Artificial Intelligence Supported Image ReviewingNCT06356441
Artificial Intelligence-supported Reading Versus Standard Double Reading for the Interpretation of Magnetic Resonance Imaging in the Detection of Local Recurrence for Nasopharyngeal Carcinoma: a Randomised Controlled Multicenter Study
An observational study of Artificial Intelligence Supported Image Reviewing, run by Sun Yat-sen University at 1 site in China.
Trial summary
The aim of this randomized controlled study is to investigate whether the previously developed artificial intelligence model can triage post-radiotherapy magnetic resonance images of patients with nasopharyngeal carcinoma and assist radiologists in their interpretation. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Artificial Intelligence Supported Image Reviewing
- What is being tested?
- AI
- How many people
- 10,400 participants
- Where
- 1 siteChina
- Study dates
- Apr 2024to Apr 2026
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: This study enrolled patients with treatment naive nasopharyngeal carcinoma who have finished radiotherapy for 6 months or more and have no tumor residue in previous examinations.
Who may be able to join
- Patients with treatment naive nasopharyngeal carcinoma who had finished radiotherapy for 6 months or more
- The previous magnetic resonance imaging examination had showed complete remission in the primary site
- Images are acquired using a 3T magnetic resonance imaging device, including unenhanced T1-weighted and T2-weighted sequences and contrast-enhanced T1-weighted sequences
Who cannot join
- Patients are enrolled in this study for a specific magnetic resonance imaging scan and not for subsequent follow-up magnetic resonance imaging scans.
Trial locations and contacts
1 site in 1 country.
China1 site
Sun Yat-Sen University Cancer Center
Guangzhou, Guangdong, 510060
Fang-Yun Xie, +8602087342926, xiefy@sysucc.org.cn
Pu-Yun OuYang, +8602087342926, ouyangpy@sysucc.org.cn
About this study
The aim of this randomized controlled study is to investigate whether the previously developed artificial intelligence model can triage post-radiotherapy magnetic resonance images of patients with nasopharyngeal carcinoma and assist radiologists in their interpretation.
- Being tested
- AI
- Keywords
- artificial intelligence, image reviewing, magnetic resonance imaging
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AIDiagnostic test
An artificial intelligence model predicts the risk and contours of local recurrence for MR images and triages them before radiologists interpret them.
| Study group | Role | What they receive |
|---|---|---|
| AI-supported reading | The AI model predicts the incidence of local recurrence. If the incidence is below 60%, one radiologist will interpret the MR images. If the incidence is above 60%, two radiologists will interpret the MR images. The radiologists will be provided with the predictive incidence and contours in their interpretation if desired. If two radiologists provide contradictory interpretations, a third radiologist will participate in the discussion to reach a consensus. Receives: Diagnostic Test: AI | |
| Standard double reading | The MR images will be interpreted by two radiologists, and in cases of disagreement, a third radiologist will be consulted to reach a consensus. |
What is being measured
The primary outcome is the main question the study is designed to answer.
sensitivity
Measured: through study completion, an average of 2 years
Secondary outcomes (8)
specificity
Measured: through study completion, an average of 2 years
positive predictive value
Measured: through study completion, an average of 2 years
negative predictive value
Measured: through study completion, an average of 2 years
total time of interpretation for all the MR images
Measured: through study completion, an average of 2 years
the rate of discussion with a third radiologist
Measured: through study completion, an average of 2 years
the detection rate of local recurrence in the AI-supported reading group
Measured: through study completion, an average of 2 years
the sensitivity in the subgroups of different rT-stage
Measured: through study completion, an average of 2 years
the incidence of cases whose recurrent risks and contours cannot be provided by the AI model
Measured: through study completion, an average of 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Apr 2024
- Study startApr 2024 (estimated)
- Primary completionApr 2026 (estimated)
- Study completionApr 2026 (estimated)
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 Artificial Intelligence Supported Image ReviewingStudies of the same condition, open and under way.
- More trials run by Sun Yat-sen UniversityThe 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
- 10,400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Fang-Yun Xie, Sun Yat-sen University
- Investigators
- Fang-Yun Xie, Principal investigator, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06356441
- Sponsor's protocol ID
- B2024-039-01
- Status last verified
- Apr 2024
- First submitted
- 4 Apr 2024
- Last updated
- 10 Apr 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
OuYang PY, He Y, Guo JG, Liu JN, Wang ZL, Li A, Li J, Yang SS, Zhang X, Fan W, Wu YS, Liu ZQ, Zhang BY, Zhao YN, Gao MY, Zhang WJ, Xie CM, Xie FY. Artificial intelligence aided precise detection of local recurrence on MRI for nasopharyngeal carcinoma: a multicenter cohort study. EClinicalMedicine. 2023 Aug 30;63:102202. doi: 10.1016/j.eclinm.2023.102202. eCollection 2023 Sep.
