Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
Development and Multicenter Validation of a Deep Learning Model Based on Whole Slide Imaging and Magnetic Resonance Imaging of the Nasopharynx and Lymph Nodes to Predict Distant Metastases at Diagnosis in Nasopharyngeal Carcinoma
Sun Yat-sen University
500 participants
Feb 15, 2025
OBSERVATIONAL
Conditions
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.
Eligibility
Inclusion Criteria1
- 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 Criteria1
- Previous history of other malignant tumors (such as other head and neck squamous cell carcinomas, thyroid cancer, breast cancer, esophageal cancer, etc.).
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Locations(2)
View Full Details on ClinicalTrials.gov
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NCT06831357