The Application Value of Deep Learning-Based Nomograms in Benign-Malignant Discrimination of TI-RADS Category 4 Thyroid Nodules
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 500
- 试验地点
- 1
- 主要终点
- Selection of clinical features and assessment
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 23 Years 至 78 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Ultrasound-confirmed diagnosis of thyroid nodules that are classified as TI-RADS category
- •Availability of pathological results.
排除标准
- •Lack of pathological diagnosis.
- •History of thyroid surgery or other treatments.
- •Poor quality of ultrasound images of thyroid nodules.
- •Incomplete clinical and imaging data of the patient.
结局指标
主要结局
Selection of clinical features and assessment
时间窗: 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.
deep learning prediction model（YOLOv3） and the model evaluation
时间窗: 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
时间窗: 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
Impact and assessment of ultrasound grading
时间窗: 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.
次要结局
未报告次要终点
研究者
Ma Zhe
Director of Ultrasound
Qianfoshan Hospital