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

Diameter-Axial-Polar Nephrometry is Predictive of Surgical Outcomes Following Partial Nephrectomy.

Medicine·2015
Same author

Assessing visual green effects of individual urban trees using airborne Lidar data.

The Science of the total environment·2015
Same author

A New Ligustrazine Derivative-Selective Cytotoxicity by Suppression of NF-κB/p65 and COX-2 Expression on Human Hepatoma Cells. Part 3.

International journal of molecular sciences·2015
Same author

Organ and effective dose evaluation in coronary angiography by using a 320 MDCT based on in-phantom dose measurements with TLDs.

Journal of radiological protection : official journal of the Society for Radiological Protection·2015
Same author

High Strength Multifunctional Multiwalled Hydrogel Tubes: Ion-Triggered Shape Memory, Antibacterial, and Anti-inflammatory Efficacies.

ACS applied materials & interfaces·2015
Same author

Increased Plasma S100A12 Levels Are Associated With Diabetic Retinopathy and Prognostic Biomarkers of Macrovascular Events in Type 2 Diabetic Patients.

Investigative ophthalmology & visual science·2015

Related Experiment Video

Updated: May 7, 2025

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

6.8K

A machine learning-based model to predict POD24 in follicular lymphoma: a study by the Chinese workshop on follicular

Jie Zha1,2, Qinwei Chen1,2, Wei Zhang3

  • 1Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.

Biomarker Research
|January 3, 2025
PubMed
Summary

A new machine learning model, FLIPI-C, accurately predicts early disease progression in follicular lymphoma (FL) patients within 24 months (POD24). This tool uses simple markers to identify high-risk FL patients for better treatment decisions.

Keywords:
FLIPI-CFollicular lymphomaMachine learningOverall survivalPOD24

More Related Videos

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

8.1K
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

6.6K

Related Experiment Videos

Last Updated: May 7, 2025

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

6.8K
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

8.1K
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

6.6K

Area of Science:

  • Hematology
  • Oncology
  • Machine Learning in Medicine

Background:

  • Disease progression within 24 months (POD24) is a critical factor impacting overall survival (OS) in follicular lymphoma (FL) patients.
  • Identifying FL patients at high risk for POD24 is crucial for optimizing treatment strategies and improving patient outcomes.

Purpose of the Study:

  • To develop and validate a robust predictive model, named FLIPI-C, for identifying FL patients at high risk of POD24.
  • To leverage machine learning techniques for enhanced prognostic accuracy in follicular lymphoma.

Main Methods:

  • A cohort of 1,938 FL patients (FL1-3a) was used, randomly split into training and internal validation sets.
  • The XGBoost algorithm was employed to build the POD24 prediction model, which was validated internally and externally using the GALLIUM cohort.
  • Key predictors identified include lymphocyte-to-monocyte ratio (LMR), elevated lactate dehydrogenase (LDH), low hemoglobin (Hb), elevated beta-2 microglobulin (β2-MG), SUVmax, and lymph node involvement.

Main Results:

  • The FLIPI-C model demonstrated superior predictive accuracy (AUC) for POD24 and 3-year OS in both internal and external validation cohorts compared to existing models.
  • Internal validation showed AUCs of 0.764 for POD24 and 0.700 for OS; external validation yielded AUCs of 0.703 for POD24 and 0.653 for OS.
  • Decision curve analysis confirmed the superior clinical utility and net benefits of the FLIPI-C model.

Conclusions:

  • The FLIPI-C model, developed via machine learning, offers superior predictive accuracy for POD24 in FL patients.
  • It utilizes simple, readily available clinical and laboratory markers, making it practical for clinical application.
  • FLIPI-C shows promise in guiding treatment decisions and prognostic assessments for FL patients at high risk of early progression.