Neck MassNCT05187923
Computer Aided Tool for Diagnosis of Neck Masses in Children
An observational study of Neck Mass and Thyroglossal Duct Cysts, run by West China Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 0 to 18 years
- Conditions the study is for
- Neck Mass, Thyroglossal Duct Cysts, Branchial Cleft Anomalies
- What is being tested?
- Artificial Intelligence Algorithm
- How many people
- 1,500 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2021to 31 Dec 2024
- 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 0 to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
Study population: Patients who were found neck masses, and had completed clinical information and radiological images before operation, biopsy, neoadjuvant chemotherapy, and radiotherapy.
Who may be able to join
- Age up to 18 years old
- Receiving no treatment before diagnosis
- With written informed consent
Who cannot join
- Clinical data missing
- Unavailable radiological images
- Without written informed consent
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
West China Hospital, Sichuan University
Chengdu, Sichuan, 6100041
Yuhan Yang, MD, 8613258389785, yyh_1023@163.com
About this study
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.
Read the detailed description
This study is a retrospective-prospective design by West China Hospital, Sichuan University, including clinical data and radiological images. A retrospective database was enrolled for patients with definite histological diagnosis and available radiological images from June 2010 and December 2020. The investigators have constructed deep learning and machine learning diagnostic models on this retrospective cohort and validated it internally. A prospective cohort would recruit patients found neck masses since January 2021. The proposed computer aided diagnostic models would also be validated in this prospective cohort externally. 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 data and radiological images in children.
- Conditions
- Neck Mass, Thyroglossal Duct Cysts, Branchial Cleft Anomalies, Dermoid and Epidermoid Cysts, Infantile Hemangiomas, Teratomas
- Being tested
- Artificial Intelligence Algorithm
- Medical subject headings
- Thyroglossal Cyst, Branchial Cleft Anomalies, Dermoid Cyst, Epidermal Cyst, Teratoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence AlgorithmDiagnostic test
Different machine learning and deep learning computer aided strategies for model construction and validation.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Diagnostic Test: Artificial Intelligence Algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 1 month
Secondary outcomes (4)
The diagnostic sensitivity of neck masses with AI-based screening tools in children
The diagnostic sensitivity of neck masses with AI-based screening tools in children.
Measured: 1 month
The diagnostic specificity of neck masses with AI-based screening tools in children
The diagnostic specificity of neck masses with AI-based screening tools in children.
Measured: 1 month
The diagnostic positive predictive value of neck masses with AI-based screening tools in children
The diagnostic positive predictive value of neck masses with AI-based screening tools in children.
Measured: 1 month
The diagnostic negative predictive value of neck masses with AI-based screening tools in children
The diagnostic negative predictive value of neck masses with AI-based screening tools in children
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jan 2022
- Study start1 Jan 2021
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Neck MassStudies of the same condition, open and under way.
- More trials run by West China HospitalThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Other
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- West China Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yuhan Yang, West China Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05187923
- Sponsor's protocol ID
- HX-20211023
- Status last verified
- Jan 2022
- First submitted
- 24 Dec 2021
- Last updated
- 27 Jan 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Jan 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Computer Aided Tool for Diagnosis of Neck Masses in Children
- ClinicalTrials.gov ID
- NCT05187923
- Acronym / sponsor ID
- HX-20211023
- Conditions
- Neck Mass, Thyroglossal Duct Cysts, Branchial Cleft Anomalies, Dermoid and Epidermoid Cysts, Infantile Hemangiomas, Teratomas
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- West China HospitalUniversity, hospital or other
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 12 Jan 2022 (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 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.
Detailed description
This study is a retrospective-prospective design by West China Hospital, Sichuan University, including clinical data and radiological images. A retrospective database was enrolled for patients with definite histological diagnosis and available radiological images from June 2010 and December 2020. The investigators have constructed deep learning and machine learning diagnostic models on this retrospective cohort and validated it internally. A prospective cohort would recruit patients found neck masses since January 2021. The proposed computer aided diagnostic models would also be validated in this prospective cohort externally. 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 data and radiological images in children.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Retrospective cohort | – | Diagnostic Test: Artificial Intelligence AlgorithmThe 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. |
| Prospective cohort | – | Diagnostic Test: Artificial Intelligence AlgorithmThe same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort. |
Who may qualify
- Ages
- 0 to 18 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- Study population
- Patients who were found neck masses, and had completed clinical information and radiological images before operation, biopsy, neoadjuvant chemotherapy, and radiotherapy.
Key inclusion criteria (3 listed)
- Age up to 18 years old
- Receiving no treatment before diagnosis
- With written informed consent
Key exclusion criteria (3 listed)
- Clinical data missing
- Unavailable radiological images
- Without written informed consent
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 |
|---|---|---|
| Artificial Intelligence AlgorithmDifferent machine learning and deep learning computer aided strategies for model construction and validation. | Diagnostic test | Prospective cohort, Retrospective cohort |
Primary outcome
The diagnostic accuracy of neck masses with AI-based screening tools in children
Time frame: 1 month
The diagnostic accuracy of neck masses with AI-based screening tools in children.
4 secondary outcomes
The diagnostic sensitivity of neck masses with AI-based screening tools in children
Time frame: 1 month
The diagnostic specificity of neck masses with AI-based screening tools in children
Time frame: 1 month
The diagnostic positive predictive value of neck masses with AI-based screening tools in children
Time frame: 1 month
The diagnostic negative predictive value of neck masses with AI-based screening tools in children
Time frame: 1 month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jan 2022.
- Individual participant dataSharing planned: No
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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.
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 Neck Mass and Thyroglossal Duct Cysts, run by West China Hospital 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 27 Jan 2022. 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?
Contact Yuhan Yang, MD (8613258389785, yyh_1023@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The diagnostic accuracy of neck masses with AI-based screening tools in children (time frame: 1 month). 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 27 Jan 2022.