Lung DiseasesNCT05398887
Effectiveness of Ultra-low-dose Chest CT With AI Based Denoising Solution
A study testing Low radiation dose CT, Underwent ultra dose chest CT and Artificial Intelligence based model in people with Lung Diseases, run by Intermed Hospital.
Trial summary
The main objective of the study is to evaluate the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with innovative vendor-neutral CT denoising solution based on deep learning technology. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Diseases
- What is being tested?
- Low radiation dose CT, Underwent ultra dose chest CT and Artificial Intelligence based model
- Type of treatment
- Surgery or procedure
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- No sites listed yet
- Study dates
- 15 Jun 2022to 1 Oct 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
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients aged over 18-year-old
- Patients undergoing CT Chest for all purpose
Who cannot join
- Age less than 18 years
- Any suspicion of pregnancy
- History of thoracic surgery or placement of the metallic device in the thorax
- An inability to hold respiration during CT
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Utilization and Effectiveness of Ultra-low-dose Chest Computed Tomography Using Innovative CT Denoising Solution Based on Deep Learning Technology
The main objective of the study is to evaluate the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with innovative vendor-neutral CT denoising solution based on deep learning technology.
Read the detailed description
Considering lung cancer-related public health challenges, a reliable lung cancer screening method for high-risk cohorts in Mongolia is needed. Thus, our study aims to assess the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with artificial intelligence based CT denoising technique among various patient groups.
- Conditions
- Lung Diseases
- Being tested
- Low radiation dose CT, Underwent ultra dose chest CT, Artificial Intelligence based model
- Keywords
- Computed tomography, Artificial Intelligence, Denoising technique, Ultra-low-dose Computed tomography
- Medical subject headings
- Lung Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Low radiation dose CTRadiation
Underwent low dose chest CT with 30% lower radiation dose
Underwent ultra dose chest CTRadiation
Underwent ultra dose chest CT with 90% lower radiation dose
Artificial Intelligence based modelOther
Deep-learning based contrast boosting algorithms
| Study group | Role | What they receive |
|---|---|---|
| Low dose Chest CT scan | Active comparator | Underwent low dose chest CT with 30% lower radiation dose
Interventions:
Radiation: Low radiation dose CT Other: Image quality analysis Receives: Radiation: Low radiation dose CT |
| Ultra low dose CT scan with Artificial Intelligence | Experimental | Interventions:
Radiation: Low radiation dose CT Image quality Other: Deep-learning based contrast boosting algorithms Receives: Radiation: Underwent ultra dose chest CT; Other: Artificial Intelligence based model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Detection rate of pulmonary conditions
Pulmonary condition detection rate on low dose chest CT and ultra dose chest CT with artificial intelligence-based CT denoising solution by blinded reviewers
Measured: Within 2 weeks after data collection
Contrast media dose
Administered contrast media dose in each patient
Measured: Within 2 weeks after data collection
Secondary outcomes (1)
Image contrast
Signal to Noise, Noise and Edge-rise-distance on a five-point scale (1-5) with a higher score indicates better conspicuity.
Measured: Within 2 weeks after data collection
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Jun 2022
- Study start15 Jun 2022 (estimated)
- Primary completion1 Sep 2022 (estimated)
- Study completion1 Oct 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (19)Papers the sponsor has linked to this study.
- Other trials for Lung DiseasesStudies of the same condition, open and under way.
- 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
- Randomised
- Intervention model
- Parallel groups
- Masking
- Quadruple blind
- Who is masked
- Participant, Care provider, Investigator, Outcomes assessor
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Intermed Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Bayarbaatar Bold, Intermed Hospital
- Investigators
- Khulan Khurelsukh, M.D, MSc, Study chair, Intermed HospitalDelgerekh Sainjargal, M.D, MSc, Principal investigator, Intermed HospitalBayarbaatar Bold, M.D, Principal investigator, Intermed Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05398887
- Sponsor's protocol ID
- IMC20220515-01
- Status last verified
- May 2022
- First submitted
- 25 May 2022
- Last updated
- 1 Jun 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
International Agency for Research on Cancer. Global Cancer Observatory: cancer today. World Health Organization. https://gco.iarc.fr/today (accessed Feb 14, 2022).
Health Development Center, WHO. Health Indicators 2019. Mongolian Health Development Center. http://hdc.gov.mn/media/uploads/202108/Eruul_mendiin_uzuulelt_2020.pdf (accessed Feb 14, 2022).
