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

NCT06356441 | Not Yet Recruiting

• 1 other identifier: B2024-039-01
• Study Type: observational
• Target: 10,400
• Locations: 1 country
• Sites: 1

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.

Conditions

Artificial Intelligence Supported Image Reviewing

Keywords

artificial intelligence; image reviewing; magnetic resonance imaging

Outcome Measures

Primary Outcomes (1)

• sensitivity

  through study completion, an average of 2 years

Secondary Outcomes (8)

• specificity

  through study completion, an average of 2 years

• positive predictive value

  through study completion, an average of 2 years

• negative predictive value

  through study completion, an average of 2 years

• total time of interpretation for all the MR images

  through study completion, an average of 2 years

• the rate of discussion with a third radiologist

  through study completion, an average of 2 years

Study Arms (2)

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.

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.

Interventions

AI DIAGNOSTIC_TEST

An artificial intelligence model predicts the risk and contours of local recurrence for MR images and triages them before radiologists interpret them.

AI-supported reading

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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

You may not qualify if:

• Patients are enrolled in this study for a specific magnetic resonance imaging scan and not for subsequent follow-up magnetic resonance imaging scans.

Sponsors & Collaborators

• Sun Yat-sen University: lead

Study Sites (1)

Sun Yat-Sen University Cancer Center

Guangzhou, Guangdong, 510060, China

Location

Related Publications (1)

• 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.

  PMID: 37680944 BACKGROUND

Study Officials

• Fang-Yun Xie

  Sun Yat-sen University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Fang-Yun Xie

CONTACT

+8602087342926; xiefy@sysucc.org.cn

Pu-Yun OuYang

CONTACT

+8602087342926; ouyangpy@sysucc.org.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: professor

Study Record Dates

• First Submitted: April 4, 2024
• First Posted: April 10, 2024
• Study Start: April 1, 2024
• Primary Completion: April 1, 2026
• Study Completion: April 1, 2026
• Last Updated: April 10, 2024

Record last verified: 2024-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)