Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images.

GigaScience·2026
Same author

MicNet: integrating spatially resolved transcriptomes and pathology images by contrastive deep neural network.

Genome biology·2026
Same author

Computational identification of migrating T cells in spatial transcriptomics data.

JCI insight·2026
Same author

BiGER: Bayesian rank aggregation in genomics with extended ranking schemes.

Nature communications·2026
Same author

SpaFun: discovering domain-specific spatial expression patterns and new disease-relevant genes using functional principal component analysis.

Briefings in bioinformatics·2026
Same author

Evaluation of soybean sprouting growth vigor based on ZnONPs.

Frontiers in plant science·2026
Same journal

Development and Validation of a Six-Gene Signature of Myeloid Antigen Presentation Dysfunction Based on Single-Cell and Multi-Cohort Transcriptomics for Predicting Prognosis and Recurrence of Hepatocellular Carcinoma.

Cancer informatics·2026
Same journal

Widening Health Inequality and Causal Metabolic Drivers in Global Colorectal Cancer: A Multi-Dimensional Study.

Cancer informatics·2026
Same journal

GFAP-Dependent Transcriptional Dynamics and Cellular Heterogeneity in Primary, Recurrent, and Grade III Gliomas.

Cancer informatics·2026
Same journal

Translating Data Into Clinical Tools: An Integrative Strategy for Precision Biomarker Identification in Soft Tissue Sarcoma Diagnosis and Prognosis.

Cancer informatics·2026
Same journal

The MAPK Pathway Coordinates an Immunosuppressive Microenvironment in Colorectal Cancer: A Single-Cell Guided Prognostic Model.

Cancer informatics·2026
Same journal

Multi-Scale Cross-Attention Multiple Instance Learning Network for Automated Classification of Colorectal Polyps.

Cancer informatics·2026
See all related articles

Related Experiment Video

Updated: Feb 28, 2026

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells
08:54

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Published on: May 20, 2020

10.1K

Lung Cancer Pathological Image Analysis Using a Hidden Potts Model.

Qianyun Li1, Faliu Yi2, Tao Wang3

  • 1Department of Biostatistics, University of Florida, Gainesville, FL, USA.

Cancer Informatics
|June 16, 2017
PubMed
Summary
This summary is machine-generated.

Researchers analyzed lung cancer patient images to understand cell spatial distribution and survival. Lymphocyte-tumor cell interaction significantly predicts patient survival time.

Keywords:
Potts modeldouble Metropolis-Hastingsintractable normalizing constantsurvival analysis

More Related Videos

Practical Considerations in Studying Metastatic Lung Colonization in Osteosarcoma Using the Pulmonary Metastasis Assay
07:44

Practical Considerations in Studying Metastatic Lung Colonization in Osteosarcoma Using the Pulmonary Metastasis Assay

Published on: March 12, 2018

10.7K
Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer
06:51

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

Published on: July 21, 2018

18.8K

Related Experiment Videos

Last Updated: Feb 28, 2026

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells
08:54

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Published on: May 20, 2020

10.1K
Practical Considerations in Studying Metastatic Lung Colonization in Osteosarcoma Using the Pulmonary Metastasis Assay
07:44

Practical Considerations in Studying Metastatic Lung Colonization in Osteosarcoma Using the Pulmonary Metastasis Assay

Published on: March 12, 2018

10.7K
Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer
06:51

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

Published on: July 21, 2018

18.8K

Area of Science:

  • Computational biology
  • Pathology
  • Biostatistics

Background:

  • Biological data acquisition increasingly relies on imaging techniques.
  • Understanding cellular spatial distribution in tumors is crucial for prognosis.
  • Lung cancer survival prediction requires accurate analysis of pathological images.

Purpose of the Study:

  • To investigate the relationship between cell spatial distribution and survival time in lung cancer patients.
  • To model the spatial interactions of lymphocytes, stroma, and tumor cells.
  • To develop a predictive model for lung cancer survival using spatial and cell count data.

Main Methods:

  • Analysis of pathological images from 205 lung cancer patients.
  • Application of a modified Potts model to represent cell type interactions.
  • Parameter estimation using the double Metropolis-Hastings algorithm for intractable distributions.

Main Results:

  • Significant association found between spatial interaction of lymphocyte and tumor cells and patient survival.
  • The modified Potts model effectively captures cell type spatial dynamics.
  • The double Metropolis-Hastings algorithm successfully estimated model parameters.

Conclusions:

  • Spatial interaction patterns, particularly between lymphocytes and tumor cells, are key indicators of lung cancer prognosis.
  • Integrating spatial cell distribution with cell counts enhances survival prediction accuracy.
  • This approach offers a novel method for improving lung cancer patient outcome assessment.