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September 2019 A Bayesian mark interaction model for analysis of tumor pathology images
Qiwei Li, Xinlei Wang, Faming Liang, Guanghua Xiao
Ann. Appl. Stat. 13(3): 1708-1732 (September 2019). DOI: 10.1214/19-AOAS1254

Abstract

With the advance of imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high resolution. Recent developments in deep-learning methods have enabled us to identify and classify individual cells from digital pathology images at large scale. Reliable statistical approaches to model the spatial pattern of cells can provide new insight into tumor progression and shed light on the biological mechanisms of cancer. We consider the problem of modeling spatial correlations among three commonly seen cells observed in tumor pathology images. A novel geostatistical marking model with interpretable underlying parameters is proposed in a Bayesian framework. We use auxiliary variable MCMC algorithms to sample from the posterior distribution with an intractable normalizing constant. We demonstrate how this model-based analysis can lead to sharper inferences than ordinary exploratory analyses, by means of application to three benchmark datasets and a case study on the pathology images of $188$ lung cancer patients. The case study shows that the spatial correlation between tumor and stromal cells predicts patient prognosis. This statistical methodology not only presents a new model for characterizing spatial correlations in a multitype spatial point pattern conditioning on the locations of the points, but also provides a new perspective for understanding the role of cell–cell interactions in cancer progression.

Citation

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Qiwei Li. Xinlei Wang. Faming Liang. Guanghua Xiao. "A Bayesian mark interaction model for analysis of tumor pathology images." Ann. Appl. Stat. 13 (3) 1708 - 1732, September 2019. https://doi.org/10.1214/19-AOAS1254

Information

Received: 1 April 2018; Revised: 1 March 2019; Published: September 2019
First available in Project Euclid: 17 October 2019

zbMATH: 07145973
MathSciNet: MR4019155
Digital Object Identifier: 10.1214/19-AOAS1254

Rights: Copyright © 2019 Institute of Mathematical Statistics

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Vol.13 • No. 3 • September 2019
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