Prognostic Prediction of Nasopharyngeal Carcinoma Based on Radiomics Features of MR Diffusion-weighted Imaging
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 125
- 试验地点
- 1
- 主要终点
- Calculating AUC of machine learning model based on MR diffusion-weighted imaging to evaluate efficacy for prognosis
研究概览
简要总结
The purpose of this study is to explore whether the imaging model based on RESOLVE-DWI sequence can exploiting the heterogeneity of nasopharyngeal carcinoma and indicate the prognosis, so as to provide intervention information for clinical decision-making. All patients were randomly divided into the training group and the validation group. Radiomics features extracted from T2-weighted, DWI, apparent diffusion coefficient (ADC), and contrast- enhanced T1-weighted were used to build a radiomics model. Patients'clinical variables were also obtained to build a clinical model. Model of training cohort was established using cross-validation for nasopharyngeal carcinoma prognosis by machine learning, including Logistics Regression, SVM, KNN, Decision Tree, Random Forest, XGBoost, and then, the model will be verified in the validation cohort. Area under the curve (AUC) of the Machine learning model was used as the main evaluation metric.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •patients with nasopharyngeal carcinoma diagnosed by pathology;
- •complete clinical data and MR imaging data;
- •without radiotherapy, chemotherapy or operation before MR examination.
排除标准
- •incomplete follow-up data;
- •poor image quality and can not be used for analysis;
- •patients with other tumors in the past or at the same time.
结局指标
主要结局
Calculating AUC of machine learning model based on MR diffusion-weighted imaging to evaluate efficacy for prognosis
时间窗: Before January 2022
After building machine learning model based on the features extracted by MR diffusion-weighted imaging of patients with nasopharyngeal carcinoma. Some measurements will be output from machine learning model such as AUC、F1、Accuracy and so on. Area under the curve (AUC) of the Machine learning model will be used as the main evaluation metric to evaluate the efficacy of a machine learning model which is used to predict the prognosis of patients with nasopharyngeal carcinoma (NPC).
次要结局
- Comparing AUC of machine learning model based on MR diffusion-weighted imaging and conventional MR sequences for prognosis(Before January 2022)
- Calculating AUC of machine learning model based on MR diffusion-weighted imaging combinated with conventional MR sequence to evaluate efficacy for prognosis(Before January 2022)