Multimodal Deep Learning for Lymph Node Metastasis in Thyroid Cancer

NCT07299318 | Not Yet Recruiting DMC

• 1 other identifier: 2025(2352)
• Study Type: observational
• Target: 3,200
• Locations: 1 country
• Sites: 1

Brief 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

Outcome Measures

Primary Outcomes (3)

• Area Under the Receiver Operating Characteristic Curve for a Multimodal Deep Learning Model Based on Cervical Ultrasound in Predicting Lymph Node Metastasis

  The researcher will employ a multimodal deep learning model that integrates preoperative cervical color Doppler ultrasound images with corresponding structured text reports. The final output of the model is a predicted probability of lymph node metastasis for each patient (a continuous value between 0 and 1). This predicted probability will be compared with postoperative histopathological diagnosis results (the gold standard). A receiver operating characteristic curve will be plotted for the model, and its area under the curve will be calculated.This is the gold standard metric for evaluating the discriminative ability of a binary classification model (metastasis vs. non-metastasis). A higher AUC value indicates stronger overall discriminative power of the model.

  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

  This metric aims to evaluate the ability of the constructed multimodal deep learning model to correctly identify patients with papillary thyroid carcinoma who truly have cervical lymph node metastasis, under the optimal diagnostic threshold. Researchers need to collect the number of patients diagnosed with lymph node metastasis through postoperative pathology, as well as the number of patients predicted as "positive" (i.e., predicted to have metastasis) by the model, in order to calculate the sensitivity of the cervical ultrasound-based multimodal deep learning model in predicting lymph node metastasis. Calculation formula: Sensitivity = Number of true positive patients / Total number of positive patients confirmed by postoperative pathology.

  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

  This metric aims to evaluate the ability of the constructed multimodal deep learning model to correctly rule out patients with papillary thyroid carcinoma who have not developed cervical lymph node metastasis, under the optimal diagnostic threshold. Researchers need to collect the number of patients diagnosed without lymph node metastasis via postoperative pathology, as well as the number of patients predicted by the model as "negative" (i.e., predicted to have no metastasis), in order to calculate the specificity of the cervical ultrasound-based multimodal deep learning model in predicting lymph node metastasis. Calculation formula: Specificity = Number of true negative patients / Total number of negative patients confirmed by postoperative pathology.

  Within 2 months after the completion of subject enrollment.

Secondary Outcomes (3)

• The pathologically confirmed lymph node metastasis rate in the study cohort

  Within 2 months after the completion of subject enrollment

• Adjusted Odds Ratios for Clinical Factors Associated with Pathologically Confirmed Lymph Node Metastasis

  Within 2 months after the completion of subject enrollment

• The weighted Kappa coefficient for the consistency between model-predicted pTNM stage and pathological stage

  Within 2 months after the completion of subject enrollment

Study Arms (1)

Papillary thyroid carcinoma group

Other: not intervention

Interventions

not intervention OTHER

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.

Papillary thyroid carcinoma group

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Clinical data from patients who underwent thyroidectomy at West China Hospital of Sichuan University and its affiliated branch hospitals between October 2020 and October 2025 were retrospectively collected and analyzed.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• West China Hospital: lead

Study Sites (1)

West China hospital of Sichuan University

Chengdu, Sichuan, 610041, China

Location

MeSH Terms

Conditions

Thyroid Cancer, Papillary

Condition Hierarchy (Ancestors)

Adenocarcinoma, Papillary; Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Thyroid Neoplasms; Endocrine Gland Neoplasms; Neoplasms by Site; Head and Neck Neoplasms; Endocrine System Diseases; Thyroid Diseases

Central Study Contacts

Jianyong Lei

CONTACT

+86 19983137992; leijianyong@scu.edu.cn

Yu Feng

CONTACT

1350502131@qq.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Doctorate

Study Record Dates

• First Submitted: December 9, 2025
• First Posted: December 23, 2025
• Study Start: January 1, 2026
• Primary Completion: March 1, 2026
• Study Completion: May 1, 2026
• Last Updated: December 23, 2025

Record last verified: 2025-12

Locations

CN (1)