WHO global report. WHO global report on mortality attributable to tobacco. 2012
Zheng W, McLerran DF, Rolland BA, Fu Z, Boffetta P, He J, Gupta PC, Ramadas K, Tsugane S, Irie F, Tamakoshi A, Gao YT, Koh WP, Shu XO, Ozasa K, Nishino Y, Tsuji I, Tanaka H, Chen CJ, Yuan JM, Ahn YO, Yoo KY, Ahsan H, Pan WH, Qiao YL, Gu D, Pednekar MS, Sauvaget C, Sawada N, Sairenchi T, Yang G, Wang R, Xiang YB, Ohishi W, Kakizaki M, Watanabe T, Oze I, You SL, Sugawara Y, Butler LM, Kim DH, Park SK, Parvez F, Chuang SY, Fan JH, Shen CY, Chen Y, Grant EJ, Lee JE, Sinha R, Matsuo K, Thornquist M, Inoue M, Feng Z, Kang D, Potter JD. Burden of total and cause-specific mortality related to tobacco smoking among adults aged >/= 45 years in Asia: a pooled analysis of 21 cohorts. PLoS Med. 2014 Apr 22;11(4):e1001631. doi: 10.1371/journal.pmed.1001631. eCollection 2014 Apr.
PubMed 24756146Fourth national STEPS Survey on the Prevalence of Noncommunicable Disease and Injury Risk Factors-2019. World Health Organization.
Smith-Bindman R, Lipson J, Marcus R, Kim KP, Mahesh M, Gould R, Berrington de Gonzalez A, Miglioretti DL. Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch Intern Med. 2009 Dec 14;169(22):2078-86. doi: 10.1001/archinternmed.2009.427.
PubMed 20008690National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29.
PubMed 21714641de Koning HJ, van der Aalst CM, de Jong PA, Scholten ET, Nackaerts K, Heuvelmans MA, Lammers JJ, Weenink C, Yousaf-Khan U, Horeweg N, van 't Westeinde S, Prokop M, Mali WP, Mohamed Hoesein FAA, van Ooijen PMA, Aerts JGJV, den Bakker MA, Thunnissen E, Verschakelen J, Vliegenthart R, Walter JE, Ten Haaf K, Groen HJM, Oudkerk M. Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. N Engl J Med. 2020 Feb 6;382(6):503-513. doi: 10.1056/NEJMoa1911793. Epub 2020 Jan 29.
PubMed 31995683Boyd MA. U.S. radiation protection: role of national and international recommendations and opportunities for collaboration (harmony, not dissonance). Health Phys. 2015 Feb;108(2):278-82. doi: 10.1097/HP.0000000000000236.
PubMed 25551510Katsura M, Matsuda I, Akahane M, Yasaka K, Hanaoka S, Akai H, Sato J, Kunimatsu A, Ohtomo K. Model-based iterative reconstruction technique for ultralow-dose chest CT: comparison of pulmonary nodule detectability with the adaptive statistical iterative reconstruction technique. Invest Radiol. 2013 Apr;48(4):206-12. doi: 10.1097/RLI.0b013e31827efc3a.
PubMed 23344517Kim Y, Kim YK, Lee BE, Lee SJ, Ryu YJ, Lee JH, Chang JH. Ultra-Low-Dose CT of the Thorax Using Iterative Reconstruction: Evaluation of Image Quality and Radiation Dose Reduction. AJR Am J Roentgenol. 2015 Jun;204(6):1197-202. doi: 10.2214/AJR.14.13629.
PubMed 26001228Lee SW, Kim Y, Shim SS, Lee JK, Lee SJ, Ryu YJ, Chang JH. Image quality assessment of ultra low-dose chest CT using sinogram-affirmed iterative reconstruction. Eur Radiol. 2014 Apr;24(4):817-26. doi: 10.1007/s00330-013-3090-9. Epub 2014 Jan 18.
PubMed 24442444Nagatani Y, Takahashi M, Murata K, Ikeda M, Yamashiro T, Miyara T, Koyama H, Koyama M, Sato Y, Moriya H, Noma S, Tomiyama N, Ohno Y, Murayama S; investigators of ACTIve study group. Lung nodule detection performance in five observers on computed tomography (CT) with adaptive iterative dose reduction using three-dimensional processing (AIDR 3D) in a Japanese multicenter study: Comparison between ultra-low-dose CT and low-dose CT by receiver-operating characteristic analysis. Eur J Radiol. 2015 Jul;84(7):1401-12. doi: 10.1016/j.ejrad.2015.03.012. Epub 2015 Apr 2.
