Clinical trial · Observational
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Papillary Thyroid Carcinoma | Thyroid Gland Papillary Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| not intervention | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Papillary thyroid carcinoma group
- interventionNames
- Other: not intervention
Primary outcomes (3)
- measure
- Area Under the Receiver Operating Characteristic Curve for a Multimodal Deep Learning Model Based on Cervical Ultrasound in Predicting Lymph Node Metastasis
- timeFrame
- Within 2 months after the completion of subject enrollment
- description
- 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.
- measure
- Sensitivity of a Multimodal Deep Learning Model Based on Cervical Ultrasound for Predicting Lymph Node Metastasis
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
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
References
Publications (0)
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