Clinical performance of a multiparametric MRI-based post concussive syndrome index

Steven P Meyers; Adnan Hirad; Patricia Gonzalez; Jeffrey J Bazarian; Mark H Mirabelli; Katherine H Rizzone; Heather M Ma; Peter Rosella; Saara Totterman; Edward Schreyer; Jose G Tamez-Pena

doi:10.3389/fneur.2023.1282833

Clinical performance of a multiparametric MRI-based post concussive syndrome index

Front Neurol. 2023 Dec 18:14:1282833. doi: 10.3389/fneur.2023.1282833. eCollection 2023.

Authors

Affiliations

¹ Department of Imaging Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States.
² Department of Vascular Surgery, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States.
³ Qmetrics Technologies, Pittsford, NY, United States.
⁴ Departments of Emergency Medicine, Neurology, Neurosurgery, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States.
⁵ Department of Orthopedics, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States.
⁶ Department of Physical Medicine and Rehabilitation, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States.
⁷ School of Medicine and Health Sciences, Tecnologico de Monterey, Monterrey, Mexico.

Abstract

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.

Keywords: MRI; MTBI; diffusion imaging; post-concussive syndrome; radiomics.

Grants and funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.