The prognostic importance of traumatic axonal injury on early MRI: the Trondheim TAI-MRI grading and quantitative models.

OBJECTIVES

We analysed magnetic resonance imaging (MRI) findings after traumatic brain injury (TBI) aiming to improve the grading of traumatic axonal injury (TAI) to better reflect the outcome.

METHODS

Four-hundred sixty-three patients (8-70 years) with mild (n = 158), moderate (n = 129), or severe (n = 176) TBI and early MRI were prospectively included. TAI presence, numbers, and volumes at predefined locations were registered on fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging, and presence and numbers on T2*GRE/SWI. Presence and volumes of contusions were registered on FLAIR. We assessed the outcome with the Glasgow Outcome Scale Extended. Multivariable logistic and elastic-net regression analyses were performed.

RESULTS

The presence of TAI differed between mild (6%), moderate (70%), and severe TBI (95%). In severe TBI, bilateral TAI in mesencephalon or thalami and bilateral TAI in pons predicted worse outcomes and were defined as the worst grades (4 and 5, respectively) in the Trondheim TAI-MRI grading. The Trondheim TAI-MRI grading performed better than the standard TAI grading in severe TBI (pseudo-R 0.19 vs. 0.16). In moderate-severe TBI, quantitative models including both FLAIR volume of TAI and contusions performed best (pseudo-R 0.19-0.21). In patients with mild TBI or Glasgow Coma Scale (GCS) score 13, models with the volume of contusions performed best (pseudo-R 0.25-0.26).

CONCLUSIONS

We propose the Trondheim TAI-MRI grading (grades 1-5) with bilateral TAI in mesencephalon or thalami, and bilateral TAI in pons as the worst grades. The predictive value was highest for the quantitative models including FLAIR volume of TAI and contusions (GCS score <13) or FLAIR volume of contusions (GCS score ≥ 13), which emphasise artificial intelligence as a potentially important future tool.

CLINICAL RELEVANCE STATEMENT

The Trondheim TAI-MRI grading reflects patient outcomes better in severe TBI than today's standard TAI grading and can be implemented after external validation. The prognostic importance of volumetric models is promising for future use of artificial intelligence technologies.

KEY POINTS

Traumatic axonal injury (TAI) is an important injury type in all TBI severities. Studies demonstrating which MRI findings that can serve as future biomarkers are highly warranted. This study proposes the most optimal MRI models for predicting patient outcome at 6 months after TBI; one updated pragmatic model and a volumetric model. The Trondheim TAI-MRI grading, in severe TBI, reflects patient outcome better than today's standard grading of TAI and the prognostic importance of volumetric models in all severities of TBI is promising for future use of AI.