measurable changes in the brains of patients with persistent post-concussive syndrome (PCS). Because of inconsistent results in univariate DTI metrics among patients with mild traumatic brain injury (mTBI), there is currently no single objective and reliable MRI index for clinical decision-making in patients with PCS.
Purpose:
This study aimed to evaluate the performance of a newly developed PCS Index (PCSI) derived from machine learning of multiparametric magnetic resonance imaging (MRI) data to classify and differentiate subjects with mTBI and PCS history from those without a history of mTBI.
Materials And Methods:
Data were retrospectively extracted from 139 patients aged between 18 and 60 years with PCS who underwent MRI examinations at 2 weeks to 1-year post-mTBI, as well as from 336 subjects without a history of head trauma. The performance of the PCS Index was assessed by comparing 69 patients with a clinical diagnosis of PCS with 264 control subjects. The PCSI values for patients with PCS were compared based on the mechanism of injury, time interval from injury to MRI examination, sex, history of prior concussion, loss of consciousness, and reported symptoms.
Results:
Injured patients had a mean PCSI value of 0.57, compared to the control group, which had a mean PCSI value of 0.12 (p = 8.42e-23) with accuracy of 88%, sensitivity of 64%, and specificity of 95%, respectively. No statistically significant differences were found in the PCSI values when comparing the mechanism of injury, sex, or loss of consciousness.
Conclusion:
The PCSI for individuals aged between 18 and 60 years was able to accurately identify patients with post-concussive injuries from 2 weeks to 1-year post-mTBI and differentiate them from the controls. The results of this study suggest that multiparametric MRI-based PCSI has great potential as an objective clinical tool to support the diagnosis, treatment, and follow-up care of patients with post-concussive syndrome. Further research is required to investigate the replicability of this method using other types of clinical MRI scanners.
Insights
A new Post-Concussive Syndrome Index (PCSI) using multiparametric MRI accurately identifies patients with persistent post-concussive syndrome (PCS) after mild traumatic brain injury (mTBI). This tool shows promise for objective diagnosis and management of PCS.
Area of Science:
- Neuroimaging
- Radiology
- Neurology
Background:
- Persistent post-concussive syndrome (PCS) presents challenges in diagnosis due to inconsistent Diffusion Tensor Imaging (DTI) findings.
- There is a need for a reliable MRI index for clinical decision-making in mild traumatic brain injury (mTBI) patients with PCS.
Purpose of the Study:
- To evaluate a novel Post-Concussive Syndrome Index (PCSI) developed using machine learning of multiparametric MRI data.
- To assess the PCSI's ability to classify and differentiate individuals with a history of mTBI and PCS from healthy controls.
Main Methods:
- Retrospective analysis of MRI data from 139 patients with PCS (2 weeks to 1 year post-mTBI) and 336 controls.
- Assessment of PCSI performance by comparing 69 PCS patients against 264 controls.
- Analysis of PCSI values based on injury mechanism, time to MRI, sex, prior concussion, loss of consciousness, and symptoms.
Main Results:
- The PCSI demonstrated high accuracy (88%), sensitivity (64%), and specificity (95%) in differentiating PCS patients from controls.
- PCS patients had a significantly higher mean PCSI (0.57) compared to controls (0.12) (p=8.42e-23).
- No significant differences in PCSI were observed based on injury mechanism, sex, or loss of consciousness.
Conclusions:
- The multiparametric MRI-based PCSI effectively identifies individuals with PCS within 1 year post-mTBI.
- PCSI shows potential as an objective clinical tool for PCS diagnosis, treatment, and follow-up.
- Further research is needed to validate the PCSI's replicability across different MRI scanners.
