Nasopharyngeal CarcinomaNCT05627310
Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images
An observational study of Nasopharyngeal Carcinoma, run by Eye & ENT Hospital of Fudan University at 8 sites in China.
Trial summary
Develop a deep learning algorithm via nasal endoscopic images from eight NPC treatment centerto detect and screen nasopharyngeal carcinoma(NPC). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Nasopharyngeal Carcinoma
- What is being tested?
- Diagnostic
- How many people
- 50,000 participants
- Where
- 8 sitesChina
- Study dates
- 1 Nov 2022to 31 Mar 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Nasopharyngeal endoscopic images were collected from 8 hospital in NPC epidemic area all over China.
Who may be able to join
- The quality of endoscopic images should clinical acceptable.
- Patients were diagnosed with biopsy(NPC, benign hyperplasia). Control corhort(normal nasopharynx) don't require bispsy result.
Who cannot join
- images with spots from lens flares or stains, and overexposure were excluded from further analysis.
- image can not expose most part of lesion clearly.
Trial locations and contacts
8 sites in 1 country.
8 sites in 1 country
China8 sites, 8 recruiting
- Recruiting
Xiangya Hospital of Central South University
Changsha, Hunan
Wei-Hong Jiang, MD PhD
- Recruiting
Fujian Medical University Union Hospital
Fuzhou, Fujian
De-Sheng Wang, MD PhD
- Recruiting
The People' s Hospital of Jiangmen
Jiangmen, Guangdong
Ming-Zhang Chang, MD
- Recruiting
The First Affiliated Hospital of Nanchang University
Nanchang, Jiangxi
Jing Ye, MD PhD
- Recruiting
First Affiliated Hospital of Guangxi Medical University
Nanning, Guangxi
Jie-En Li, MD PhD
- Recruiting
The People' s Hospital of Guangxi Zhuang Autonomous Region
Nanning, Guangxi
Shen-Hong Qu, MD PhD
- Recruiting
Quan Zhou First Affiliated Hospital of Fujian Medical University
Quanzhou, Fujian
Jun Liao, MD PhD
- Recruiting
Eye&ENT Hospital of Fudan University
Shanghai, Shanghai Municipality
Yu-Xuan Shi, MD PhD
About this study
Official title: Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images: a Multi-center Prospective Study
Develop a deep learning algorithm via nasal endoscopic images from eight NPC treatment centerto detect and screen nasopharyngeal carcinoma(NPC).
Read the detailed description
Nasopharyngeal carcinoma (NPC) is an epithelial cancer derived from nasopharyngeal mucosa. Nasal endoscopy is the conventional examination for NPC screening. It is a major challenge for inexperienced endoscopists to accurately distinguish NPC and other benign dieseases. In this study, we collcet multi-center endoscopic images and train a deep learning model to detect NPC and indicate tumor location. Then, the model perfomance will be compared with endoscopists and be tested prospectively with external dataset.
- Conditions
- Nasopharyngeal Carcinoma
- Being tested
- Diagnostic
- Keywords
- nasopharyngeal carcinoma, deep learning, tumor screening
- Medical subject headings
- Nasopharyngeal Carcinoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
DiagnosticOther
Training dataset was used to train the deep learning model, which was validated and tested by external dataset.
| Study group | Role | What they receive |
|---|---|---|
| Training Cohort | Nasopharyngeal endoscopic images collected from 8 hospitals all over China | |
| Validation Cohort | Nasopharyngeal endoscopic images collected from 8 hospitals all over China Receives: Other: Diagnostic | |
| Testing Cohort | Nasopharyngeal endoscopic images prospectively collected from 8 hospitals all over China Receives: Other: Diagnostic |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve of the deep learning algorithm
The investigators will calculate the area under the receiver operating characteristic curve of deep learning algorithm and compare this index between deep learning system and human doctors.
Measured: baseline
Secondary outcomes (2)
Sensitivity of the deep learning system
The investigators will calculate the sensitivity of deep learning algorithm and compare this index between deep learning system and human doctors.
Measured: baseline
Specificity of the deep learning system
The investigators will calculate the specificity of deep learning algorithm and compare this index between deep learning system and human doctors.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Nov 2022
- Study start1 Nov 2022
- Primary completion31 Dec 2023 (estimated)
- Study completion31 Mar 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Nasopharyngeal CarcinomaStudies of the same condition, open and under way.
