Lung cancer pathological image analysis using a hidden potts model

Qianyun Li, Faliu Yi, Tao Wang, Guanghua Xiao, Faming Liang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Nowadays, many biological data are acquired via images. In this article, we study the pathological images scanned from 205 patients with lung cancer with the goal to find out the relationship between the survival time and the spatial distribution of different types of cells, including lymphocyte, stroma, and tumor cells. Toward this goal, we model the spatial distribution of different types of cells using a modified Potts model for which the parameters represent interactions between different types of cells and estimate the parameters of the Potts model using the double Metropolis-Hastings algorithm. The double Metropolis-Hastings algorithm allows us to simulate samples approximately from a distribution with an intractable normalizing constant. Our numerical results indicate that the spatial interaction between the lymphocyte and tumor cells is significantly associated with the patient’s survival time, and it can be used together with the cell count information to predict the survival of the patients.

Original languageEnglish (US)
JournalCancer Informatics
StatePublished - 2017


  • Double Metropolis-Hastings
  • Intractable normalizing constant
  • Potts model
  • Survival analysis

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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