Deep Learning ModelNCT05231616
Deep Learning Model for Diagnosis and Contour of Cervical Lymph Node for Nasopharyngeal Carcinoma
An observational study of Deep Learning Model, run by Sun Yat-sen University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Deep Learning Model
- How many people
- 5,000 participants
- Where
- 1 siteChina
- Study dates
- 5 Jan 2021to 31 Dec 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: Nasopharyngeal carcinoma
Who may be able to join
- Pathological diagnosis of nasopharyngeal carcinoma; Cervival lymph nodes confirmed by pathology
Who cannot join
- a history of cancer
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, 510060
Fang-Yun Xie, professor, +86-20-87342618 ext. 2618, xiefy@sysucc.org.cn
About this study
Official title: 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.
- Conditions
- Deep Learning Model
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity and specificity
Measured: 2022-12-31
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Feb 2022
- Study start5 Jan 2021
- Primary completion31 Dec 2022 (estimated)
- Study completion31 Dec 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Deep Learning ModelStudies of the same condition, open and under way.
- More trials run by Sun Yat-sen UniversityThe 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
- Prospective
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Fang-Yun Xie, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05231616
- Sponsor's protocol ID
- B2020-334
- Status last verified
- Feb 2022
- First submitted
- 8 Feb 2022
- Last updated
- 28 Feb 2022
- 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 28 Feb 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Magnetic Resonance Imaging Based Deep Learning Model for Diagnosis and Contour of Cervical Lymph Node for Nasopharyngeal Carcinoma: a Multicenter Study
- ClinicalTrials.gov ID
- NCT05231616
- Acronym / sponsor ID
- B2020-334
- Conditions
- Deep Learning Model
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- 5 Jan 2021 (actual)
- Primary completion
- 31 Dec 2022 (estimated)
- Study completion
- 31 Dec 2022 (estimated)
- First posted
- 9 Feb 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 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 design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Nasopharyngeal carcinoma
Key inclusion criteria (1 listed)
- Pathological diagnosis of nasopharyngeal carcinoma; Cervival lymph nodes confirmed by pathology
Key exclusion criteria (1 listed)
- a history of cancer
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Sensitivity and specificity
Time frame: 2022-12-31
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Feb 2022.
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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 Deep Learning Model, run by 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 1 inclusion and 1 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 28 Feb 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 Fang-Yun Xie, Professor (+86-20-87342618 ext. 2618, xiefy@sysucc.org.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity and specificity (time frame: 2022-12-31). 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 28 Feb 2022.