Lesion Detection Assessment in the Liver: Standard vs Low Radiation Dose Using Varied Post-Processing Techniques
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Primary Objective:
To evaluate whether post-processing software Adaptive Statistical Iterative Reconstruction (ASIR), ASIR-V, Veo 3.0 (GE version of Model-based Iterative Reconstruction (MBIR), and Deep Learning Image Reconstruction (DLIR) is able to preserve lesion detection in the liver and other measures of image quality at reduced radiation doses for computed tomography (CT).
Secondary Objectives:
Assessment of whether post-processing software enhances lesion detection in the liver and other measures of image quality at standard and reduced radiation doses.
Assessment of whether DLIR and GSI DLIR reconstructions perform differently, both in terms of accuracy and image quality metrics such as noise reduction.
Study Type
Study Type
Enrollment (Actual)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Corey T. Jensen, MD
- Phone Number: 713-745-3835
- Email: CJensen@mdanderson.org
Study Locations
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Texas
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Houston, Texas, United States, 77030
- University of Texas MD Anderson Cancer Center
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Patient must be >/= 18 years of age and </=90 years of age
- Men and non-pregnant women
- Pathology proven diagnosis of colon or colorectal carcinoma
- Liver metastases on most recent CT examination
- Standard of care CT abdomen examination planned WITH IV contrast
Exclusion Criteria:
- Patients cannot give informed consent
- Patients cannot undergo CT examination
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: Non-Randomized
- Interventional Model: Single Group Assignment
- Masking: Single
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Experimental: Computed Tomography Scan - 50% Dose Reduction
Participants undergo routine standard of care CT examination for colon carcinoma restaging, then have an additional scan of the liver at 50% dose reduction.
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Participants undergo routine standard of care CT examination for colon carcinoma restaging, then have an additional scan of the liver at 50% dose reduction.
Other Names:
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Experimental: Computed tomography Scan - 70% Dose Reduction
Participants undergo routine standard of care CT examination for colon carcinoma restaging, then have an additional scan of the liver at 70% dose reduction.
|
Participants undergo routine standard of care CT examination for colon carcinoma restaging, then have an additional scan of the liver at 70% dose reduction.
Other Names:
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Experimental: Deep Learning Image Reconstruction (DLIR)
DLIR is available in both single (SE) and dual/multi energy (DE) CT scanning modes.
DLIR SECT and DLIR DECT reconstructions have yet to be compared.
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Participants to receive standard-of-care imaging without the artificial intelligence software and imaging technique.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Metastasis Detection Accuracy
Time Frame: 1 day
|
Primary endpoint is metastasis detection accuracy status of each patient, where the standard of care scan reviewed by ''truth readers'' (independent to the blinded radiologists) serve as the gold standard.
If any lesion of a patient is diagnosed as metastasis by "truth readers" or blinded readers' consensus, that patient will be considered true positive and diagnosis positive, respectively.
The expected accuracy of standard CT is 95%, and a low dose CT detection be considered non-inferior if its accuracy is 85% or higher.
|
1 day
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Corey T. Jensen, MD, M.D. Anderson Cancer Center
Publications and helpful links
General Publications
- Jensen CT, Gupta S, Saleh MM, Liu X, Wong VK, Salem U, Qiao W, Samei E, Wagner-Bartak NA. Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases. Radiology. 2022 Apr;303(1):90-98. doi: 10.1148/radiol.211838. Epub 2022 Jan 11.
- Jensen CT, Wagner-Bartak NA, Vu LN, Liu X, Raval B, Martinez D, Wei W, Cheng Y, Samei E, Gupta S. Detection of Colorectal Hepatic Metastases Is Superior at Standard Radiation Dose CT versus Reduced Dose CT. Radiology. 2019 Feb;290(2):400-409. doi: 10.1148/radiol.2018181657. Epub 2018 Nov 27.
Helpful Links
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
- Neoplasms by Site
- Neoplasms
- Intestinal Diseases
- Gastrointestinal Neoplasms
- Digestive System Neoplasms
- Gastrointestinal Diseases
- Intestinal Neoplasms
- Rectal Diseases
- Colonic Diseases
- Digestive System Diseases
- Colorectal Neoplasms
- Colonic Neoplasms
- Diagnostic Techniques and Procedures
- Diagnosis
- Tomography
- Diagnostic Imaging
- Radiography
- Image Interpretation, Computer-Assisted
- Radiographic Image Enhancement
- Image Enhancement
- Photography
- Tomography, X-Ray
- Tomography, X-Ray Computed
Other Study ID Numbers
Other Study ID Numbers
- 2016-1135
- NCI-2018-01272 (Other Identifier: NCI-CTRP Clinical Trials Reporting Registry)
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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