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Prognostic Prediction of NPC Based on MR Diffusion-weighted Imaging

NCT05112510 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 125

Last updated 2021-11-09

No results posted yet for this study

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.

Conditions

  • Patients With Nasopharyngeal Carcinoma

Interventions

OTHER

Observing whether developing distant metastasis or recurrence

The study is a observational study and has no intervention.

Sponsors & Collaborators

  • Fifth Affiliated Hospital, Sun Yat-Sen University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-06-01
Primary Completion
2022-03-01
Completion
2022-06-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05112510 on ClinicalTrials.gov