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A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images
Qiwei Li1, Xinlei Wang2, Faming Liang3
1Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, USA.
Biostatistics (Oxford, England)
|May 23, 2018
Summary
A new Bayesian model analyzes cell interactions in digital pathology images. Stronger tumor and stromal cell interactions predict better lung cancer patient prognosis, offering insights into cancer progression.
Area of Science:
- Computational pathology
- Statistical modeling
- Cancer biology
Background:
- Digital pathology enables high-resolution imaging of tumor tissues, facilitating large-scale cell analysis.
- Deep learning advances cell identification and classification, opening avenues for studying spatial cell patterns and interactions.
- Understanding cell-cell interactions is crucial for insights into tumor progression and cancer mechanisms.
Purpose of the Study:
- To develop a novel Bayesian hierarchical model for analyzing spatial patterns and interactions of lymphocytes, stromal cells, and tumor cells in digital pathology images.
- To quantify clinically meaningful interactions between different cell types within heterogeneous tumor microenvironments.
- To investigate the prognostic value of cell-cell interaction strengths in lung cancer.
Main Methods:
- Proposed a Bayesian hierarchical model incorporating a hidden Potts model for cell projection onto a lattice and a Markov random field prior for region identification.
- Employed Markov chain Monte Carlo sampling with a double Metropolis-Hastings algorithm to handle intractable normalizing constants.
- Applied the model to digital pathology images from 205 lung cancer patients in the National Lung Screening trial.
Main Results:
- The model successfully quantified interactions between lymphocytes, stromal cells, and tumor cells in pathology images.
- A significant correlation was found between the interaction strength of tumor and stromal cells and patient prognosis (P = 0.005).
- The identified cell-cell interactions provide a new perspective on cancer progression.
Conclusions:
- The developed statistical methodology offers a novel approach to understanding cell-cell interactions in cancer.
- Quantifying spatial interactions between tumor and stromal cells can serve as a predictive biomarker for lung cancer prognosis.
- This approach enhances the utility of digital pathology in uncovering biological mechanisms of cancer.
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