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

Cancer Survival Analysis01:21

Cancer Survival Analysis

759
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
759
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.8K
VSEPR Theory for Determination of Electron Pair Geometries
45.8K
Survival Curves01:18

Survival Curves

710
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
710
Survival Tree01:19

Survival Tree

423
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
423
Design Example: Designing Water Slide01:18

Design Example: Designing Water Slide

625
When designing a water slide, controlling the speed of water flow is crucial for rider safety while maintaining an exciting experience. As water flows down the slide, gravity causes it to accelerate, with its speed at the bottom depending on the height from which it starts. The higher the slide, the more potential energy the water has at the top, which is converted into kinetic energy as it descends, increasing its speed.
Bernoulli's principle determines the water's velocity along the slide....
625
Stomach Histology01:26

Stomach Histology

3.2K
The stomach comprises several layers that work together to facilitate digestion and protect the organ. The outermost layer is called the serosa, which provides support and protection to the stomach. The muscularis externa layer is responsible for the mechanical breakdown of food by contracting and moving the stomach. The submucosa layer, located beneath the muscularis externa, contains connective tissue, blood vessels, nerves, and glands that secrete mucus and other substances essential for...
3.2K

You might also read

Related Articles

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

Sort by
Same authorSame journal

How to benchmark medical AI agents.

PLoS medicine·2026
Same author

AI-based selection of tumor regions for genomic profiling in neuropathology.

Neuro-oncology advances·2026
Same author

Clinical decision support in hematological malignancies using a case-grounded AI agent.

Nature medicine·2026
Same author

A deep learning framework for efficient pathology image analysis.

Nature communications·2026
Same author

Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.

JMIR mental health·2026
Same author

Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.

Cancer research·2026

Related Experiment Video

Updated: Jan 30, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

908

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.

Jakob Nikolas Kather1,2,3,4, Johannes Krisam5, Pornpimol Charoentong1,3

  • 1Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany.

Plos Medicine
|January 25, 2019
PubMed
Summary

Deep convolutional neural networks (CNNs) can extract prognostic biomarkers from routine colorectal cancer (CRC) histopathology images. This novel approach identifies a "deep stroma score" that predicts patient survival, offering a new tool for cancer prognosis.

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

733
Portal Vein Injection of Colorectal Cancer Organoids to Study the Liver Metastasis Stroma
07:59

Portal Vein Injection of Colorectal Cancer Organoids to Study the Liver Metastasis Stroma

Published on: September 3, 2021

7.1K

Related Experiment Videos

Last Updated: Jan 30, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

908
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

733
Portal Vein Injection of Colorectal Cancer Organoids to Study the Liver Metastasis Stroma
07:59

Portal Vein Injection of Colorectal Cancer Organoids to Study the Liver Metastasis Stroma

Published on: September 3, 2021

7.1K

Area of Science:

  • Computational pathology
  • Digital pathology
  • Oncology

Background:

  • Hematoxylin-eosin (HE)-stained slides are standard for colorectal cancer (CRC) patients.
  • Quantitative prognostic biomarkers are not routinely extracted from these slides.
  • Investigating deep convolutional neural networks (CNNs) for prognostic biomarker extraction from HE images.

Purpose of the Study:

  • To determine if CNNs can extract prognosticators directly from HE-stained CRC tissue slides.
  • To develop and validate a novel prognostic biomarker using AI-driven image analysis.

Main Methods:

  • Trained a CNN on over 100,000 HE image patches from 86 CRC slides.
  • Performed automated tissue decomposition on 862 HE slides from The Cancer Genome Atlas (TCGA) cohort.
  • Calculated a "deep stroma score" based on CNN neuron activations.
  • Validated the score in an independent cohort of 409 CRC patients (DACHS study).

Main Results:

  • CNN achieved >94% accuracy in classifying nine-class HE image patches.
  • The "deep stroma score" was an independent prognostic factor for overall survival (OS) in both TCGA and DACHS cohorts.
  • The score also predicted CRC-specific OS and relapse-free survival (RFS) in the DACHS cohort.
  • Manual quantification and gene expression signatures were less consistently prognostic across tumor stages.

Conclusions:

  • CNNs can assess the tumor microenvironment from histopathological images.
  • The developed "deep stroma score" shows potential as a prognostic biomarker for colorectal cancer.
  • Prospective validation is needed for clinical implementation.