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Optimization of MRI-based scoring scales of brain injury severity in children with unilateral cerebral palsy

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Abstract

Background

Several scoring systems for measuring brain injury severity have been developed to standardize the classification of MRI results, which allows for the prediction of functional outcomes to help plan effective interventions for children with cerebral palsy.

Objective

The aim of this study is to use statistical techniques to optimize the clinical utility of a recently proposed template-based scoring method by weighting individual anatomical scores of injury, while maintaining its simplicity by retaining only a subset of scored anatomical regions.

Materials and methods

Seventy-six children with unilateral cerebral palsy were evaluated in terms of upper limb motor function using the Assisting Hand Assessment measure and injuries visible on MRI using a semiquantitative approach. This cohort included 52 children with periventricular white matter injury and 24 with cortical and deep gray matter injuries. A subset of the template-derived cerebral regions was selected using a data-driven region selection algorithm. Linear regression was performed using this subset, with interaction effects excluded.

Results

Linear regression improved multiple correlations between MRI-based and Assisting Hand Assessment scores for both periventricular white matter (R squared increased to 0.45 from 0, P < 0.0001) and cortical and deep gray matter (0.84 from 0.44, P < 0.0001) cohorts. In both cohorts, the data-driven approach retained fewer than 8 of the 40 template-derived anatomical regions.

Conclusion

The equal or better prediction of the clinically meaningful Assisting Hand Assessment measure using fewer anatomical regions highlights the potential of these developments to enable enhanced quantification of injury and prediction of patient motor outcome, while maintaining the clinical expediency of the scoring approach.

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Acknowledgments

Alex Pagnozzi is supported by the Australian Postgraduate Award (APA) from the University of Queensland and a stipend from the Commonwealth Scientific Industrial and Research Organisation (CSIRO). Roslyn Boyd is supported by a NHMRC Career Development Fellowship (1037220) and a NHMRC Project Grant COMBIT (1003887). No other authors have potential conflicts of interest to declare.

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Authors and Affiliations

  1. CSIRO Digital Productivity and Services Flagship, The Australian e-Health Research Centre, Royal Brisbane and Women’s Hospital, Level 5, UQ Health Sciences Building, Herston, QLD, 4029, Australia

    Alex M. Pagnozzi, James Doecke, Stephen Rose & Nicholas Dowson

  2. School of Medicine, The University of Queensland, St. Lucia, Brisbane, Australia

    Alex M. Pagnozzi

  3. Stella Maris Scientific Institute, Pisa, Italy

    Simona Fiori & Andrea Guzzetta

  4. Queensland Cerebral Palsy and Rehabilitation Research Centre, School of Medicine, The University of Queensland, Brisbane, Australia

    Roslyn N. Boyd

  5. Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy

    Andrea Guzzetta

  6. Centre for Medical Diagnostic Technologies in Queensland, The University of Queensland, St. Lucia, Brisbane, Australia

    Yaniv Gal

Authors
  1. Alex M. Pagnozzi
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  2. Simona Fiori
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  3. Roslyn N. Boyd
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Correspondence to Alex M. Pagnozzi.

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Pagnozzi, A.M., Fiori, S., Boyd, R.N. et al. Optimization of MRI-based scoring scales of brain injury severity in children with unilateral cerebral palsy. Pediatr Radiol 46, 270–279 (2016). https://doi.org/10.1007/s00247-015-3473-y

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  • DOI: https://doi.org/10.1007/s00247-015-3473-y

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