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Radiologic image‐based statistical shape analysis of brain tumours

Author

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  • Karthik Bharath
  • Sebastian Kurtek
  • Arvind Rao
  • Veerabhadran Baladandayuthapani

Abstract

We propose a curve‐based Riemannian geometric approach for general shape‐based statistical analyses of tumours obtained from radiologic images. A key component of the framework is a suitable metric that enables comparisons of tumour shapes, provides tools for computing descriptive statistics and implementing principal component analysis on the space of tumour shapes and allows for a rich class of continuous deformations of a tumour shape. The utility of the framework is illustrated through specific statistical tasks on a data set of radiologic images of patients diagnosed with glioblastoma multiforme, a malignant brain tumour with poor prognosis. In particular, our analysis discovers two patient clusters with very different survival, subtype and genomic characteristics. Furthermore, it is demonstrated that adding tumour shape information to survival models containing clinical and genomic variables results in a significant increase in predictive power.

Suggested Citation

  • Karthik Bharath & Sebastian Kurtek & Arvind Rao & Veerabhadran Baladandayuthapani, 2018. "Radiologic image‐based statistical shape analysis of brain tumours," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1357-1378, November.
    • Handle: RePEc:bla:jorssc:v:67:y:2018:i:5:p:1357-1378
    DOI: 10.1111/rssc.12272
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    References listed on IDEAS

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    1. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
    2. Sebastian Kurtek & Anuj Srivastava & Eric Klassen & Zhaohua Ding, 2012. "Statistical Modeling of Curves Using Shapes and Related Features," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1152-1165, September.
    3. Philip T. Reiss & R. Todd Ogden, 2010. "Functional Generalized Linear Models with Images as Predictors," Biometrics, The International Biometric Society, vol. 66(1), pages 61-69, March.
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    Cited by:

    1. Cho, Min Ho & Kurtek, Sebastian & Bharath, Karthik, 2022. "Tangent functional canonical correlation analysis for densities and shapes, with applications to multimodal imaging data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

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