A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain

@inproceedings{GrechSollars2016ABS,
  title={A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain},
  author={Grech-Sollars},
  year={2016}
}
  • Grech-Sollars
  • Published 2016
The focus of this study is the development of a statistical modelling procedure for characterising intra-tumour heterogeneity, motivated by recent clinical literature indicating that a variety of tumours exhibit a considerable degree of genetic spatial variability. A formal spatial statistical model has been developed and used to characterise the structural heterogeneity of a number of supratentorial primitive neuroectodermal tumours (PNETs), based on diffusion-weighted magnetic resonance… CONTINUE READING
0 Citations
42 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 42 references

Dataset 1 in : A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain Monte Carlo ( MCMC ) simulation

  • King, M Grech-Sollars
  • 2016

Markov chain Monte Carlo random effects modeling in magnetic resonance image processing using the BRugs interface to WinBUGS

  • King, F Calamante, CA Clark
  • J Stat Softw
  • 2011

Functional principal component analyses of biomedical images as outcome measures

  • E O’Connor, N Fieller, A Holmes
  • J R Stat Soc Ser C Appl Stat
  • 2010

Similar Papers

Loading similar papers…