Clinical trial · Observational
The Application Value of Deep Learning-Based Nomograms in Benign-Malignant Discrimination of TI-RADS Category 4 Thyroid Nodules
- 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)
This retrospective study focuses on benign and malignant classification of thyroid nodules using deep learning techniques and evaluates the value of deep learning based nomograms in the classification of TI-RADS category 4 thyroid nodules to improve the accuracy of benign and malignant identification of TI-RADS category 4 thyroid nodules. Materials and methods: Patients who visited in The First Affiliated Hospital of Shandong First Medical University ＆ Shandong Provincial Qianfoshan Hospital were collected. Their general clinical features, information on preoperative ultrasound diagnosis, and postoperative pathologic data were reviewed.
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 |
|---|---|---|---|
| Thyroid Nodule | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (2)
- label
- maligant
- description
- Thyroid nodules with surgical or puncture biopsy-confirmed pathological findings of malignancy in the TI-RADS4 category
- label
- benign
- description
- Thyroid nodules with surgical or puncture biopsy-confirmed pathological findings of benign TI-RADS4 category
Primary outcomes (4)
- measure
- deep learning prediction model（YOLOv3） and the model evaluation
- timeFrame
- Immediately evaluated after the prediction model was built
- description
- Based on the characteristics of benign and malignant thyroid nodules, the dataset was divided into a training set and a test set using the cross-validation method, and the YOLOv3 model was trained using data from the training set, and the performance of the model was evaluated using data from the test set.The model is evaluated using a number of metrics such as: precision-recall curve, effective classification precision, confusion matrix and area under the curve.
- measure
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 23 Years
- Maximum age
- 78 Years
Show eligibility criteria text
Inclusion Criteria: 1. Ultrasound-confirmed diagnosis of thyroid nodules that are classified as TI-RADS category 4. 2. Availability of pathological results. Exclusion Criteria: 1. Lack of pathological diagnosis. 2. History of thyroid surgery or other treatments. 3. Poor quality of ultrasound images of thyroid nodules. 4. Incomplete clinical and imaging data of the patient.
References
Publications (0)
Data not yet available