Thyroid Nodule
Conditions
Keywords
TI-RADS category 4 thyroid nodules, YOLOv3 model, nomograms
Brief summary
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.
Interventions
None listed
Sponsors
Ma Zhe
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
23 Years to 78 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| deep learning prediction model（YOLOv3） and the model evaluation | Immediately evaluated after the prediction model was built | 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. |
| nomogram prediction and assessment | Immediately evaluated after the nomogram was built | Factoring clinical features, ultrasound grading and model predictions to map nomograms using R language.Evaluation of the nomogram using various metrics, including subject operating characteristic curves, calibration curves and decision curve analysis |
| Selection of clinical features and assessment | After the dataset is collected and pathology results are obtained, the statistical results obtained are analyzed for clinical factors, averaging about 1 year. | The researchers selected patients with TI-RADS category 4 thyroid nodules within 1 year to comprise the dataset. The researchers analyzed the clinical factors in the dataset and analyzed the significance of these clinical factors on the statistical results and clinical characteristics using the Wilcoxon two-sample rank sum test or chi-square test. |
| Impact and assessment of ultrasound grading | The graded results of the ultrasound examination were analyzed after the data set collection was completed, the ultrasound examination was completed and the final pathology results were obtained, on average about 1 year. | The researchers selected patients with TI-RADS category 4 thyroid nodules within 1 year to comprise the dataset. The researchers analyzed the results of grading TI-RADS category 4 nodules in this dataset and determined the significance of ultrasound grading on the statistical results using the chi-square test. |
Countries
China
Outcome results
None listed