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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| maligant | Thyroid nodules with surgical or puncture biopsy-confirmed pathological findings of malignancy in the TI-RADS4 category | ||
| benign | Thyroid nodules with surgical or puncture biopsy-confirmed pathological findings of benign TI-RADS4 category |
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| Measure | Description | Time Frame |
|---|---|---|
| deep learning prediction model（YOLOv3） and the model evaluation | 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. | Immediately evaluated after the prediction model was built |
| nomogram prediction and assessment | 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 | Immediately evaluated after the nomogram was built |
| Selection of clinical features and assessment | 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. | After the dataset is collected and pathology results are obtained, the statistical results obtained are analyzed for clinical factors, averaging about 1 year. |
| Impact and assessment of ultrasound grading | 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. | 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. |
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Inclusion Criteria:
Exclusion Criteria:
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The study collected data on a total of 500 TI-RADS category 4 thyroid nodules from 500 patients who attended the First Affiliated Hospital of Shandong First Medical University from April 2022 to November 2023.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| QianfoshanH | Jinan | Shandong | China |
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| ID | Term |
|---|---|
| D016606 | Thyroid Nodule |
| ID | Term |
|---|---|
| D013964 | Thyroid Neoplasms |
| D004701 | Endocrine Gland Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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| D006258 |
| Head and Neck Neoplasms |
| D004700 | Endocrine System Diseases |
| D013959 | Thyroid Diseases |