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

CT-based vascular invasion in pancreatic ductal adenocarcinoma compared with intraoperative and histological findings.

Insights into imaging·2026
Same author

Learning Curves and Complexity Evolution in Robotic Liver Surgery: An International Multicenter Study with Comparison to Global Benchmark Outcomes.

Annals of surgery·2026
Same author

Performance of GPT-based large language models in hepatocellular carcinoma stratification: liver function assessment, BCLC staging, and treatment recommendations.

Scientific reports·2026
Same author

Refining prognostication in intermediate-stage hepatocellular carcinoma after transarterial chemoembolization: promise and limitations of the proposed NTAA model.

Hepatobiliary surgery and nutrition·2026
Same author

Initial Results of FAPI PET/MRI to Assess the Extent of Endometriosis.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same author

Prognostic role of FDG-PET in patients with relapsed/refractory large B-cell lymphoma treated with CD3-CD20 directed bispecific antibodies: a multicentric analysis.

European journal of nuclear medicine and molecular imaging·2026
Same journal

CIRSE Position Statement on Sustainability in Interventional Radiology.

Cardiovascular and interventional radiology·2026
Same journal

Optical Navigation Robot-Assisted versus Conventional CT-Guided Localization of Pulmonary Nodules: A Comparison of Efficacy and Analysis of Complication Predictors.

Cardiovascular and interventional radiology·2026
Same journal

Modified Balloon Technique for Endoluminal Mitomycin-C Delivery in Refractory Esophageal Anastomotic Stricture: A Technical Note.

Cardiovascular and interventional radiology·2026
Same journal

Should Interventional Radiologists Incorporate Intravascular Ultrasound (IVUS) Into Their Daily Practice? A Systematic Review and Meta-Analysis of IVUS in Peripheral Arterial Endovascular Interventions.

Cardiovascular and interventional radiology·2026
Same journal

Precision of Ablation Margin Assessment After Liver Microwave Ablation: A Head-to-Head Comparison of Visual and Software-Assisted Methods.

Cardiovascular and interventional radiology·2026
Same journal

Stereotactic Microwave Ablation of Early-Stage Hepatocellular Carcinoma in Patients with Transjugular Intrahepatic Portosystemic Shunts: A Matched Case-Control Study.

Cardiovascular and interventional radiology·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Radiomics-Based Prediction Model for Outcome of Radioembolization in Metastatic Colorectal Cancer.

Wolfgang Roll1,2, Max Masthoff3,2, Michael Köhler3,2

  • 1Department of Nuclear Medicine, University Hospital Münster, Münster, Germany.

Cardiovascular and Interventional Radiology
|February 28, 2024
PubMed
Summary
This summary is machine-generated.

A computed tomography (CT) radiomics model effectively predicts treatment response and survival in patients with colorectal liver metastases undergoing transarterial Yttrium-90 radioembolization (TARE). The model quantifies tumor heterogeneity, identifying high-risk patients for poorer outcomes.

Keywords:
Liver metastasesRadioembolizationRadiomics

More Related Videos

Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer
08:12

Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer

Published on: March 14, 2019

5.5K
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

265

Related Experiment Videos

Last Updated: Jul 2, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer
08:12

Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer

Published on: March 14, 2019

5.5K
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

265

Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging Analysis

Background:

  • Colorectal liver metastases are a common clinical challenge.
  • Transarterial Yttrium-90 radioembolization (TARE) is a treatment option for these metastases.
  • Predicting treatment response and survival is crucial for optimizing patient management.

Purpose of the Study:

  • To evaluate a contrast-enhanced computed tomography (CT) radiomics-based model.
  • To predict treatment response and survival in patients with colorectal liver metastases receiving TARE.
  • To assess the benefit of radiomics in this specific patient population.

Main Methods:

  • Retrospective study of 51 patients undergoing TARE.
  • Radiomic features (RF) extracted from pre-TARE CT scans.
  • A radiomics model developed using logistic regression to classify response (RECIST 1.1).
  • Kaplan-Meier analysis to compare survival between risk groups.

Main Results:

  • Two RFs (Energy, Maximal Correlation Coefficient) reflected tumor heterogeneity and predicted TARE non-response.
  • The radiomics model achieved an AUC of 0.75 for predicting treatment response.
  • High-risk patients identified by the model had significantly shorter overall survival (3.4 vs. 6.4 months).

Conclusions:

  • CT radiomics can predict response and survival in TARE-treated colorectal liver metastases.
  • Quantifying tumor heterogeneity using radiomics is key to predicting outcomes.
  • This model offers potential for improved patient stratification and treatment planning.