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

A Visually Interpretable Histopathology-Based Immune Model Predicts T-effector Biology and Response to Immune checkpoint inhibition in Clear Cell Renal Cell Carcinoma Clinical Trial and Contemporary Real-World Datasets.

bioRxiv : the preprint server for biology·2026
Same author

Pediatric fibromuscular dysplasia presenting with acute right lower extremity limb ischemia requiring endovascular treatment and amputation - a case report.

BMC pediatrics·2026
Same author

p53 Is a Master Regulator of Proteostasis in SMARCB1-Deficient Malignant Rhabdoid Tumors.

Cancer cell·2026
Same author

Histological features of liver biopsy in patients with COVID-19: a single institution experience with long term follow-up outcome.

Diagnostic pathology·2026
Same author

Surveillance Alone After a Subtotal Resection of Disseminated Juvenile Xanthogranuloma.

Pediatric blood & cancer·2026
Same author

Radiologic, Pathologic, and Deep Learning Predictors of Response to Immune Checkpoint Blockade in Renal Cell Carcinoma Patients Undergoing Post-Treatment Nephrectomy.

medRxiv : the preprint server for health sciences·2025

Related Experiment Video

Updated: Jan 17, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

19.1K

MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology.

Aleksandra Weronika Nielsen1, Hafez Eslami Manoochehri1,2, Hua Zhong1,3

  • 1Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Genome Medicine
|September 19, 2025
PubMed
Summary

MorphoITH quantifies tumor cell diversity from pathology slides, offering a scalable way to understand cancer evolution and intra-tumor heterogeneity (ITH). This method mirrors genetic changes, paving the way for precision oncology.

Keywords:
Artificial intelligenceCcRCCDeep learningDigital pathologyHistopathologyIntra-tumor heterogeneityKidney cancerTumor evolutionTumor morphology

More Related Videos

Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach
07:16

Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach

Published on: April 25, 2025

758
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.5K

Related Experiment Videos

Last Updated: Jan 17, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

19.1K
Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach
07:16

Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach

Published on: April 25, 2025

758
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.5K

Area of Science:

  • Oncology
  • Computational Pathology
  • Genomics

Background:

  • Tumor evolution and intra-tumor heterogeneity (ITH) drive cancer aggressiveness.
  • Multi-regional sequencing is costly and limited in scalability for studying ITH.
  • A need exists for accessible methods to analyze tumor evolution.

Purpose of the Study:

  • To develop a novel framework, MorphoITH, for inferring molecular ITH from histopathology slides.
  • To quantify phenotypic diversity as a proxy for genetic ITH.
  • To enable scalable analysis of tumor evolution in clinical settings.

Main Methods:

  • Developed MorphoITH, a framework integrating self-supervised deep learning for phenotypic variation analysis.
  • Quantified morphological diversity across cytology, architecture, and microenvironment.
  • Employed rigorous methods to eliminate spurious sources of variation.

Main Results:

  • MorphoITH successfully captured clinically significant features in clear cell renal cell carcinoma (ccRCC), including vascular architecture and nuclear grade.
  • Identified morphological changes linked to subclonal alterations in driver genes (BAP1, PBRM1, SETD2).
  • MorphoITH's morphological trajectories correlated with underlying genetic evolution patterns in multi-regional sequencing data.

Conclusions:

  • MorphoITH offers a scalable and rigorous method for quantifying morphological ITH.
  • MorphoITH serves as a potential proxy for genetic ITH and tumor evolution.
  • This approach links histopathology with genomic insights for enhanced phenotypic profiling in precision oncology.