Clinical trial NCT06356441: 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
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
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Artificial Intelligence Supported Image Reviewing
Keywords: artificial intelligence
image reviewing
magnetic resonance imaging
Lead sponsor: Sun Yat-sen University
Sponsor class: OTHER
First posted: 2024-04-10
Enrollment: 10400
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
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.

Brief 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.


Eligibility criteria: 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.

Primary outcomes:
measure: sensitivity
timeFrame: through study completion, an average of 2 years
Secondary outcomes:
measure: specificity
timeFrame: through study completion, an average of 2 years
measure: positive predictive value
timeFrame: through study completion, an average of 2 years
measure: negative predictive value
timeFrame: through study completion, an average of 2 years
measure: total time of interpretation for all the MR images
timeFrame: through study completion, an average of 2 years
measure: the rate of discussion with a third radiologist
timeFrame: through study completion, an average of 2 years
measure: the detection rate of local recurrence in the AI-supported reading group
timeFrame: through study completion, an average of 2 years
measure: the sensitivity in the subgroups of different rT-stage
timeFrame: through study completion, an average of 2 years
measure: the incidence of cases whose recurrent risks and contours cannot be provided by the AI model
timeFrame: through study completion, an average of 2 years
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Sun Yat-Sen University Cancer Center
state: Guangdong
Study officials:
affiliation: Sun Yat-sen University
name: Fang-Yun Xie
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.