NCT07299318

Multimodal Deep Learning for Lymph Node Metastasis in Thyroid Cancer

A Multicenter Study on Developing a Multimodal Deep Learning Model Based on Color Doppler Ultrasound for Predicting Lymph Node Metastasis and Cancer Staging in Papillary Thyroid Carcinoma

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 3,200 (estimated)
• Sponsor: West China Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 80 Years
• Healthy volunteers: Not accepted

Summary

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy in clinical practice, accounting for approximately 85% of all thyroid malignancies. The occurrence of cervical lymph node metastasis further increases the risk of local tumor recurrence and distant metastasis, thereby reducing patient survival rates. Pathological examinations reveal that approximately 30-80% of PTC patients have lymph node metastasis. Early detection of metastatic lymph nodes and the development of individualized treatment plans are crucial for improving patient prognosis. Currently, the primary method for diagnosing lymph node metastasis is ultrasound-guided fine-needle aspiration, but its accuracy is limited by sample quality and carries a risk of false-negative results. In recent years, deep learning technology has demonstrated significant potential in the field of medical image analysis. Therefore, the investigators aim to develop a deep learning model based on neck ultrasound to more accurately predict lymph node metastasis.

Conditions

• Papillary Thyroid Carcinoma

Interventions

Type	Name	Description
OTHER	not intervention	This is a retrospective observational study in which participants will not undergo any interventions, and only data collection and analysis will be performed on the participants.

Timeline

• Start date: 2026-01-01
• Primary completion: 2026-03-01
• Completion: 2026-05-01
• First posted: 2025-12-23
• Last updated: 2025-12-23

Locations

1 site across 1 country: China