Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasop
An AI model was developed to predict the likelihood of distant metastasis in patients with nasopharyngeal cancer based on pathology slides and MRI scans of the primary tumor. The model was validated using data from multiple centers. It was then applied to patients with advanced stages who were recommended to undergo PE
| Condition(s) | Nasopharyngeal Cancinoma (NPC), Distant Metastasis |
|---|---|
| Status | Recruiting |
| Study type | Observational |
| Summary | An AI model was developed to predict the likelihood of distant metastasis in patients with nasopharyngeal cancer based on pathology slides and MRI scans of the primary tumor. The model was validated using data from multiple centers. It was then applied to patients with advanced stages who were recommended to undergo PET/CT scans based on the NCCN or CSCO guidelines. This AI model can accurately screen patients with high risk of distant metastasis at the time of initial diagnosis to receive PET/CT, avoid excessive examination of patients with low risk of distant metastasis, save medical resources and reduce the economic burden on patients. |
| Who can participate | Inclusion Criteria: A. The primary lesion was pathologically confirmed as nasopharyngeal carcinoma (WHO classification is I, II and III); B. The stage was T3-4 or N2-3, and the nasopharynx + neck MRI plain scan and enhanced scan were performed to confirm the nasopharyngeal and cervical lymph node lesions, and PET/CT or conventional examination (chest CT plain scan + enhanced scan, upper abdominal CT or MRI plain scan + enhanced scan or abdominal color Doppler ultrasound or ultrasound angiography, and whole body bone imaging) was performed to screen for distant metastases. Exclusion Criteria: Previous history of other malignant tumors (such as other head and neck squamous cell carcinomas, thyroid cancer, breast cancer, esophageal cancer, etc.). |
| Sex | All |
| Lead sponsor | Sun Yat-sen University |
| Locations | Guangzhou, Guangdong, China; Guangzhou, Guangdong, China |
| Start date | 2025-02-15 |
| NCT ID | NCT06831357 |
| Official listing | https://clinicaltrials.gov/study/NCT06831357 |