Pulmonary Nodules, MultipleDLIRTHORAXNCT04482114
Detection and Volumetry of Pulmonary Nodules on Ultra-low Dose Chest CT Scan With Deeplearning Image Reconstruction Algorithm (DLIR)
A study testing ULD CT in people with Pulmonary Nodules, Multiple, run by Centre Hospitalier Universitaire, Amiens at 1 site in France.
Trial summary
evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in the detection of pulmonary nodules. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Pulmonary Nodules, Multiple
- What is being tested?
- ULD CT
- Type of treatment
- Injection or infusion, Surgery or procedure
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 70 participants
- Where
- 1 siteFrance
- Study dates
- 22 Jul 2020to 4 Mar 2022
- Phase
- Interventional
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Age ≥ 18 years old,
- Patient referred for non-enhanced chest CT for lung nodule check-up or follow-up,
- Affiliation to a social security program,
- Ability of the subject to understand and express opposition
Who cannot join
- Age <18 years old,
- Person under guardianship or curatorship,
- Pregnant woman,
- Any contraindications to CT
Trial locations and contacts
1 site in 1 country.
About this study
evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in the detection of pulmonary nodules.
Read the detailed description
- Background: Lung cancer is the leading cause of cancer deaths. Patients with pulmonary nodules often undergo multiple computed tomography (CT) examinations for diagnostic and follow-up purposes.
- Purpose: The main objective of this study is to evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in the detection of pulmonary nodules.
- Abstract: Despite recent advances, lung cancer remains the most commonly diagnosed cancer and the leading cause of cancer death worldwide because it is often diagnosed at advanced stages that are not surgically curable. Nevertheless, early detection of lung cancer allows surgical resection, offers curative treatment and the best chance of survival. There is currently no screening program in France, but individual screening can be carried out depending on risk factors. Many pulmonary nodules are discovered each year, most of which are benign. The challenge is to distinguish malignant lesions from the multitude of benign lesions. One of the most effective criteria is the doubling time of the nodules which leads to multiple follow-up examinations requiring ionizing radiation to assess the size and growth of the nodules. Great efforts are currently being made by CT manufacturers in order to reduce the radiation with equivalent diagnostic performance. Patients who were referred to our department for an unenhanced low-dose chest CT (LD CT) for pulmonary nodules check-up or follow-up, and had consented to participate in the study, will undergo an additional ultra-low dose acquisition (ULDCT, <0,25 mSv, similar to standard two-view chest X-Ray) with deep learning-based reconstruction (DLIR). The main objective of this study is to evaluate the diagnostic performance between ULD and LD CT protocols for the detection of pulmonary nodules. The impact of dose reduction will be assessed in this context. The data from each examination will be blindly interpreted from the results of the other one. No follow-up will be required for the study.
- Conditions
- Pulmonary Nodules, Multiple
- Being tested
- ULD CT
- Keywords
- lung nodules, pulmonary nodules, lung cancer, radiation dose, dose reduction, low dose CT, ultra-low dose CT, DLIR, deep learning reconstruction
- Medical subject headings
- Multiple Pulmonary Nodules, Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ULD CTRadiation
All the examinations are part of the routine care. Addition of the ULD CT protocol does not require injection of contrast agent and does not extend the duration of the examination.
| Study group | Role | What they receive |
|---|---|---|
| ultra-low dose CT | Other | All the examinations are part of the routine care. Addition of the ULD CT protocol does not require injection of contrast agent and does not extend the duration of the examination. Receives: Radiation: ULD CT |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy
The study aimed to investigate the diagnostic accuracy (Sensibility and Specificity) of ultra-low dose CT using DLIR reconstruction for the detection of pulmonary nodules in comparison with the low dose CT reference protocol.
Measured: Day 0
Secondary outcomes (2)
Image quality
- The signal-to-noise ratio or SNR is calculated on areas of interest placed manually on the image (pulmonary parenchyma, axillary fat and surrounding air).
- This ratio is calculated by the average signal strength in these areas, divided by the standard deviation of the signal in outdoor areas such as the surrounding air.
- The quality of the image is estimated by a score ranging from 0 (poor quality) to 3 (excellent quality) determined subjectively by the operator.
Measured: Day 0
Pulmonary nodules volume
Difference of pulmonary nodules volume between images acquired at low dose CT and ultra-low dose CT.
Measured: Day 0
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jul 2020
- Study start22 Jul 2020
- Primary completion4 Mar 2022
- Study completion4 Mar 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pulmonary Nodules, MultipleStudies of the same condition, open and under way.