- More trials run by Eye & ENT Hospital of Fudan 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
- Other
- Time perspective
- Other
- Enrolment
- 50,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Eye & ENT Hospital of Fudan University
- Sponsor type
- University, hospital or other
- Collaborators
- Xiangya Hospital of Central South University, The First Affiliated Hospital of Nanchang University, Fujian Medical University Union Hospital, Quan Zhou First Affiliated Hospital of Fujian Medical University, First Affiliated Hospital of Guangxi Medical University, People's Hospital of Guangxi Zhuang Autonomous Region, The People' s Hospital of Jiangmen
- Responsible party
- Sponsor
- Investigators
- Hongmeng Yu, MD PhD, Principal investigator, Eye&ENT Hospital, Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05627310
- Sponsor's protocol ID
- AIAD202204
- Status last verified
- Nov 2022
- First submitted
- 11 Jul 2022
- Last updated
- 25 Nov 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Wang W, Jin Z, Liu X, Chen X. NaMA-Mamba: Foundation model for generalizable nasal disease detection using masked autoencoder with Mamba on endoscopic images. Comput Med Imaging Graph. 2025 Jun;122:102524. doi: 10.1016/j.compmedimag.2025.102524. Epub 2025 Mar 12.
PubMed 40088572
Study at a glance
From the registry record, last updated by the sponsor on 25 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images: a Multi-center Prospective Study
- ClinicalTrials.gov ID
- NCT05627310
- Acronym / sponsor ID
- AIAD202204
- Conditions
- Nasopharyngeal Carcinoma
- Keywords
- nasopharyngeal carcinoma, deep learning, tumor screening
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 50,000 participants (estimated)
- Lead sponsor
- Eye & ENT Hospital of Fudan UniversityUniversity, hospital or other
- Collaborators
- Xiangya Hospital of Central South University, The First Affiliated Hospital of Nanchang University, Fujian Medical University Union Hospital, Quan Zhou First Affiliated Hospital of Fujian Medical University, First Affiliated Hospital of Guangxi Medical University, People's Hospital of Guangxi Zhuang Autonomous Region, The People' s Hospital of Jiangmen
- Study start
- 1 Nov 2022 (actual)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 31 Mar 2024 (estimated)
- First posted
- 25 Nov 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.
Develop a deep learning algorithm via nasal endoscopic images from eight NPC treatment centerto detect and screen nasopharyngeal carcinoma(NPC).
Detailed description
Nasopharyngeal carcinoma (NPC) is an epithelial cancer derived from nasopharyngeal mucosa. Nasal endoscopy is the conventional examination for NPC screening. It is a major challenge for inexperienced endoscopists to accurately distinguish NPC and other benign dieseases. In this study, we collcet multi-center endoscopic images and train a deep learning model to detect NPC and indicate tumor location. Then, the model perfomance will be compared with endoscopists and be tested prospectively with external dataset.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Training Cohort | – | –Nasopharyngeal endoscopic images collected from 8 hospitals all over China |
| Validation Cohort | – | Other: DiagnosticNasopharyngeal endoscopic images collected from 8 hospitals all over China |
| Testing Cohort | – | Other: DiagnosticNasopharyngeal endoscopic images prospectively collected from 8 hospitals all over China |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Nasopharyngeal endoscopic images were collected from 8 hospital in NPC epidemic area all over China.
Key inclusion criteria (2 listed)
- The quality of endoscopic images should clinical acceptable.
- Patients were diagnosed with biopsy(NPC, benign hyperplasia). Control corhort(normal nasopharynx) don't require bispsy result.
Key exclusion criteria (2 listed)
- images with spots from lens flares or stains, and overexposure were excluded from further analysis.
- image can not expose most part of lesion clearly.
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 |
|---|---|---|
| DiagnosticTraining dataset was used to train the deep learning model, which was validated and tested by external dataset. | Other | Testing Cohort, Validation Cohort |
Primary outcome
Area under the receiver operating characteristic curve of the deep learning algorithm
Time frame: baseline
The investigators will calculate the area under the receiver operating characteristic curve of deep learning algorithm and compare this index between deep learning system and human doctors.
2 secondary outcomes
Sensitivity of the deep learning system
Time frame: baseline
Specificity of the deep learning system
Time frame: baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Nov 2022.
- Derived from this study: PubMed 40088572Wang W, Jin Z, Liu X, Chen X. NaMA-Mamba: Foundation model for generalizable nasal disease detection using masked autoencoder with Mamba on endoscopic images. Comput Med Imaging Graph. 2025 Jun;122:102524. doi: 10.1016/j.compmedimag.2025.102524. Epub 2025 Mar 12.
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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.
Hongmeng Yu, MD PhD principal investigator
Eye&ENT Hospital, Fudan University
8 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Nasopharyngeal Carcinoma, run by Eye & ENT Hospital of Fudan University at 8 sites 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 2 inclusion and 2 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 25 Nov 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 8 sites 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 Yu-Xuan Shi, MD PhD (+8618952373378, syxent@hotmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the receiver operating characteristic curve of the deep learning algorithm (time frame: baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05627310, which also lists 1 linked publication.
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 25 Nov 2022.