PubMed 25892051Wang R, Sui X, Schoepf UJ, Song W, Xue H, Jin Z, Schmidt B, Flohr TG, Canstein C, Spearman JV, Chen J, Meinel FG. Ultralow-radiation-dose chest CT: accuracy for lung densitometry and emphysema detection. AJR Am J Roentgenol. 2015 Apr;204(4):743-9. doi: 10.2214/AJR.14.13101.
PubMed 25794063Yanagawa M, Gyobu T, Leung AN, Kawai M, Kawata Y, Sumikawa H, Honda O, Tomiyama N. Ultra-low-dose CT of the lung: effect of iterative reconstruction techniques on image quality. Acad Radiol. 2014 Jun;21(6):695-703. doi: 10.1016/j.acra.2014.01.023. Epub 2014 Apr 6.
PubMed 24713541
Study at a glance
From the registry record, last updated by the sponsor on 1 Jun 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Utilization and Effectiveness of Ultra-low-dose Chest Computed Tomography Using Innovative CT Denoising Solution Based on Deep Learning Technology
- ClinicalTrials.gov ID
- NCT05398887
- Acronym / sponsor ID
- IMC20220515-01
- Conditions
- Lung Diseases
- Keywords
- Computed tomography, Artificial Intelligence, Denoising technique, Ultra-low-dose Computed tomography
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Intermed HospitalUniversity, hospital or other
- Study start
- 15 Jun 2022 (estimated)
- Primary completion
- 1 Sep 2022 (estimated)
- Study completion
- 1 Oct 2022 (estimated)
- First posted
- 1 Jun 2022 (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.
The main objective of the study is to evaluate the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with innovative vendor-neutral CT denoising solution based on deep learning technology.
Detailed description
Considering lung cancer-related public health challenges, a reliable lung cancer screening method for high-risk cohorts in Mongolia is needed. Thus, our study aims to assess the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with artificial intelligence based CT denoising technique among various patient groups.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Quadruple blind (participant, care provider, investigator, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Low dose Chest CT scan | Active comparator | Radiation: Low radiation dose CTUnderwent low dose chest CT with 30% lower radiation dose Interventions: Radiation: Low radiation dose CT Other: Image quality analysis |
| Ultra low dose CT scan with Artificial Intelligence | Experimental | Radiation: Underwent ultra dose chest CT, Other: Artificial Intelligence based modelInterventions: Radiation: Low radiation dose CT Image quality Other: Deep-learning based contrast boosting algorithms |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Patients aged over 18-year-old
- Patients undergoing CT Chest for all purpose
Key exclusion criteria (4 listed)
- Age less than 18 years
- Any suspicion of pregnancy
- History of thoracic surgery or placement of the metallic device in the thorax
- An inability to hold respiration during 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 |
|---|---|---|
| Low radiation dose CTUnderwent low dose chest CT with 30% lower radiation dose | Radiation | Low dose Chest CT scan |
| Underwent ultra dose chest CTUnderwent ultra dose chest CT with 90% lower radiation dose | Radiation | Ultra low dose CT scan with Artificial Intelligence |
| Artificial Intelligence based modelDeep-learning based contrast boosting algorithms | Other | Ultra low dose CT scan with Artificial Intelligence |
Primary outcome
Detection rate of pulmonary conditions
Time frame: Within 2 weeks after data collection
Pulmonary condition detection rate on low dose chest CT and ultra dose chest CT with artificial intelligence-based CT denoising solution by blinded reviewers
Contrast media dose
Time frame: Within 2 weeks after data collection
Administered contrast media dose in each patient
1 secondary outcome
Image contrast
Time frame: Within 2 weeks after data collection
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Jun 2022.
- BackgroundInternational Agency for Research on Cancer. Global Cancer Observatory: cancer today. World Health Organization. https://gco.iarc.fr/today (accessed Feb 14, 2022).
- BackgroundHealth Development Center, WHO. Health Indicators 2019. Mongolian Health Development Center. http://hdc.gov.mn/media/uploads/202108/Eruul_mendiin_uzuulelt_2020.pdf (accessed Feb 14, 2022).
