Deep Learning Model for Diagnosis and Contour of Cervical... | NCT05231616

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

NCT05231616

Sponsor

Sun Yat-sen University

Status

Unknown status

Last Update Posted

Feb 28, 2022Actual

Enrollment

5,000Estimated

Phase

Not provided

Conditions

Deep Learning Model

Interventions

Not provided

Countries

China

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Feb 2022

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Recruiting

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jan 5, 2021Actual

Primary Completion Date

Dec 31, 2022Estimated

Completion Date

Dec 31, 2022Estimated

First Submitted Date

Feb 8, 2022

First Submission Date that Met QC Criteria

Feb 8, 2022

First Posted Date

Feb 9, 2022Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Feb 25, 2022

Last Update Posted Date

Feb 28, 2022Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Not provided

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief Summary

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.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

5,000Estimated

Measure	Description	Time Frame
Sensitivity and specificity		2022-12-31

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

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

Exclusion Criteria:

a history of cancer

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

Not provided

Maximum Age

Not provided

Standard Ages

ChildAdultOlder Adult

Study Population

Nasopharyngeal carcinoma

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Fang-Yun Xie, Professor	Contact	+86-20-87342618	2618	xiefy@sysucc.org.cn

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Sun Yat-sen University Cancer Center	Recruiting	Guangzhou	Guangdong	510060	China