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

Genomic analysis of <i>Botrytis cinerea</i> causing postharvest strawberry rot and the control effect of pydiflumetofen.

Frontiers in microbiology·2026
Same author

Cardiovascular Disease Subtypes and Alzheimer's Disease: Phenotypic and Genetic Associations in the UK Biobank and All of Us Research Program.

Journal of the American Heart Association·2026
Same author

Nonlinear Modeling Reveals Novel Associations Between Genetically Predicted Protein Levels and Pancreatic Cancer Risk.

Molecular carcinogenesis·2026
Same author

Decoding cellular communication networks and signaling pathways in bone, skeletal muscle, and bone-muscle crosstalk through spatial transcriptomics in a young male mouse.

Bone research·2026
Same author

The impact of trust in AI on digital innovation examining the moderation of intellectual capital and task characteristics.

Scientific reports·2026
Same author

A predictive 14-gene signature and FLI1 inhibition overcome tumor-infiltrating lymphocyte dysfunction in triple-negative breast cancer.

Biochemical and biophysical research communications·2026
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
See all related articles
  1. Home
  3. Uncovering Hidden Prognostic Patterns In Colorectal Cancer Histology Using Unsupervised Learning: A Computational Pathology Study.
  1. Home
  3. Uncovering Hidden Prognostic Patterns In Colorectal Cancer Histology Using Unsupervised Learning: A Computational Pathology Study.

Related Experiment Video

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

853

Uncovering Hidden Prognostic Patterns in Colorectal Cancer Histology Using Unsupervised Learning: A Computational

Wen-Tong Zhou1, Yong Liu1, Gang Yu2

  • 1Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410031, China.

Bioengineering (Basel, Switzerland)
|March 28, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study used deep learning to find new visual patterns in colorectal cancer (CRC) tissues. These patterns, combined with clinical data, improve the prediction of patient survival and aid in treatment decisions.

Keywords:
colorectal cancerhistomorphological patternspathological imagespatient prognosisunsupervised learning

More Related Videos

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

1.0K
Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing
15:17

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

Published on: September 25, 2011

14.5K

Related Experiment 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

853
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

1.0K
Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing
15:17

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

Published on: September 25, 2011

14.5K

Area of Science:

  • Computational pathology
  • Oncology
  • Artificial intelligence in medicine

Background:

  • Colorectal cancer (CRC) is a major global health concern with limited diagnostic features from current histopathology.
  • There's a need for advanced methods to identify subtle prognostic indicators in CRC tissues.

Purpose of the Study:

  • To discover prognostically significant histomorphological patterns in CRC using unsupervised deep learning.
  • To develop and validate a prognostic risk model integrating novel morphological features with clinical data.

Main Methods:

  • Developed a framework combining convolutional neural networks and deep clustering on 23,341 CRC tissue image patches.
  • Identified 30 distinct histomorphological clusters and performed survival analyses.
  • Integrated significant clusters with clinical factors (T stage, N stage, differentiation) to build a risk model.

Main Results:

  • Identified three histomorphological clusters (Cluster13, Cluster19, Cluster24) significantly associated with patient prognosis.
  • The prognostic risk model stratified patients into high-risk and low-risk groups with significant survival differences in training and validation sets.
  • Incorporating morphological clusters improved predictive performance modestly but significantly compared to clinical factors alone.

Conclusions:

  • Computational pathology can reveal novel, visually subtle morphological features with independent prognostic value in CRC.
  • These findings offer potential to refine CRC patient stratification and enhance clinical decision-making.
  • The study highlights the complementary role of AI-driven histomorphology in oncology diagnostics.