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Not yet recruiting NCT07299318

Multimodal Deep Learning for Lymph Node Metastasis in Thyroid Cancer

— Observational Papillary Thyroid Carcinoma

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: not intervention.
Who it may be relevant to
Registry conditions: Papillary Thyroid Carcinoma. Basic parameters: 18 years — 80 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Official title

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

Overview

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.

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.

Primary outcome measures

  • Area Under the Receiver Operating Characteristic Curve for a Multimodal Deep Learning Model Based on Cervical Ultrasound in Predicting Lymph Node Metastasis [Time frame: Within 2 months after the completion of subject enrollment]
  • Sensitivity of a Multimodal Deep Learning Model Based on Cervical Ultrasound for Predicting Lymph Node Metastasis [Time frame: Within 2 months after the completion of subject enrollment.]
  • Specificity of a Multimodal Deep Learning Model Based on Cervical Ultrasound for Predicting Lymph Node Metastasis [Time frame: Within 2 months after the completion of subject enrollment.]
Secondary outcome measures (3)
  • The pathologically confirmed lymph node metastasis rate in the study cohort [Time frame: Within 2 months after the completion of subject enrollment]
  • Adjusted Odds Ratios for Clinical Factors Associated with Pathologically Confirmed Lymph Node Metastasis [Time frame: Within 2 months after the completion of subject enrollment]
  • The weighted Kappa coefficient for the consistency between model-predicted pTNM stage and pathological stage [Time frame: Within 2 months after the completion of subject enrollment]

Eligibility criteria

Inclusion criteria

Cases aged 18-80 years who underwent thyroid ultrasound examination and postoperative pathological examination of the thyroid.

Cases with a first-time diagnosis of papillary thyroid carcinoma. Cases who underwent lymph node dissection

Exclusion criteria

Cases aged <18 years or >80 years. Cases with poor-quality ultrasound images. Cases with incompletely visualized nodules. Cases with images showing multiple distinct lesions. Cases belonging to special populations. Cases with concurrent other tumors. Cases with a history of thyroid cancer resection

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

China · 1 center
  • West China hospital of Sichuan University — Chengdu

Identifiers

NCT: NCT07299318 · 2025(2352)

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