TY - JOUR
T1 - Synergistic Role of Quantitative Diffusion Magnetic Resonance Imaging and Structural Magnetic Resonance Imaging in Predicting Outcomes after Traumatic Brain Injury
AU - Avesta, Arman
AU - Yendiki, Anastasia
AU - Perlbarg, Vincent
AU - Velly, Lionel
AU - Khalilzadeh, Omid
AU - Puybasset, Louis
AU - Galanaud, Damien
AU - Gupta, Rajiv
N1 - Funding Information:
This work was partially funded by a grant from the French Ministry of Health (Projet Hospitalier de Recherche Clinique #P051061).
Funding Information:
This research was also supported in part by the following grants: Air Force Contract Number FA8650-17-C-9113; Army USAMRAA Joint Warfighter Medical Research Program, contract number W81XWH-15-C-0052; and Congressionally Directed Medical Research Program W81XWH-13-2-0067.
Publisher Copyright:
© 2022 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - Objective This study aimed to assess if quantitative diffusion magnetic resonance imaging analysis would improve prognostication of individual patients with severe traumatic brain injury. Methods We analyzed images of 30 healthy controls to extract normal fractional anisotropy ranges along 18 white-matter tracts. Then, we analyzed images of 33 patients, compared their fractional anisotropy values with normal ranges extracted from controls, and computed severity of injury to white-matter tracts. We also asked 2 neuroradiologists to rate severity of injury to different brain regions on fluid-attenuated inversion recovery and susceptibility-weighted imaging. Finally, we built 3 models: (1) fed with neuroradiologists' ratings, (2) fed with white-matter injury measures, and (3) fed with both input types. Results The 3 models respectively predicted survival at 1 year with accuracies of 70%, 73%, and 88%. The accuracy with both input types was significantly better (P < 0.05). Conclusions Quantifying severity of injury to white-matter tracts complements qualitative imaging findings and improves outcome prediction in severe traumatic brain injury.
AB - Objective This study aimed to assess if quantitative diffusion magnetic resonance imaging analysis would improve prognostication of individual patients with severe traumatic brain injury. Methods We analyzed images of 30 healthy controls to extract normal fractional anisotropy ranges along 18 white-matter tracts. Then, we analyzed images of 33 patients, compared their fractional anisotropy values with normal ranges extracted from controls, and computed severity of injury to white-matter tracts. We also asked 2 neuroradiologists to rate severity of injury to different brain regions on fluid-attenuated inversion recovery and susceptibility-weighted imaging. Finally, we built 3 models: (1) fed with neuroradiologists' ratings, (2) fed with white-matter injury measures, and (3) fed with both input types. Results The 3 models respectively predicted survival at 1 year with accuracies of 70%, 73%, and 88%. The accuracy with both input types was significantly better (P < 0.05). Conclusions Quantifying severity of injury to white-matter tracts complements qualitative imaging findings and improves outcome prediction in severe traumatic brain injury.
KW - TBI
KW - TRACULA
KW - diffusion tensor imaging
KW - fractional anisotropy
KW - outcome prediction
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U2 - 10.1097/RCT.0000000000001284
DO - 10.1097/RCT.0000000000001284
M3 - Article
C2 - 35297580
AN - SCOPUS:85126781567
SN - 0363-8715
VL - 46
SP - 236
EP - 243
JO - Journal of Computer Assisted Tomography
JF - Journal of Computer Assisted Tomography
IS - 2
ER -