- BackgroundWHO global report. WHO global report on mortality attributable to tobacco. 2012
- Background: PubMed 24756146Zheng W, McLerran DF, Rolland BA, Fu Z, Boffetta P, He J, Gupta PC, Ramadas K, Tsugane S, Irie F, Tamakoshi A, Gao YT, Koh WP, Shu XO, Ozasa K, Nishino Y, Tsuji I, Tanaka H, Chen CJ, Yuan JM, Ahn YO, Yoo KY, Ahsan H, Pan WH, Qiao YL, Gu D, Pednekar MS, Sauvaget C, Sawada N, Sairenchi T, Yang G, Wang R, Xiang YB, Ohishi W, Kakizaki M, Watanabe T, Oze I, You SL, Sugawara Y, Butler LM, Kim DH, Park SK, Parvez F, Chuang SY, Fan JH, Shen CY, Chen Y, Grant EJ, Lee JE, Sinha R, Matsuo K, Thornquist M, Inoue M, Feng Z, Kang D, Potter JD. Burden of total and cause-specific mortality related to tobacco smoking among adults aged >/= 45 years in Asia: a pooled analysis of 21 cohorts. PLoS Med. 2014 Apr 22;11(4):e1001631. doi: 10.1371/journal.pmed.1001631. eCollection 2014 Apr.
- BackgroundFourth national STEPS Survey on the Prevalence of Noncommunicable Disease and Injury Risk Factors-2019. World Health Organization.
- Background: PubMed 20008690Smith-Bindman R, Lipson J, Marcus R, Kim KP, Mahesh M, Gould R, Berrington de Gonzalez A, Miglioretti DL. Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch Intern Med. 2009 Dec 14;169(22):2078-86. doi: 10.1001/archinternmed.2009.427.
- Background: PubMed 21714641National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29.
- Background: PubMed 31995683de Koning HJ, van der Aalst CM, de Jong PA, Scholten ET, Nackaerts K, Heuvelmans MA, Lammers JJ, Weenink C, Yousaf-Khan U, Horeweg N, van 't Westeinde S, Prokop M, Mali WP, Mohamed Hoesein FAA, van Ooijen PMA, Aerts JGJV, den Bakker MA, Thunnissen E, Verschakelen J, Vliegenthart R, Walter JE, Ten Haaf K, Groen HJM, Oudkerk M. Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. N Engl J Med. 2020 Feb 6;382(6):503-513. doi: 10.1056/NEJMoa1911793. Epub 2020 Jan 29.
- Background: PubMed 25551510Boyd MA. U.S. radiation protection: role of national and international recommendations and opportunities for collaboration (harmony, not dissonance). Health Phys. 2015 Feb;108(2):278-82. doi: 10.1097/HP.0000000000000236.
- Background: PubMed 23344517Katsura M, Matsuda I, Akahane M, Yasaka K, Hanaoka S, Akai H, Sato J, Kunimatsu A, Ohtomo K. Model-based iterative reconstruction technique for ultralow-dose chest CT: comparison of pulmonary nodule detectability with the adaptive statistical iterative reconstruction technique. Invest Radiol. 2013 Apr;48(4):206-12. doi: 10.1097/RLI.0b013e31827efc3a.
- Background: PubMed 26001228Kim Y, Kim YK, Lee BE, Lee SJ, Ryu YJ, Lee JH, Chang JH. Ultra-Low-Dose CT of the Thorax Using Iterative Reconstruction: Evaluation of Image Quality and Radiation Dose Reduction. AJR Am J Roentgenol. 2015 Jun;204(6):1197-202. doi: 10.2214/AJR.14.13629.
- Background: PubMed 24442444Lee SW, Kim Y, Shim SS, Lee JK, Lee SJ, Ryu YJ, Chang JH. Image quality assessment of ultra low-dose chest CT using sinogram-affirmed iterative reconstruction. Eur Radiol. 2014 Apr;24(4):817-26. doi: 10.1007/s00330-013-3090-9. Epub 2014 Jan 18.
- 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.
Khulan Khurelsukh, M.D, MSc study chair
Intermed Hospital
Delgerekh Sainjargal, M.D, MSc principal investigator
Intermed Hospital
Bayarbaatar Bold, M.D principal investigator
Intermed Hospital
Questions about this trial
What is this clinical trial studying?
A study testing Low radiation dose CT, Underwent ultra dose chest CT and Artificial Intelligence based model in people with Lung Diseases, run by Intermed Hospital.
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 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: status not confirmed, as of the sponsor's last update on 1 Jun 2022. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Bayarbaatar Bold, M.D (976-99063486, bayarbaatar99@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Detection rate of pulmonary conditions (time frame: Within 2 weeks after data collection). The study lists 2 primary outcomes in all. 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 NCT05398887, which also lists 19 linked publications.
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 1 Jun 2022.