A Bayesian nonparametric model for textural pattern heterogeneity

Xiao Li, Michele Guindani, Chaan S. Ng, Brian Paul Hobbs

Research output: Contribution to journalArticlepeer-review


Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumour heterogeneity through patterns of enhancement, texture, morphology and shape. The prevailing technique for image texture analysis relies on the construction and synthesis of grey-level co-occurrence matrices (GLCM). Practice currently reduces the structured count data of a GLCM to reductive and redundant summary statistics for which analysis requires variable selection and multiple comparisons for each application, thus limiting reproducibility. In this article, we develop a Bayesian multivariate probabilistic framework for the analysis and unsupervised clustering of a sample of GLCM objects. By appropriately accounting for skewness and zero inflation of the observed counts and simultaneously adjusting for existing spatial autocorrelation at nearby cells, the methodology facilitates estimation of texture pattern distributions within the GLCM lattice itself. The techniques are applied to cluster images of adrenal lesions obtained from CT scans with and without administration of contrast. We further assess whether the resultant subtypes are clinically oriented by investigating their correspondence with pathological diagnoses. Additionally, we compare performance to a class of machine learning approaches currently used in cancer radiomics with simulation studies.

Original languageEnglish (US)
Pages (from-to)459-480
Number of pages22
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Issue number2
StatePublished - Mar 2021


  • Bayesian nonparametrics
  • cancer radiomics
  • grey-level co-occurrence matrix
  • multivariate count data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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