Radiation ExposureNCT05804799
Liver CT Dose Reduction With Deep Learning Based Reconstruction
An observational study of Radiation Exposure and Liver Cancer, run by Seoul National University Hospital at 3 sites in 2 countries.
Trial summary
A deep learning-based de-noising (DLD) reconstruction algorithm (ClariCT.AI) has the potential to reduce image noise and improve image quality. This capability of the CliriCT.AI program might enable dose reduction for contrast-enhanced liver CT examination. Read more about this study
- Who is it for?
- Ages 20 to 85 years
- Conditions the study is for
- Radiation Exposure, Liver Cancer
- What is being tested?
- Contrast-enhanced liver CT scan
- How many people
- 300 participants
- Where
- 3 sitesin 2 countries
- Study dates
- 1 Jan 2021to 31 Dec 2022
- How long will I take part?
- 24 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 to 85 years
- All sexes
- Adult, Older adult
Study population: Patients with a suspicion of focal liver lesions had the plan to do a contrast-enhanced liver CT scan.
Who may be able to join
- Age between 20-year-old and 85 years old
- patients referred to the Radiology department to perform contrast-enhanced liver CT under the suspicion of focal liver lesions
Who cannot join
- patients with estimated glomerular filtration rate < 60 mL/min/1.73m2
- previous history of severe adverse reaction to iodinated contrast media.
Trial locations and contacts
3 sites in 2 countries.
3 sites in 2 countries
South Korea2 sites
Seoul National University Hospital
Seoul, 03080
Korea University Guro Hospital
Seoul, 08308
About this study
Official title: Comparison of Image Quality and Diagnostic Pefromance of Low Dose Liver CT With Deep Learning Reconstuction to Standard Dose CT: A Prospective Multicenter Non-inferiority Trial
A deep learning-based de-noising (DLD) reconstruction algorithm (ClariCT.AI) has the potential to reduce image noise and improve image quality. This capability of the CliriCT.AI program might enable dose reduction for contrast-enhanced liver CT examination. In this prospective multicenter study, whether the ClariCT.AI program can reduce the noise level of low-dose contrast-enhanced liver CT (LDCT) data and therefore, can provide comparable image quality to the standard dose of contrast-enhanced liver CT (SDCT) images will be evaluated.
The aim of this study is to compare image quality and diagnostic capability in detecting malignant tumors of LDCT with DLD to those of SDCT with MBIR using the predefined non-inferiority margin.
Read the detailed description
A deep learning-based de-noising (DLD) reconstruction algorithm (ClariCT.AI) has the potential to reduce image noise and improve image quality. This capability of the CliriCT.AI program might enable dose reduction for contrast-enhanced liver CT examination. In this prospective multicenter study, whether the ClariCT.AI program can reduce the noise level of low-dose contrast-enhanced liver CT (LDCT) data and therefore, can provide comparable image quality to the standard dose of contrast-enhanced liver CT (SDCT) images will be evaluated.
The aim of this study is to compare image quality and diagnostic capability in detecting malignant tumors of LDCT with DLD to those of SDCT with MBIR using the predefined non-inferiority margin.
- Conditions
- Radiation Exposure, Liver Cancer
- Being tested
- Contrast-enhanced liver CT scan
- Medical subject headings
- Liver Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Contrast-enhanced liver CT scanDiagnostic test
The contrast-enhanced liver CT scans were obtained from all of the participants.
The liver CT images were reconstructed by both low-dose scans with a deep-learning-based denoising program (ClariCT.AI) and standard-dose scans with model-based iterative reconstruction.
| Study group | Role | What they receive |
|---|---|---|
| Liver CT study group | Patients with a suspicion of focal liver lesions had the plan to perform a contrast-enhanced liver CT scan.
The liver CT images were reconstructed by both low-dose scans with a deep-learning-based denoising program (ClariCT.AI) and standard-dose scans with model-based iterative reconstruction. Receives: Diagnostic Test: Contrast-enhanced liver CT scan |
What is being measured
The primary outcome is the main question the study is designed to answer.
Measurement of standard deviation of CT attenuation values at the liver
Standard deviation of CT attenuation values at the liver parenchyma
Measured: within 6 months from acquisition of liver CT scans
Secondary outcomes (1)
Sensitivity to detect malignant liver tumor
Sensitivity of liver CT scans to detect malignant liver tumor
Measured: within 6 months from acquisition of liver CT scans
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Apr 2023
- Study start1 Jan 2021
- Primary completion31 Aug 2022
- Study completion31 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Radiation ExposureStudies of the same condition, open and under way.
