Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Dysphagia in Head and Neck Radiotherapy: The Influence of Pharyngeal Constrictor Anatomy and Dosimetry.

Dysphagia·2024

Same author

Developing and validating multi-omics prediction models for late patient-reported dysphagia in head and neck radiotherapy.

Biomedical physics & engineering express·2024

Same author

Can machine learning models improve early detection of brain metastases using diffusion weighted imaging-based radiomics?

Quantitative imaging in medicine and surgery·2023

Same author

Machine learning-generated decision boundaries for prediction and exploration of patient-specific quality assurance failures in stereotactic radiosurgery plans.

Medical physics·2022

Same author

A model of infection and immune response to low dose radiation.

International journal of radiation biology·2022

Same author

Investigating diffusion patterns of brain metastases pre- and post-stereotactic radiosurgery: a feasibility study.

Biomedical physics & engineering express·2021

Search research articles

Related Experiment Video

Updated: May 1, 2026

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

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

Published on: October 27, 2023

Multi-omic feature reliability of deformable image registration-based images.

Owen Paetkau¹, Ekaterina Tchistiakova¹, Charles Kirkby¹

¹Department of Physics and Astronomy University of Calgary, 2500, University Dr NW, Calgary, AB, T2N 1N4, Canada.

Biomedical Physics & Engineering Express

|May 12, 2025

Summary

Radiomic features showed lower reliability than dosiomic features when extracted from synthetic CT images using different deformable image registration workflows. This highlights the need for caution when developing predictive models based on these features.

Keywords:

deformable image registration dosiomics head and neck radiomics radiotherapy reliability

More Related Videos

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Related Experiment Videos

Last Updated: May 1, 2026

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

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

Published on: October 27, 2023

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Area of Science:

Medical Physics
Radiotherapy Oncology
Image Analysis

Background:

Synthetic CT (sCT) images are crucial for adaptive radiotherapy, enabling contour propagation and dose recalculation.
Deformable image registration (DIR) workflows are essential for generating accurate sCTs from planning CTs.
Evaluating the reliability of radiomic and dosiomic features derived from sCTs is vital for clinical implementation.

Purpose of the Study:

To assess the reliability of radiomic and dosiomic features extracted from sCTs generated by two commercial DIR workflows.
To compare the performance of MIM and Velocity DIR systems in generating reliable multi-omic features.
To guide the selection of appropriate features for predictive modeling in head and neck radiotherapy.

Main Methods:

Extracted multi-omic features from organs at risk (OARs) in 58 head and neck cancer patients.
Propagated contours from planning CT to sCTs using MIM and Velocity DIR workflows.
Evaluated feature reliability using intraclass correlation coefficient (ICC), with >0.75 considered moderately reliable.

Main Results:

MIM and Velocity DIR workflows produced statistically similar OAR volumes and mean doses on sCTs.
Gamma analysis confirmed plan quality with 83% of plans achieving >95% passing rate at 3%/3 mm.
Dosiomic features (59% moderate reliability) were more reliable than radiomic features (21% moderate reliability) across workflows.

Conclusions:

Radiomic features demonstrate lower reliability compared to dosiomic features across different sCT DIR workflows.
The spinal cord exhibited the highest reliability for both radiomic (46%) and dosiomic (85%) features.
Caution is advised when implementing predictive models using features from varying sCT DIR workflows due to reliability differences.