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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
| |
| 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. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
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| Measure | Description | Time Frame |
|---|---|---|
| sensitivity | through study completion, an average of 2 years |
| Measure | Description | Time Frame |
|---|---|---|
| specificity | through study completion, an average of 2 years | |
| positive predictive value | through study completion, an average of 2 years | |
| negative predictive value |
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Inclusion Criteria:
Exclusion Criteria:
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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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Fang-Yun Xie | Contact | +8602087342926 | xiefy@sysucc.org.cn | |
| Pu-Yun OuYang | Contact | +8602087342926 | ouyangpy@sysucc.org.cn |
| Name | Affiliation | Role |
|---|---|---|
| Fang-Yun Xie | Sun Yat-sen University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sun Yat-Sen University Cancer Center | Guangzhou | Guangdong | 510060 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37680944 | Background | 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. |
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| 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 |
| the detection rate of local recurrence in the AI-supported reading group | through study completion, an average of 2 years |
| the sensitivity in the subgroups of different rT-stage | through study completion, an average of 2 years |
| the incidence of cases whose recurrent risks and contours cannot be provided by the AI model | through study completion, an average of 2 years |