Patients With Nasopharyngeal Carcinoma
Conditions
Brief summary
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.
Interventions
OTHERObserving whether developing distant metastasis or recurrence
The study is a observational study and has no intervention.
Sponsors
Fifth Affiliated Hospital, Sun Yat-Sen University
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
1. patients with nasopharyngeal carcinoma diagnosed by pathology; 2. complete clinical data and MR imaging data; 3. without radiotherapy, chemotherapy or operation before MR examination.
Exclusion criteria
1. incomplete follow-up data; 2. poor image quality and can not be used for analysis; 3. patients with other tumors in the past or at the same time.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Calculating AUC of machine learning model based on MR diffusion-weighted imaging to evaluate efficacy for prognosis | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Comparing AUC of machine learning model based on MR diffusion-weighted imaging and conventional MR sequences for prognosis | Before January 2022 | After separately building machine learning model based on the highly correlated features extracted by MR diffusion-weighted imaging and conventional MR sequences of patients with nasopharyngeal carcinoma. Some measurements will be output from the machine learning models 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 study if the prediction efficiency of the machine learning model based on the highly correlated features extracted by MR diffusion weighted imaging imaging is better than that of conventional MR sequences. |
| 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 | Fianlly, we build a machine learning model based on MR diffusion-weighted imaging combinated with conventional MR sequences from patients with nasopharyngeal carcinoma. Some measurements will be output from the machine learning models 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 explore whether the machine learning model established by imaging features of MR diffusion-weighted imaging and conventional MR sequence has best prediction efficiency comparing with the models mentioned above. |
Countries
China
Outcome results
None listed