NCT03151564

Brief Summary

To compare 2 different image creation/processing techniques during a standard CT scan in order to "see" problems in the liver and learn which method provides better image quality. The techniques use new artificial intelligence software to decrease image noise, which helps the radiologist to evaluate.

Trial Health

75
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
146

participants targeted

Target at P75+ for not_applicable

Timeline
11mo left

Started May 2017

Longer than P75 for not_applicable

Geographic Reach
1 country

1 active site

Status
active not recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress90%
May 2017Apr 2027

First Submitted

Initial submission to the registry

May 9, 2017

Completed
Same day until next milestone

Study Start

First participant enrolled

May 9, 2017

Completed
3 days until next milestone

First Posted

Study publicly available on registry

May 12, 2017

Completed
10 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

April 30, 2027

Expected
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

April 30, 2027

Last Updated

March 5, 2026

Status Verified

March 1, 2026

Enrollment Period

10 years

First QC Date

May 9, 2017

Last Update Submit

March 3, 2026

Conditions

Diseases of LiverColon CarcinomaColorectal CarcinomaLiver Metastases

Keywords

Diseases of liverColon carcinomaColorectal carcinomaLiver metastasesComputed tomographyCTReduced radiation dosesDeep Learning Image ReconstructionsDLIR

Outcome Measures

Primary Outcomes (1)

  • Metastasis Detection Accuracy

    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

Study Arms (3)

Computed Tomography Scan - 50% Dose Reduction

EXPERIMENTAL

Participants undergo routine standard of care CT examination for colon carcinoma restaging, then have an additional scan of the liver at 50% dose reduction.

Diagnostic Test: Computed Tomography Scan - 50% Dose Reduction

Computed tomography Scan - 70% Dose Reduction

EXPERIMENTAL

Participants undergo routine standard of care CT examination for colon carcinoma restaging, then have an additional scan of the liver at 70% dose reduction.

Diagnostic Test: Computed Tomography Scan - 70% Dose Reduction

Deep Learning Image Reconstruction (DLIR)

EXPERIMENTAL

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.

Diagnostic Test: Deep Learning Image Reconstruction (DLIR)

Interventions

Computed Tomography Scan - 50% Dose ReductionDIAGNOSTIC_TEST

Participants undergo routine standard of care CT examination for colon carcinoma restaging, then have an additional scan of the liver at 50% dose reduction.

Also known as: CT scan
Computed Tomography Scan - 50% Dose Reduction
Computed Tomography Scan - 70% Dose ReductionDIAGNOSTIC_TEST

Participants undergo routine standard of care CT examination for colon carcinoma restaging, then have an additional scan of the liver at 70% dose reduction.

Also known as: CT scan
Computed tomography Scan - 70% Dose Reduction
Deep Learning Image Reconstruction (DLIR)DIAGNOSTIC_TEST

Participants to receive standard-of-care imaging without the artificial intelligence software and imaging technique.

Deep Learning Image Reconstruction (DLIR)

Eligibility Criteria

Age18 Years - 90 Years
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • 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

You may not qualify if:

  • Patients cannot give informed consent
  • Patients cannot undergo CT examination

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

University of Texas MD Anderson Cancer Center

Houston, Texas, 77030, United States

Location

Related Publications (2)

  • 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.

    PMID: 35014900DERIVED

  • 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.

    PMID: 30480489DERIVED

Related Links

MeSH Terms

Conditions

Digestive System DiseasesColonic NeoplasmsColorectal Neoplasms

Interventions

Tomography, X-Ray Computed

Condition Hierarchy (Ancestors)

Intestinal NeoplasmsGastrointestinal NeoplasmsDigestive System NeoplasmsNeoplasms by SiteNeoplasmsGastrointestinal DiseasesColonic DiseasesIntestinal DiseasesRectal Diseases

Intervention Hierarchy (Ancestors)

Image Interpretation, Computer-AssistedDiagnostic ImagingDiagnostic Techniques and ProceduresDiagnosisRadiographic Image EnhancementImage EnhancementPhotographyRadiographyTomography, X-RayTomography

Study Officials

  • Corey T. Jensen, MD

    M.D. Anderson Cancer Center

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NON RANDOMIZED
Masking
SINGLE
Who Masked
OUTCOMES ASSESSOR
Purpose
DIAGNOSTIC
Intervention Model
SINGLE GROUP
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

May 9, 2017

First Posted

May 12, 2017

Study Start

May 9, 2017

Primary Completion (Estimated)

April 30, 2027

Study Completion (Estimated)

April 30, 2027

Last Updated

March 5, 2026

Record last verified: 2026-03

Locations

US(1)