Bio-inspired computation approach for tumor growth with spatial randomness analysis of kidney cancer xenograft pathology slides

Aydin Saribudak, Yiyu Dong, James Hsieh, M. Ümit Uyar

Research output: Contribution to journalConference article

4 Scopus citations

Abstract

In this paper, we analyze digitized images of Hematoxylin-Eosin (H&E) slides equipped with tumorous tissues from patient derived xenograft models to build our bio-inspired computation method, namely Personalized Relevance Parameterization of Spatial Randomness (PReP-SR). Applying spatial pattern analysis techniques of quadrat counts, kernel estimation and nearest neighbor functions to the images of the H&E samples, slide-specific features are extracted to examine the hypothesis that existence of dependency of nuclei positions possesses information of individual tumor characteristics. These features are then used as inputs to PReP-SR to compute tumor growth parameters for exponential-linear model. Differential evolution algorithms are developed for tumor growth parameter computations, where a candidate vector in a population consists of size selection indices for spatial evaluation and weight coefficients for spatial features and their correlations. Using leave-one-out-crossvalidation method, we showed that, for a set of H&E slides from kidney cancer patient derived xenograft models, PReP-SR generates personalized model parameters with an average error rate of 13:58%. The promising results indicate that bio-inspired computation techniques may be useful to construct mathematical models with patient specific growth parameters in clinical systems.

Keywords

  • Artificial intelligence
  • Bio-inspired computation
  • Differential evolution
  • Exponential linear tumor growth model
  • H&E slide
  • Kidney cancer
  • Pathology
  • Spatial pattern analysis

Fingerprint Dive into the research topics of 'Bio-inspired computation approach for tumor growth with spatial randomness analysis of kidney cancer xenograft pathology slides'. Together they form a unique fingerprint.

  • Cite this