Esophageal Cancer StageNCT07251114
AI-assisted Endoscopic Ultrasound Grading of Early Esophageal Cancer Invasion Depth: A Multicenter, Prospective, Randomized Cohort Study
A study testing Artificial Intelligence system in people with Esophageal Cancer Stage and Early Esophageal Cancer, run by Fujian Provincial Hospital at 5 sites in China.
Trial summary
This study mainly uses an artificial intelligence system to assist in the classification of the depth of invasion of early esophageal squamous cell carcinoma under ultrasound endoscopy, providing a basis for preoperative T staging and diagnosis and treatment decisions. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Esophageal Cancer Stage, Early Esophageal Cancer
- What is being tested?
- Artificial Intelligence system
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- 5 sitesChina
- Study dates
- 20 Nov 2025to 31 Dec 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Satisfy ①⑧⑨ and one of the following conditions simultaneously: ②③④⑤⑥⑦ ① Age over 18 years old, ② Esophageal ulcer, ③ low-grade intraepithelial neoplasia, ④ high-grade intraepithelial neoplasia, ⑤ patients with esophageal squamous cell carcinoma, ⑥ white patches of esophageal mucosa, ⑦ esophageal polyps, ⑧ with endoscopic examination records and detailed pathological records, ⑨ agree to participate in the study;
Who cannot join
- ① Patients who have undergone esophageal cancer surgery, ② those with a history of radiotherapy and chemotherapy for esophageal cancer, ③ patients with missing data.
Trial locations and contacts
5 sites in 1 country. Contact the study team or a site near you to ask about taking part.
5 sites in 1 country
China5 sites, 5 recruiting
- Recruiting
Fujian provincial hospital
Fuzhou, Fujian, 350001
Wei Liang, MD, +86-18120888996, fjsllw@163.com
Yanqin Xu, MD, +86-13599382138, 454202013@qq.com
- Not yet recruiting
Affiliated Hospital of Putian University
Putian, Fujian, 351100
Huifeng Wu, MD, +86-13706067949, 453881679@qq.com
Huifeng Wu, MD
- Not yet recruiting
Putian First Hospital
Putian, Fujian, 351100
Junwei Xie, MD, +86-15105949153, fjslzss@163.com
Junwei Xie, MD
- Not yet recruiting
Putian Hospital of Traditional Chinese Medicine
Putian, Fujian, 351100
Fangfang Pan, MD, 86-15105938179, 960935120@qq.com
Fangfang Pan, MD
- Not yet recruiting
Xianyou County General Hospital
Putian, Fujian, 351100
Huijing Wu, MD, +86-15205032044, 736161645@qq.com
Huijing Wu, MD
About this study
This study mainly uses an artificial intelligence system to assist in the classification of the depth of invasion of early esophageal squamous cell carcinoma under ultrasound endoscopy, providing a basis for preoperative T staging and diagnosis and treatment decisions.
Read the detailed description
For patients with early esophageal squamous cell carcinoma and precancerous lesions who met the inclusion and exclusion criteria and voluntarily participated in this project, they were randomly divided into the AI group and the conventional group by central randomization, with 100 cases in each group（anticipated）. Randomization method: The personnel responsible for randomization at the center (who do not participate in the inclusion of subjects) log in to the central randomization system to obtain a randomization number, and finally form a randomization allocation table. Blinding implementation: The observation group and control group determined on the random allocation table were marked as A and B respectively, and then the operating physician implemented protocol A or B. Main indicators: Grading judgment of infiltration depth, pathological consistency
- Conditions
- Esophageal Cancer Stage, Early Esophageal Cancer
- Being tested
- Artificial Intelligence system
- Keywords
- early esophageal cancer, Artificial Intelligence, EUS
- Medical subject headings
- Esophageal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence systemDevice
Also called Artificial Intelligence
Use artificial intelligence to assist in the determination of the invasion depth of early esophageal squamous cell carcinoma under endoscopic ultrasound
| Study group | Role | What they receive |
|---|---|---|
| AI Group | Experimental | Use artificial intelligence to assist in the determination of the invasion depth of early esophageal squamous cell carcinoma under endoscopic ultrasound Receives: Device: Artificial Intelligence system |
| Control group | No intervention | Routine diagnosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy of grading judgment of infiltration depth
By comparing with the postoperative pathology, the accuracy of the preoperative T grading with the assistance of the artificial intelligence grading system was verified
Measured: 2 years
Secondary outcomes (2)
survival rate
3-year survival rate
Measured: Three years
Progression Free-Survival
The period from the start of treatment to tumor progression or death for any reason
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Nov 2025
- Study start20 Nov 2025 (estimated)
- Primary completion31 Oct 2027 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Esophageal Cancer StageStudies of the same condition, open and under way.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Use artificial intelligence to assist in the determination of the invasion depth of early esophageal squamous cell carcinoma under endoscopic ultrasound