- More trials run by Centre Hospitalier Universitaire, AmiensThe 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- prospective transversal monocentric study
- Masking
- None (open label)
- Enrolment
- 70 participants (actual)
Sponsor and investigators
- Lead sponsor
- Centre Hospitalier Universitaire, Amiens
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04482114
- Sponsor's protocol ID
- PI2020_843_0052
- Acronym
- DLIRTHORAX
- Status last verified
- Jun 2026
- First submitted
- 15 Jul 2020
- Last updated
- 11 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 11 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Detection and Volumetry of Pulmonary Nodules on Ultra-low Dose Chest CT Scan With Deeplearning Image Reconstruction Algorithm (DLIR)
- ClinicalTrials.gov ID
- NCT04482114
- Acronym / sponsor ID
- DLIRTHORAX / PI2020_843_0052
- Conditions
- Pulmonary Nodules, Multiple
- Keywords
- lung nodules, pulmonary nodules, lung cancer, radiation dose, dose reduction, low dose CT, ultra-low dose CT, DLIR
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 70 participants (actual)
- Lead sponsor
- Centre Hospitalier Universitaire, AmiensUniversity, hospital or other
- Study start
- 22 Jul 2020 (actual)
- Primary completion
- 4 Mar 2022 (actual)
- Study completion
- 4 Mar 2022 (actual)
- First posted
- 22 Jul 2020 (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.
evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in the detection of pulmonary nodules.
Detailed description
- Background: Lung cancer is the leading cause of cancer deaths. Patients with pulmonary nodules often undergo multiple computed tomography (CT) examinations for diagnostic and follow-up purposes.
- Purpose: The main objective of this study is to evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in the detection of pulmonary nodules.
- Abstract: Despite recent advances, lung cancer remains the most commonly diagnosed cancer and the leading cause of cancer death worldwide because it is often diagnosed at advanced stages that are not surgically curable. Nevertheless, early detection of lung cancer allows surgical resection, offers curative treatment and the best chance of survival. There is currently no screening program in France, but individual screening can be carried out depending on risk factors. Many pulmonary nodules are discovered each year, most of which are benign. The challenge is to distinguish malignant lesions from the multitude of benign lesions. One of the most effective criteria is the doubling time of the nodules which leads to multiple follow-up examinations requiring ionizing radiation to assess the size and growth of the nodules. Great efforts are currently being made by CT manufacturers in order to reduce the radiation with equivalent diagnostic performance. Patients who were referred to our department for an unenhanced low-dose chest CT (LD CT) for pulmonary nodules check-up or follow-up, and had consented to participate in the study, will undergo an additional ultra-low dose acquisition (ULDCT, <0,25 mSv, similar to standard two-view chest X-Ray) with deep learning-based reconstruction (DLIR). The main objective of this study is to evaluate the diagnostic performance between ULD and LD CT protocols for the detection of pulmonary nodules. The impact of dose reduction will be assessed in this context. The data from each examination will be blindly interpreted from the results of the other one. No follow-up will be required for the study.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- prospective transversal monocentric study
| Arm | Type | Receives |
|---|---|---|
| ultra-low dose CT | Other | Radiation: ULD CTAll the examinations are part of the routine care. Addition of the ULD CT protocol does not require injection of contrast agent and does not extend the duration of the examination. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Age ≥ 18 years old,
- Patient referred for non-enhanced chest CT for lung nodule check-up or follow-up,
- Affiliation to a social security program,
- Ability of the subject to understand and express opposition
Key exclusion criteria (4 listed)
- Age <18 years old,
- Person under guardianship or curatorship,
- Pregnant woman,
- Any contraindications to CT
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 |
|---|---|---|
| ULD CTAll the examinations are part of the routine care. Addition of the ULD CT protocol does not require injection of contrast agent and does not extend the duration of the examination. | Radiation | ultra-low dose CT |
Primary outcome
Diagnostic accuracy
Time frame: Day 0
The study aimed to investigate the diagnostic accuracy (Sensibility and Specificity) of ultra-low dose CT using DLIR reconstruction for the detection of pulmonary nodules in comparison with the low dose CT reference protocol.
2 secondary outcomes
Image quality
Time frame: Day 0
Pulmonary nodules volume
Time frame: Day 0
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 11 Jun 2026.
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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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing ULD CT in people with Pulmonary Nodules, Multiple, run by Centre Hospitalier Universitaire, Amiens at 1 site in France.
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 4 inclusion and 4 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 11 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. 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?
Diagnostic accuracy (time frame: Day 0). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 11 Jun 2026.