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Multimodal Deep Learning for Lymph Node Metastasis in Thyroid Cancer

NCT07299318 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 3200

Last updated 2025-12-23

No results posted yet for this study

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

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.

Sponsors & Collaborators

  • West China Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-01-01
Primary Completion
2026-03-01
Completion
2026-05-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07299318 on ClinicalTrials.gov