NCT05187923

Brief Summary

The aim of this study was to evaluate the diagnostic efficacy of computer aided diagnostic tool for neck masses using machine learning and deep learning techniques on clinical information and radiological images in children.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
1,500

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jan 2021

Longer than P75 for all trials

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

January 1, 2021

Completed
12 months until next milestone

First Submitted

Initial submission to the registry

December 24, 2021

Completed
19 days until next milestone

First Posted

Study publicly available on registry

January 12, 2022

Completed
3 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 31, 2024

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2024

Completed
Last Updated

January 27, 2022

Status Verified

January 1, 2022

Enrollment Period

4 years

First QC Date

December 24, 2021

Last Update Submit

January 11, 2022

Conditions

Neck MassThyroglossal Duct CystsBranchial Cleft AnomaliesDermoid and Epidermoid CystsInfantile HemangiomasTeratomas

Outcome Measures

Primary Outcomes (1)

  • The diagnostic accuracy of neck masses with AI-based screening tools in children

    The diagnostic accuracy of neck masses with AI-based screening tools in children.

    1 month

Secondary Outcomes (4)

  • The diagnostic sensitivity of neck masses with AI-based screening tools in children

    1 month

  • The diagnostic specificity of neck masses with AI-based screening tools in children

    1 month

  • The diagnostic positive predictive value of neck masses with AI-based screening tools in children

    1 month

  • The diagnostic negative predictive value of neck masses with AI-based screening tools in children

    1 month

Study Arms (2)

Retrospective cohort

The internal cohort was retrospectively enrolled in West China Hospital, Sichuan University from June 2010 and December 2020. It is a training and internal validation cohort.

Diagnostic Test: Artificial Intelligence Algorithm

Prospective cohort

The same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort.

Diagnostic Test: Artificial Intelligence Algorithm

Interventions

Artificial Intelligence AlgorithmDIAGNOSTIC_TEST

Different machine learning and deep learning computer aided strategies for model construction and validation.

Prospective cohortRetrospective cohort

Eligibility Criteria

Age0 Years - 18 Years
Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64)
Sampling MethodNon-Probability Sample
Study Population

Patients who were found neck masses, and had completed clinical information and radiological images before operation, biopsy, neoadjuvant chemotherapy, and radiotherapy.

You may qualify if:

  • Age up to 18 years old
  • Receiving no treatment before diagnosis
  • With written informed consent

You may not qualify if:

  • Clinical data missing
  • Unavailable radiological images
  • Without written informed consent

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

West China Hospital, Sichuan University

Chengdu, Sichuan, 6100041, China

RECRUITING

MeSH Terms

Conditions

Thyroglossal CystBranchial Cleft AnomaliesDermoid CystEpidermal CystTeratoma

Condition Hierarchy (Ancestors)

CystsNeoplasmsNeoplasms, Germ Cell and EmbryonalNeoplasms by Histologic Type

Central Study Contacts

Yuhan Yang, MD

CONTACT

8613258389785yyh_1023@163.com

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
OTHER
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Associate Professor

Study Record Dates

First Submitted

December 24, 2021

First Posted

January 12, 2022

Study Start

January 1, 2021

Primary Completion

December 31, 2024

Study Completion

December 31, 2024

Last Updated

January 27, 2022

Record last verified: 2022-01

Data Sharing

IPD Sharing
Will not share

Locations

CN(1)