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

EMPIRE (NSABP FC-13): a biomarker-driven phase II platform trial evaluating cemiplimab-based immunotherapy in microsatellite-stable colorectal cancer with ctDNA-defined minimal residual disease.

Future oncology (London, England)·2026
Same author

Tumoral and Systemic Immune Correlates of Response to Concurrent Pembrolizumab and Chemoradiotherapy in Patients with Resected High-Risk Head and Neck Squamous Cell Carcinoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Headwinds in Breast Cancer Research: The Case for Pragmatic Radiotherapy De-escalation Studies.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Optimizing Cancer Care Delivery Through High-Functioning Teams: Results of Implementing the 4R Oncology Model in Lung and Breast Cancers.

JCO oncology practice·2026
Same author

Therapy for Stage IV Non-Small Cell Lung Cancer With Driver Alterations: ASCO Living Guideline, 2026.3.1.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

The co-occurrence of obesity with cancer-related fatigue and their impact on physical function among breast cancer survivors.

Cancer survivorship research & care·2026
Same journal

Polatuzumab Vedotin Plus Rituximab, Gemcitabine, and Oxaliplatin in Relapsed or Refractory Diffuse Large B-Cell Lymphoma: Results From the Phase III, Randomized POLARGO Trial.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same journal

Biology Over Monotherapy: Mature Results From European Organisation For Research and Treatment of Cancer 22033-26033 Reaffirm Molecular Stratification, Not Treatment Sequence, Defines Prognosis in Low-Grade Glioma.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same journal

Risk Prognostication After Hypomethylating Agents Combined With Venetoclax in AML: The PRISM Risk Model.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same journal

Temozolomide Versus Radiotherapy as First-Line Therapy for Low-Grade Glioma: Mature Results of a Randomized Phase III Trial (EORTC 22033-26033/NCIC-CTG/TROG/MRC-CTU).

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same journal

Efficacy and Tolerability of Zenocutuzumab in Advanced <i>NRG1</i> Fusion-Positive Cholangiocarcinoma: Results From the eNRGy Phase II Trial.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same journal

Phase I/II Study of Sonrotoclax (BGB-11417) Monotherapy in Patients With Mantle Cell Lymphoma Previously Treated With Anti-CD20 Therapy and a Bruton Tyrosine Kinase Inhibitor.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 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

Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer.

Lujia Chen1, Ying Wang2, Chunhui Cai1

  • 1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA.

Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology
|February 5, 2024
PubMed
Summary
This summary is machine-generated.

A new COLOXIS model predicts which early-stage colon cancer patients benefit from oxaliplatin chemotherapy, minimizing toxicity for non-responders. This precision approach aims to improve adjuvant therapy outcomes.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Development and Maintenance of a Preclinical Patient Derived Tumor Xenograft Model for the Investigation of Novel Anti-Cancer Therapies
09:29

Development and Maintenance of a Preclinical Patient Derived Tumor Xenograft Model for the Investigation of Novel Anti-Cancer Therapies

Published on: September 30, 2016

13.7K

Related Experiment Videos

Last Updated: Jul 4, 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
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Development and Maintenance of a Preclinical Patient Derived Tumor Xenograft Model for the Investigation of Novel Anti-Cancer Therapies
09:29

Development and Maintenance of a Preclinical Patient Derived Tumor Xenograft Model for the Investigation of Novel Anti-Cancer Therapies

Published on: September 30, 2016

13.7K

Area of Science:

  • Oncology
  • Genomics
  • Clinical Trials

Background:

  • Adjuvant therapy with fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is standard for early-stage colon cancer (CC).
  • Oxaliplatin can cause severe, long-term neurotoxicity.
  • Identifying patients who benefit from oxaliplatin is crucial to maximize efficacy and minimize side effects.

Purpose of the Study:

  • To develop and validate a machine learning model, the colon oxaliplatin signature (COLOXIS), for predicting response to oxaliplatin-containing regimens in CC adjuvant therapy.
  • To assess if the COLOXIS model can identify patients who benefit from oxaliplatin, thereby guiding treatment decisions.

Main Methods:

  • Trained a machine learning model (COLOXIS) to predict oxaliplatin response.
  • Evaluated the model in 1,065 patients from NSABP C-07 and C-08 phase III trials treated with 5-fluorouracil plus leucovorin (FULV) or FOLFOX.
  • Assessed 8-year recurrence-free survival and interaction P-values to determine predictive value for oxaliplatin benefits.

Main Results:

  • The COLOXIS model dichotomized patients into COLOXIS+ (predicted responders) and COLOXIS- (predicted non-responders).
  • COLOXIS+ patients significantly benefited from oxaliplatin (HR, 0.65; P=.0065; interaction P=.03).
  • COLOXIS- patients did not show benefit from oxaliplatin (HR, 1.08; P=.65).

Conclusions:

  • The COLOXIS model accurately predicts oxaliplatin benefit in the adjuvant colon cancer setting.
  • Results support reserving oxaliplatin for COLOXIS+ patients to optimize treatment.
  • Further investigation is warranted to confirm these findings and guide clinical practice.