- More trials run by Seoul National University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 300 participants (actual)
- Target duration
- 24 Months
Sponsor and investigators
- Lead sponsor
- Seoul National University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jeong Min Lee, Seoul National University Hospital
- Investigators
- Jeong Min Lee, M.D., Principal investigator, Seoul National University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05804799
- Sponsor's protocol ID
- SNUH-2007-040-1139
- Status last verified
- Apr 2023
- First submitted
- 27 Mar 2023
- Last updated
- 12 Apr 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Publications
Lee DH, Lee JM, Lee CH, Afat S, Othman A. Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT. Radiol Artif Intell. 2024 Mar;6(2):e230192. doi: 10.1148/ryai.230192.
PubMed 38231025
Study at a glance
From the registry record, last updated by the sponsor on 12 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison of Image Quality and Diagnostic Pefromance of Low Dose Liver CT With Deep Learning Reconstuction to Standard Dose CT: A Prospective Multicenter Non-inferiority Trial
- ClinicalTrials.gov ID
- NCT05804799
- Acronym / sponsor ID
- SNUH-2007-040-1139
- Conditions
- Radiation Exposure, Liver Cancer
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 300 participants (actual)
- Lead sponsor
- Seoul National University HospitalUniversity, hospital or other
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 31 Aug 2022 (actual)
- Study completion
- 31 Dec 2022 (actual)
- First posted
- 7 Apr 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
A deep learning-based de-noising (DLD) reconstruction algorithm (ClariCT.AI) has the potential to reduce image noise and improve image quality. This capability of the CliriCT.AI program might enable dose reduction for contrast-enhanced liver CT examination. In this prospective multicenter study, whether the ClariCT.AI program can reduce the noise level of low-dose contrast-enhanced liver CT (LDCT) data and therefore, can provide comparable image quality to the standard dose of contrast-enhanced liver CT (SDCT) images will be evaluated.
The aim of this study is to compare image quality and diagnostic capability in detecting malignant tumors of LDCT with DLD to those of SDCT with MBIR using the predefined non-inferiority margin.
Detailed description
A deep learning-based de-noising (DLD) reconstruction algorithm (ClariCT.AI) has the potential to reduce image noise and improve image quality. This capability of the CliriCT.AI program might enable dose reduction for contrast-enhanced liver CT examination. In this prospective multicenter study, whether the ClariCT.AI program can reduce the noise level of low-dose contrast-enhanced liver CT (LDCT) data and therefore, can provide comparable image quality to the standard dose of contrast-enhanced liver CT (SDCT) images will be evaluated.
The aim of this study is to compare image quality and diagnostic capability in detecting malignant tumors of LDCT with DLD to those of SDCT with MBIR using the predefined non-inferiority margin.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 24 Months
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Liver CT study group | – | Diagnostic Test: Contrast-enhanced liver CT scanPatients with a suspicion of focal liver lesions had the plan to perform a contrast-enhanced liver CT scan. The liver CT images were reconstructed by both low-dose scans with a deep-learning-based denoising program (ClariCT.AI) and standard-dose scans with model-based iterative reconstruction. |
Who may qualify
- Ages
- 20 to 85 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients with a suspicion of focal liver lesions had the plan to do a contrast-enhanced liver CT scan.
Key inclusion criteria (2 listed)
- Age between 20-year-old and 85 years old
- patients referred to the Radiology department to perform contrast-enhanced liver CT under the suspicion of focal liver lesions
Key exclusion criteria (2 listed)
- patients with estimated glomerular filtration rate < 60 mL/min/1.73m2
- previous history of severe adverse reaction to iodinated contrast media.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Contrast-enhanced liver CT scanThe contrast-enhanced liver CT scans were obtained from all of the participants. The liver CT images were reconstructed by both low-dose scans with a deep-learning-based denoising program (ClariCT.AI) and standard-dose scans with model-based iterative reconstruction. | Diagnostic test | Liver CT study group |
Primary outcome
Measurement of standard deviation of CT attenuation values at the liver
Time frame: within 6 months from acquisition of liver CT scans
Standard deviation of CT attenuation values at the liver parenchyma
1 secondary outcome
Sensitivity to detect malignant liver tumor
Time frame: within 6 months from acquisition of liver CT scans
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Apr 2023.
- Derived from this study: PubMed 38231025Lee DH, Lee JM, Lee CH, Afat S, Othman A. Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT. Radiol Artif Intell. 2024 Mar;6(2):e230192. doi: 10.1148/ryai.230192.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jeong Min Lee, M.D. principal investigator
Seoul National University Hospital
3 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Radiation Exposure and Liver Cancer, run by Seoul National University Hospital at 3 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 12 Apr 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in South Korea, Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Measurement of standard deviation of CT attenuation values at the liver (time frame: within 6 months from acquisition of liver CT scans). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05804799, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Apr 2023.