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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Fang-Yun Xie
- Phone Number: +8602087342926
- Email: xiefy@sysucc.org.cn
Study Contact Backup
- Name: Pu-Yun OuYang
- Phone Number: +8602087342926
- Email: ouyangpy@sysucc.org.cn
Study Locations
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-
Guangdong
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Guangzhou, Guangdong, China, 510060
- Sun Yat-sen University Cancer Center
-
Contact:
- Fang-Yun Xie
- Phone Number: +8602087342926
- Email: xiefy@sysucc.org.cn
-
Contact:
- Pu-Yun OuYang
- Phone Number: +8602087342926
- Email: ouyangpy@sysucc.org.cn
-
Principal Investigator:
- Fang-Yun Xie
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- 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
Exclusion Criteria:
- Patients are enrolled in this study for a specific magnetic resonance imaging scan and not for subsequent follow-up magnetic resonance imaging scans.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
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.
|
An artificial intelligence model predicts the risk and contours of local recurrence for MR images and triages them before radiologists interpret them.
|
|
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 the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
sensitivity
Time Frame: through study completion, an average of 2 years
|
through study completion, an average of 2 years
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
specificity
Time Frame: through study completion, an average of 2 years
|
through study completion, an average of 2 years
|
|
positive predictive value
Time Frame: through study completion, an average of 2 years
|
through study completion, an average of 2 years
|
|
negative predictive value
Time Frame: through study completion, an average of 2 years
|
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
|
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
|
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
|
through study completion, an average of 2 years
|
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the sensitivity in the subgroups of different rT-stage
Time Frame: through study completion, an average of 2 years
|
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
|
through study completion, an average of 2 years
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Fang-Yun Xie, Sun Yat-sen University
Publications and helpful links
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Pathologic Processes
- Neoplasms by Histologic Type
- Neoplasms
- Neoplasms by Site
- Carcinoma
- Neoplasms, Glandular and Epithelial
- Disease Attributes
- Pharyngeal Neoplasms
- Otorhinolaryngologic Neoplasms
- Head and Neck Neoplasms
- Nasopharyngeal Diseases
- Pharyngeal Diseases
- Stomatognathic Diseases
- Otorhinolaryngologic Diseases
- Nasopharyngeal Neoplasms
- Nasopharyngeal Carcinoma
- Recurrence
Other Study ID Numbers
Other Study ID Numbers
- B2024-039-01
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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