A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images.
Digital pathology imaging of tumor tissues, which captures histological details in high resolution, is fast becoming a routine clinical procedure. Recent developments in deep-learning methods have enabled the identification, characterization, and classification of individual cells from pathology ima...
| Publicado en: | Biostatistics Vol. 20; no. 4; pp. 565 - 582 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Oct2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139212418&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139212418 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14654644 N58 jtl: Biostatistics issn: 14654644 maglogo: N pubinfo: dt: Oct2019 vid: 20 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 139212418 139212418 NLM29788035 139212418 10.1093/biostatistics/kxy019 NLM29788035 139212418 ppf: 565 ppct: 17 formats: tig: atl: A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images. aug: au: Li, Qiwei Wang, Xinlei Liang, Faming Yi, Faliu Xie, Yang Gazdar, Adi Xiao, Guanghua affil: Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, USA sug: subj: Lung Neoplasms Image Interpretation, Computer Assisted Models, Statistical Lung Neoplasms Pathology Algorithms Probability Human Systems Analysis Validation Studies Comparative Studies Evaluation Research Multicenter Studies Clinical Assessment Tools Scales ab: Digital pathology imaging of tumor tissues, which captures histological details in high resolution, is fast becoming a routine clinical procedure. Recent developments in deep-learning methods have enabled the identification, characterization, and classification of individual cells from pathology images analysis at a large scale. This creates new opportunities to study the spatial patterns of and interactions among different types of cells. Reliable statistical approaches to modeling such spatial patterns and interactions can provide insight into tumor progression and shed light on the biological mechanisms of cancer. In this article, we consider the problem of modeling a pathology image with irregular locations of three different types of cells: lymphocyte, stromal, and tumor cells. We propose a novel Bayesian hierarchical model, which incorporates a hidden Potts model to project the irregularly distributed cells to a square lattice and a Markov random field prior model to identify regions in a heterogeneous pathology image. The model allows us to quantify the interactions between different types of cells, some of which are clinically meaningful. We use Markov chain Monte Carlo sampling techniques, combined with a double Metropolis-Hastings algorithm, in order to simulate samples approximately from a distribution with an intractable normalizing constant. The proposed model was applied to the pathology images of $205$ lung cancer patients from the National Lung Screening trial, and the results show that the interaction strength between tumor and stromal cells predicts patient prognosis (P = $0.005$). This statistical methodology provides a new perspective for understanding the role of cell-cell interactions in cancer progression. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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