Deep Learning Model for Diagnosis and Contour of Cervical Lymph Node for Nasopharyngeal Carcinoma

February 25, 2022 updated by: Fang-Yun Xie, Sun Yat-sen University

Magnetic Resonance Imaging Based Deep Learning Model for Diagnosis and Contour of Cervical Lymph Node for Nasopharyngeal Carcinoma: a Multicenter Study

The diagnosis of cervical lymph node in nasopharyngeal carcinoma is difficult. Magnetic resonance imaging based deep learning model may be a noninvasive and rapid diagnostic method for cervical lymph node. Thus, the investigators aimed to develop and externally validate a deep learning model to assist in the diagnosis and localization of metastatic lymph nodes in nasopharyngeal carcinoma.

Study Overview

Status

Recruiting

Conditions

Study Type

Observational

Enrollment (Anticipated)

5000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510060
        • Recruiting
        • Sun Yat-sen University Cancer Center
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Nasopharyngeal carcinoma

Description

Inclusion Criteria:

  • Pathological diagnosis of nasopharyngeal carcinoma; Cervival lymph nodes confirmed by pathology

Exclusion Criteria:

  • a history of cancer

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
Sensitivity and specificity
Time Frame: 2022-12-31
2022-12-31

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sun Yat-sen University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 5, 2021

Primary Completion (Anticipated)

December 31, 2022

Study Completion (Anticipated)

December 31, 2022

Study Registration Dates

First Submitted

February 8, 2022

First Submitted That Met QC Criteria

February 8, 2022

First Posted (Actual)

February 9, 2022

Study Record Updates

Last Update Posted (Actual)

February 28, 2022

Last Update Submitted That Met QC Criteria

February 25, 2022

Last Verified

February 1, 2022

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • B2020-334

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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