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fujian Provincial Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Wei Liang, Fujian Provincial Hospital
- Investigators
- Wei Liang, MD, Principal investigator, Fujian Provincial Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07251114
- Sponsor's protocol ID
- K2025-02-067
- Status last verified
- Nov 2025
- First submitted
- 14 Nov 2025
- Last updated
- 26 Nov 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: It involves the protection of patent technology
Study at a glance
From the registry record, last updated by the sponsor on 26 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-assisted Endoscopic Ultrasound Grading of Early Esophageal Cancer Invasion Depth: A Multicenter, Prospective, Randomized Cohort Study
- ClinicalTrials.gov ID
- NCT07251114
- Acronym / sponsor ID
- K2025-02-067
- Conditions
- Esophageal Cancer Stage, Early Esophageal Cancer
- Keywords
- early esophageal cancer, Artificial Intelligence, EUS
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Fujian Provincial HospitalUniversity, hospital or other
- Study start
- 20 Nov 2025 (estimated)
- Primary completion
- 31 Oct 2027 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 26 Nov 2025 (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.
This study mainly uses an artificial intelligence system to assist in the classification of the depth of invasion of early esophageal squamous cell carcinoma under ultrasound endoscopy, providing a basis for preoperative T staging and diagnosis and treatment decisions.
Detailed description
For patients with early esophageal squamous cell carcinoma and precancerous lesions who met the inclusion and exclusion criteria and voluntarily participated in this project, they were randomly divided into the AI group and the conventional group by central randomization, with 100 cases in each group（anticipated）. Randomization method: The personnel responsible for randomization at the center (who do not participate in the inclusion of subjects) log in to the central randomization system to obtain a randomization number, and finally form a randomization allocation table. Blinding implementation: The observation group and control group determined on the random allocation table were marked as A and B respectively, and then the operating physician implemented protocol A or B. Main indicators: Grading judgment of infiltration depth, pathological consistency
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- Use artificial intelligence to assist in the determination of the invasion depth of early esophageal squamous cell carcinoma under endoscopic ultrasound
| Arm | Type | Receives |
|---|---|---|
| AI Group | Experimental | Device: Artificial Intelligence systemUse artificial intelligence to assist in the determination of the invasion depth of early esophageal squamous cell carcinoma under endoscopic ultrasound |
| Control group | No intervention | –Routine diagnosis |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Satisfy ①⑧⑨ and one of the following conditions simultaneously: ②③④⑤⑥⑦ ① Age over 18 years old, ② Esophageal ulcer, ③ low-grade intraepithelial neoplasia, ④ high-grade intraepithelial neoplasia, ⑤ patients with esophageal squamous cell carcinoma, ⑥ white patches of esophageal mucosa, ⑦ esophageal polyps, ⑧ with endoscopic examination records and detailed pathological records, ⑨ agree to participate in the study;
Key exclusion criteria (1 listed)
- ① Patients who have undergone esophageal cancer surgery, ② those with a history of radiotherapy and chemotherapy for esophageal cancer, ③ patients with missing data.
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 systemAlso: Artificial IntelligenceUse artificial intelligence to assist in the determination of the invasion depth of early esophageal squamous cell carcinoma under endoscopic ultrasound | Device | AI Group |
Primary outcome
The accuracy of grading judgment of infiltration depth
Time frame: 2 years
By comparing with the postoperative pathology, the accuracy of the preoperative T grading with the assistance of the artificial intelligence grading system was verified
2 secondary outcomes
survival rate
Time frame: Three years
Progression Free-Survival
Time frame: 1 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Nov 2025.
- Individual participant dataSharing planned: No. It involves the protection of patent technology
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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.
Wei Liang, MD principal investigator
Fujian Provincial Hospital
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence system in people with Esophageal Cancer Stage and Early Esophageal Cancer, run by Fujian Provincial Hospital at 5 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 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: looking for participants now, as of the sponsor's last update on 26 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Wei Liang, MD (+86 -18120888996, fjsllw@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The accuracy of grading judgment of infiltration depth (time frame: 2 years). 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 26 Nov 2025.