Jove
Visualize
Contact Us

Related Concept Videos

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
  1. Home
  3. Mitigating Interobserver Variability In Radiomics With Combat: A Feasibility Study.
  1. Home
  3. Mitigating Interobserver Variability In Radiomics With Combat: A Feasibility Study.

Related Experiment Video

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.2K

Mitigating Interobserver Variability in Radiomics with ComBat: A Feasibility Study.

Alessia D'Anna1, Giuseppe Stella1, Anna Maria Gueli1

  • 1Department of Physics and Astronomy "E. Majorana", University of Catania, Via Santa Sofia 64, 95123 Catania, Italy.

Journal of Imaging
|November 26, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Intraobserver Features Variability (IFV) in radiomics can be reduced using ComBat harmonization. This method improves the reliability of radiomics analysis across different centers and physicians.

Keywords:
batch correctionclinical imagingmulticenter studiesprecision medicineradiomicssegmentation

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

6.7K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.1K

Related Experiment Videos

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.2K
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

6.7K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.1K

Area of Science:

  • Radiomics
  • Medical Imaging Analysis
  • Biostatistics

Background:

  • Radiomics studies face challenges with Intraobserver Features Variability (IFV).
  • Variability arises from differences in manual or semi-automated segmentation by physicians.
  • Standardizing radiomics data is crucial for multicenter studies.

Purpose of the Study:

  • To investigate IFV in radiomics.
  • To assess the effectiveness of ComBat harmonization in reducing IFV.
  • To evaluate the impact of segmentation methods and physician experience on radiomics outcomes.

Main Methods:

  • Utilized the NSCLC-Radiomics-Interobserver1 dataset with CT scans from 22 Non-Small Cell Lung Cancer patients.
  • Performed manual ('vis') and semi-automated ('auto') Gross Tumor Volume (GTV) segmentations by five radiation oncologists.
  • Extracted 1229 radiomic features from original and filtered images before and after ComBat harmonization.

    • Main Results:

    • ComBat harmonization significantly reduced the percentage of statistically significant features attributed to IFV.
    • For manual segmentation, significant features decreased from 83% to 34%.
    • For semi-automated segmentation, significant features decreased from 75% to 33%.

    Conclusions:

    • ComBat harmonization effectively mitigates IFV in radiomics.
    • This harmonization enhances the feasibility of multicenter radiomics research.
    • Physician experience significantly impacts radiomics analysis outcomes.