Patients With Nasopharyngeal CarcinomaNCT05112510
Prognostic Prediction of NPC Based on MR Diffusion-weighted Imaging
An observational study of Patients With Nasopharyngeal Carcinoma, run by Fifth Affiliated Hospital, Sun Yat-Sen University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Patients With Nasopharyngeal Carcinoma
- What is being tested?
- Observing whether developing distant metastasis or recurrence
- How many people
- 125 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2021to 1 Jun 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: patient with nasopharyngeal carcinoma
Who may be able to join
- patients with nasopharyngeal carcinoma diagnosed by pathology;
- complete clinical data and MR imaging data;
- without radiotherapy, chemotherapy or operation before MR examination.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
The Fifth Affiliated Hospital of Sun Yat-sen University
Zhuhai, Guangdong, 519000
About this study
Official title: Prognostic Prediction of Nasopharyngeal Carcinoma Based on Radiomics Features of MR Diffusion-weighted Imaging
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
- Being tested
- Observing whether developing distant metastasis or recurrence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Observing whether developing distant metastasis or recurrenceOther
The study is a observational study and has no intervention.
| Study group | Role | What they receive |
|---|---|---|
| event group | The patients with nasopharyngeal carcinoma developed distant metastasis or recurrence after standard treatment. Receives: Other: Observing whether developing distant metastasis or recurrence | |
| non-event group | The patients with nasopharyngeal carcinoma did not develop distant metastasis or recurrence after standard treatment. Receives: Other: Observing whether developing distant metastasis or recurrence |
What is being measured
The primary outcome is the main question the study is designed to answer.
Calculating AUC of machine learning model based on MR diffusion-weighted imaging to evaluate efficacy for prognosis
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).
Measured: Before January 2022
Secondary outcomes (2)
Comparing AUC of machine learning model based on MR diffusion-weighted imaging and conventional MR sequences for prognosis
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.
Measured: 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
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.
Measured: Before January 2022
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Nov 2021
- Study start1 Jun 2021
- Primary completion1 Mar 2022 (estimated)
- Study completion1 Jun 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Patients With Nasopharyngeal CarcinomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 125 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fifth Affiliated Hospital, Sun Yat-Sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05112510
- Sponsor's protocol ID
- ZDWY.HYXK.003
- Status last verified
- Oct 2021
- First submitted
- 17 Oct 2021
- Last updated
- 9 Nov 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Nov 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prognostic Prediction of Nasopharyngeal Carcinoma Based on Radiomics Features of MR Diffusion-weighted Imaging
- ClinicalTrials.gov ID
- NCT05112510
- Acronym / sponsor ID
- ZDWY.HYXK.003
- Conditions
- Patients With Nasopharyngeal Carcinoma
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 125 participants (estimated)
- Lead sponsor
- Fifth Affiliated Hospital, Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 1 Jun 2021 (actual)
- Primary completion
- 1 Mar 2022 (estimated)
- Study completion
- 1 Jun 2022 (estimated)
- First posted
- 9 Nov 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| event group | – | Other: Observing whether developing distant metastasis or recurrenceThe patients with nasopharyngeal carcinoma developed distant metastasis or recurrence after standard treatment. |
| non-event group | – | Other: Observing whether developing distant metastasis or recurrenceThe patients with nasopharyngeal carcinoma did not develop distant metastasis or recurrence after standard treatment. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- patient with nasopharyngeal carcinoma
Key inclusion criteria (3 listed)
- patients with nasopharyngeal carcinoma diagnosed by pathology;
- complete clinical data and MR imaging data;
- without radiotherapy, chemotherapy or operation before MR examination.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Observing whether developing distant metastasis or recurrenceThe study is a observational study and has no intervention. | Other | event group, non-event group |
Primary outcome
Calculating AUC of machine learning model based on MR diffusion-weighted imaging to evaluate efficacy for prognosis
Time frame: 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).
2 secondary outcomes
Comparing AUC of machine learning model based on MR diffusion-weighted imaging and conventional MR sequences for prognosis
Time frame: 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
Time frame: Before January 2022
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Nov 2021.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Patients With Nasopharyngeal Carcinoma, run by Fifth Affiliated Hospital, Sun Yat-Sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 9 Nov 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Calculating AUC of machine learning model based on MR diffusion-weighted imaging to evaluate efficacy for prognosis (time frame: Before January 2022). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Nov 2021.