PubMed 37680944
Study at a glance
From the registry record, last updated by the sponsor on 10 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-supported Reading Versus Standard Double Reading for the Interpretation of Magnetic Resonance Imaging in the Detection of Local Recurrence for Nasopharyngeal Carcinoma: a Randomised Controlled Multicenter Study
- ClinicalTrials.gov ID
- NCT06356441
- Acronym / sponsor ID
- B2024-039-01
- Keywords
- artificial intelligence, image reviewing, magnetic resonance imaging
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 10,400 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- Apr 2024 (estimated)
- Primary completion
- Apr 2026 (estimated)
- Study completion
- Apr 2026 (estimated)
- First posted
- 10 Apr 2024 (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 aim of this randomized controlled study is to investigate whether the previously developed artificial intelligence model can triage post-radiotherapy magnetic resonance images of patients with nasopharyngeal carcinoma and assist radiologists in their interpretation.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-supported reading | – | Diagnostic Test: AIThe AI model predicts the incidence of local recurrence. If the incidence is below 60%, one radiologist will interpret the MR images. If the incidence is above 60%, two radiologists will interpret the MR images. The radiologists will be provided with the predictive incidence and contours in their interpretation if desired. If two radiologists provide contradictory interpretations, a third radiologist will participate in the discussion to reach a consensus. |
| Standard double reading | – | –The MR images will be interpreted by two radiologists, and in cases of disagreement, a third radiologist will be consulted to reach a consensus. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- This study enrolled patients with treatment naive nasopharyngeal carcinoma who have finished radiotherapy for 6 months or more and have no tumor residue in previous examinations.
Key inclusion criteria (3 listed)
- Patients with treatment naive nasopharyngeal carcinoma who had finished radiotherapy for 6 months or more
- The previous magnetic resonance imaging examination had showed complete remission in the primary site
- Images are acquired using a 3T magnetic resonance imaging device, including unenhanced T1-weighted and T2-weighted sequences and contrast-enhanced T1-weighted sequences
Key exclusion criteria (1 listed)
- Patients are enrolled in this study for a specific magnetic resonance imaging scan and not for subsequent follow-up magnetic resonance imaging scans.
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 |
|---|---|---|
| AIAn artificial intelligence model predicts the risk and contours of local recurrence for MR images and triages them before radiologists interpret them. | Diagnostic test | AI-supported reading |
Primary outcome
sensitivity
Time frame: through study completion, an average of 2 years
8 secondary outcomes
specificity
Time frame: through study completion, an average of 2 years
positive predictive value
Time frame: through study completion, an average of 2 years
negative predictive value
Time frame: through study completion, an average of 2 years
total time of interpretation for all the MR images
Time frame: through study completion, an average of 2 years
the rate of discussion with a third radiologist
Time frame: through study completion, an average of 2 years
the detection rate of local recurrence in the AI-supported reading group
Time frame: through study completion, an average of 2 years
the sensitivity in the subgroups of different rT-stage
Time frame: through study completion, an average of 2 years
the incidence of cases whose recurrent risks and contours cannot be provided by the AI model
Time frame: through study completion, an average of 2 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Apr 2024.
- Background: PubMed 37680944OuYang PY, He Y, Guo JG, Liu JN, Wang ZL, Li A, Li J, Yang SS, Zhang X, Fan W, Wu YS, Liu ZQ, Zhang BY, Zhao YN, Gao MY, Zhang WJ, Xie CM, Xie FY. Artificial intelligence aided precise detection of local recurrence on MRI for nasopharyngeal carcinoma: a multicenter cohort study. EClinicalMedicine. 2023 Aug 30;63:102202. doi: 10.1016/j.eclinm.2023.102202. eCollection 2023 Sep.
- 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.
Fang-Yun Xie principal investigator
Sun Yat-sen University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence Supported Image Reviewing, run by Sun Yat-sen University at 1 site in China.
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 3 inclusion and 1 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 10 Apr 2024. 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 China. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Fang-Yun Xie (+8602087342926, xiefy@sysucc.org.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
sensitivity (time frame: through study completion, an average of 2 years). 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 NCT06356441, 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 10 Apr 2024.