Share Email Print
cover

Proceedings Paper

A Bayesian framework for early risk prediction in traumatic brain injury
Author(s): Shikha Chaganti; Andrew J. Plassard; Laura Wilson; Miya A. Smith; Mayur B. Patel; Bennett A. Landman
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Early detection of risk is critical in determining the course of treatment in traumatic brain injury (TBI). Computed tomography (CT) acquired at admission has shown latent prognostic value in prior studies; however, no robust clinical risk predictions have been achieved based on the imaging data in large-scale TBI analysis. The major challenge lies in the lack of consistent and complete medical records for patients, and an inherent bias associated with the limited number of patients samples with high-risk outcomes in available TBI datasets. Herein, we propose a Bayesian framework with mutual information-based forward feature selection to handle this type of data. Using multi-atlas segmentation, 154 image-based features (capturing intensity, volume and texture) were computed over 22 ROIs in 1791 CT scans. These features were combined with 14 clinical parameters and converted into risk likelihood scores using Bayes modeling. We explore the prediction power of the image features versus the clinical measures for various risk outcomes. The imaging data alone were more predictive of outcomes than the clinical data (including Marshall CT classification) for discharge disposition with an area under the curve of 0.81 vs. 0.67, but less predictive than clinical data for discharge Glasgow Coma Scale (GCS) score with an area under the curve of 0.65 vs. 0.85. However, in both cases, combining imaging and clinical data increased the combined area under the curve with 0.86 for discharge disposition and 0.88 for discharge GCS score. In conclusion, CT data have meaningful prognostic value for TBI patients beyond what is captured in clinical measures and the Marshall CT classification.

Paper Details

Date Published: 21 March 2016
PDF: 8 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978422 (21 March 2016); doi: 10.1117/12.2217306
Show Author Affiliations
Shikha Chaganti, Vanderbilt Univ. (United States)
Andrew J. Plassard, Vanderbilt Univ. (United States)
Laura Wilson, Vanderbilt Univ. Medical Ctr. (United States)
Miya A. Smith, Vanderbilt Univ. Medical Ctr. (United States)
Mayur B. Patel, Vanderbilt Univ. Medical Ctr. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

© SPIE. Terms of Use
Back to Top