ESCCNCT07050576
Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma
An observational study of ESCC and Lymph Node Metastasis, run by The First Affiliated Hospital of Anhui Medical University at 1 site in China.
Trial summary
This study aims to develop a predictive model using deep learning and radiomics to assess the likelihood of lymph node metastasis in patients with early-stage esophageal squamous cell carcinoma (ESCC). Lymph node metastasis is a critical factor in determining the treatment approach and prognosis for ESCC patients. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- ESCC, Lymph Node Metastasis, Radiomics
- What is being tested?
- The prediction model of lymph node metastasis in early esophageal squamous cell carcinoma
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 1 May 2024to 30 Nov 2025
- 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: The radiomics features that affects the prediction of LNM in early-stage ESCC. All patients with early-stage ESCC from the hospitals
Who may be able to join
- Patients with pathologically confirmed early-stage (T1) ESCC
- Preoperative contrast-enhanced CT data within 2 weeks before surgery
- Without any treatment before surgical resection
Who cannot join
- Patients who underwent neoadjuvant therapy or endoscopic treatment
- Insufficient CT imaging or poor CT quality
- Incomplete pathology results
- Presence of metastatic disease
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
The First Affiliated Hospital of Anhui Medical University
Hefei, Anhui, 230022
Hao Zheng, MD, +86 139 1793 6873, pojunayfy@gmail.com
About this study
Official title: Deep Learning and Radiomics for Prediction of Lymph Node Metastasis in Early-stage Esophageal Squamous Cell Carcinoma
This study aims to develop a predictive model using deep learning and radiomics to assess the likelihood of lymph node metastasis in patients with early-stage esophageal squamous cell carcinoma (ESCC). Lymph node metastasis is a critical factor in determining the treatment approach and prognosis for ESCC patients. By analyzing medical imaging data, we hope to create a non-invasive method that can assist doctors in making more accurate treatment decisions. This research could improve patient outcomes by enabling earlier and more tailored interventions.
- Conditions
- ESCC, Lymph Node Metastasis, Radiomics
- Being tested
- The prediction model of lymph node metastasis in early esophageal squamous cell carcinoma
- Keywords
- ESCC, Lymph node metastasis, radiomics
- Medical subject headings
- Lymphatic Metastasis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
The prediction model of lymph node metastasis in early esophageal squamous cell carcinomaDiagnostic test
The predictive performance of the model was validated in the test set. The optimal prediction model was determined based on the AUC and ACC. To assess the robustness of the chosen model, ROC analysis was conducted on the external validation set.
| Study group | Role | What they receive |
|---|---|---|
| A | A total of 400 patients with early-stage ESCC from our center were divided into training and test sets. Receives: Diagnostic Test: The prediction model of lymph node metastasis in early esophageal squamous cell carcinoma | |
| B | A total of 100 patients with early-stage ESCC from other center were defined as external validation Receives: Diagnostic Test: The prediction model of lymph node metastasis in early esophageal squamous cell carcinoma |
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC(the area under the curve) values of the model
The performance and clinical relevance of the models were assessed by analyzing the area under the curve (AUC).
Measured: 4 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Jul 2025
- Study start1 May 2024
- Primary completion1 Oct 2025 (estimated)
- Study completion30 Nov 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for ESCCStudies 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
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The First Affiliated Hospital of Anhui Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07050576
- Sponsor's protocol ID
- Early-ESCC-LNM-2024
- Status last verified
- Sep 2024
- First submitted
- 26 Jun 2025
- Last updated
- 3 Jul 2025
- Results posted
- No
- Data monitoring committee
- 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 3 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning and Radiomics for Prediction of Lymph Node Metastasis in Early-stage Esophageal Squamous Cell Carcinoma
- ClinicalTrials.gov ID
- NCT07050576
- Acronym / sponsor ID
- Early-ESCC-LNM-2024
- Conditions
- ESCC, Lymph Node Metastasis, Radiomics
- Keywords
- ESCC, Lymph node metastasis, radiomics
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Anhui Medical UniversityUniversity, hospital or other
- Study start
- 1 May 2024 (actual)
- Primary completion
- 1 Oct 2025 (estimated)
- Study completion
- 30 Nov 2025 (estimated)
- First posted
- 3 Jul 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 aims to develop a predictive model using deep learning and radiomics to assess the likelihood of lymph node metastasis in patients with early-stage esophageal squamous cell carcinoma (ESCC). Lymph node metastasis is a critical factor in determining the treatment approach and prognosis for ESCC patients. By analyzing medical imaging data, we hope to create a non-invasive method that can assist doctors in making more accurate treatment decisions. This research could improve patient outcomes by enabling earlier and more tailored interventions.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| A | – | Diagnostic Test: The prediction model of lymph node metastasis in early esophageal squamous cell carcinomaA total of 400 patients with early-stage ESCC from our center were divided into training and test sets. |
| B | – | Diagnostic Test: The prediction model of lymph node metastasis in early esophageal squamous cell carcinomaA total of 100 patients with early-stage ESCC from other center were defined as external validation |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The radiomics features that affects the prediction of LNM in early-stage ESCC. All patients with early-stage ESCC from the hospitals
Key inclusion criteria (3 listed)
- Patients with pathologically confirmed early-stage (T1) ESCC
- Preoperative contrast-enhanced CT data within 2 weeks before surgery
- Without any treatment before surgical resection
Key exclusion criteria (4 listed)
- Patients who underwent neoadjuvant therapy or endoscopic treatment
- Insufficient CT imaging or poor CT quality
- Incomplete pathology results
- Presence of metastatic disease
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 |
|---|---|---|
| The prediction model of lymph node metastasis in early esophageal squamous cell carcinomaThe predictive performance of the model was validated in the test set. The optimal prediction model was determined based on the AUC and ACC. To assess the robustness of the chosen model, ROC analysis was conducted on the external validation set. | Diagnostic test | A, B |
Primary outcome
AUC(the area under the curve) values of the model
Time frame: 4 years
The performance and clinical relevance of the models were assessed by analyzing the area under the curve (AUC).
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Jul 2025.
- 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 ESCC and Lymph Node Metastasis, run by The First Affiliated Hospital of Anhui Medical 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 3 inclusion and 4 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 3 Jul 2025. 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 Hao Zheng, MD (+86 139 1793 6873, pojunayfy@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC(the area under the curve) values of the model (time frame: 4 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 3 Jul 2025.