Clinical trial NCT06831357: Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
Official title: 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
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Nasopharyngeal Cancinoma (NPC)
Distant Metastasis
Keywords: Nasopharyngeal Cancinoma (NPC)
Distant Metastasis
PET/CT
MRI
whole slide imaging
deep learning model
Lead sponsor: Sun Yat-sen University
Sponsor class: OTHER
Collaborators: name: First Affiliated Hospital, Sun Yat-Sen University
name: Fifth Affiliated Hospital, Sun Yat-Sen University
name: Affiliated Cancer Hospital & Institute of Guangzhou Medical University
name: The Affiliated Panyu Center Hospital of Guangzhou Medical University
name: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
name: Qingyuan People's Hospital
First posted: 2025-02-18
Enrollment: 500
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with pathologically confirmed nasopharyngeal carcinoma

Brief 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.

Detailed description: An AI model was constructed based on HE-stained pathological sections of the primary lesion and MRI of the nasopharynx and neck to predict the probability of distant metastasis at the first visit, and the AI model was fully verified by multicenter data; the AI model was applied to T3-4 or N2-3 patients who were recommended to undergo PET/CT examination according to the NCCN and CSCO guidelines, and the threshold of the AI model when the negative predictive value for predicting M0 was not less than 95% was determined, providing theoretical support for patients predicted by AI to be exempted from PET/CT examination.

Eligibility criteria: 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.).

Primary outcomes:
measure: Negative predictive value
timeFrame: through study completion, an average of 2 year
Secondary outcomes:
measure: Sensitivity, specificity, and positive predictive value
timeFrame: through study completion, an average of 2 year
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Department of Radiation Oncology, Sun Yat-sen University Cancer Center
state: Guangdong
city: Guangzhou
country: China
facility: Sun Yat-sen University Cancer Center
state: Guangdong
Study officials:
References:
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