Esophageal CancerDeepDTNCT07642401
Large-scale Models of Esophageal Cancer and Related Research
An observational study of Esophageal Cancer, run by The First Affiliated Hospital of Henan University of Science and Technology at 3 sites in China.
Trial summary
The goal of this observational study is to learn about the clinical utility of an artificial intelligence (AI) large language model in patients undergoing screening, diagnosis, treatment, and prognosis assessment for esophageal cancer. Read more about this study
- Who is it for?
- Ages 18 to 90 years
- Conditions the study is for
- Esophageal Cancer
- How many people
- 12,000 participants
- Where
- 3 sitesChina
- Study dates
- 15 May 2026to 31 Oct 2027
- How long will I take part?
- 3 Years
- Phase
- Observational
- 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 to 90 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population comprises patients receiving routine esophageal cancer management at participating healthcare institutions, including those undergoing screening (e.g., endoscopy), diagnosis (imaging, pathology), treatment (endoscopic resection, surgery, chemotherapy, radiotherapy), and prognostic follow-up. Inclusion criteria: age ≥18 years, suspected or confirmed esophageal cancer, and available complete clinical data (endoscopy, imaging, pathology, and follow-up records). Exclusion criteria: incomplete data or refusal to use medical records. The population spans early to advanced stages to evaluate the AI model across the full disease spectrum.
Who may be able to join
- 1. Aged 18 years or older. 2. Individuals with normal findings or inflammatory changes: endoscopic or pathological reports indicating "no significant abnormalities detected" or changes consistent with inflammation.
- 3. Individuals with benign lesions: pathological reports specifying "absence of tumor cells" or a diagnosis consistent with benign lesions.
- 4. Individuals with precancerous lesions: pathological reports with a definitive diagnosis of Low-grade Intraepithelial Neoplasia (LGIN) or High-grade Intraepithelial Neoplasia (HGIN).
- 5. Individuals with malignant tumors: pathological reports confirming a diagnosis of esophageal squamous cell carcinoma or esophageal adenocarcinoma.
Who cannot join
- 1. Diagnostically uncertain: Lack of definitive pathological evidence, or with doubtful clinical diagnosis.
- 2. Poor data quality: Low-quality key imaging data (endoscopy, CT) that is unsuitable for analysis (e.g., severe artifacts, missing images).
- 3. Severe missingness of key clinical or follow-up data (missing rate > 20%). 4. Confounding by other malignancies: Presence of other active malignant tumors other than esophageal cancer within 5 years prior to enrollment.
- 5. Loss to follow-up: Failure to obtain key survival or recurrence follow-up information in the retrospective cohort.
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China3 sites
Anyang Tumor Hospital
Anyang, Henan, 455000
The First Affiliated Hospital of Henan University of Science & Technology
Luoyang, Henan, 471000
Nanyang Central Hospital Medical Ethics Committee
Nanyang, Henan, 473000
About this study
Official title: Clinical Application Research of AI-Based Large Models for Early Screening, Diagnosis, Treatment, and Prognosis Assessment of Esophageal Cancer
The goal of this observational study is to learn about the clinical utility of an artificial intelligence (AI) large language model in patients undergoing screening, diagnosis, treatment, and prognosis assessment for esophageal cancer. The main question it aims to answer is:
Does the AI model improve early detection rate, diagnostic accuracy, treatment personalization, and prognostic prediction for esophageal cancer compared to standard care? Participants already receiving routine esophageal cancer management (including endoscopy, imaging, pathology, and clinical follow-up) as part of their regular medical care will have their de-identified data processed by the AI model; researchers will compare model-based recommendations and outcomes with standard care benchmarks over 3 years.
Last updated on Oct 31, 2027
- Conditions
- Esophageal Cancer
- Being tested
- Observational study; no assigned intervention. Participants receive routine esophageal cancer management (endoscopy, imaging, pathology, clinical follow-up) as standard care.
- Medical subject headings
- Esophageal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Observational study; no assigned intervention. Participants receive routine esophageal cancer management (endoscopy, imaging, pathology, clinical follow-up) as standard care.Other
Routine esophageal cancer management including endoscopy, imaging, pathology, and clinical follow-up as per standard clinical practice. No additional, experimental, or assigned intervention is administered. The AI large language model processes de-identified data from routine care for comparative analysis against standard care benchmarks over 3 years.
| Study group | Role | What they receive |
|---|---|---|
| Single cohort | Patients receiving routine esophageal cancer management (including endoscopy, imaging, pathology, and clinical follow-up) as part of their regular medical care. De-identified data from these participants will be processed by an AI large language model, and model-based recommendations will be compared with standard care benchmarks over 3 years. Receives: Other: Observational study; no assigned intervention. Participants receive routine esophageal cancer management (endoscopy, imaging, pathology, clinical follow-up) as standard care. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the ROC curve (AUC) of the multimodal model for diagnosing esophageal cancer, calculated by ROC analysis using pathological biopsy as the gold standard, based on 5-fold cross-validation on the internal validation set.
The AUC ranges from 0.5 to 1.0, with higher values indicating better diagnostic performance.
Measured: 1 year and 5 months
Overall accuracy (proportion of correct classifications) of the multimodal model for diagnosing esophageal cancer, derived from the confusion matrix of the model's predictions on the internal validation set, with pathological biopsy as the gold standard.
The accuracy ranges from 0% to 100%, with higher percentages indicating better classification performance.
Measured: Up to 3 years
Concordance index (C-index) of the multimodal model for predicting overall survival and progression-free survival, derived from Cox proportional hazards model on time-to-event data.
The C-index ranges from 0.5 to 1.0, where 0.5 indicates random chance and 1.0 indicates perfect prediction; higher values indicate better predictive discrimination.
Measured: Up to 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Jun 2026
- Study start15 May 2026
- Primary completion31 Oct 2027 (estimated)
- Study completion31 Oct 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Esophageal CancerStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 12,000 participants (estimated)
- Target duration
- 3 Years
Sponsor and investigators
- Lead sponsor
- The First Affiliated Hospital of Henan University of Science and Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07642401
- Sponsor's protocol ID
- 2025-0697
- Acronym
- DeepDT
- Status last verified
- Aug 2026
- First submitted
- 1 Jun 2026
- Last updated
- 27 Aug 2026
- 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 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Application Research of AI-Based Large Models for Early Screening, Diagnosis, Treatment, and Prognosis Assessment of Esophageal Cancer
- ClinicalTrials.gov ID
- NCT07642401
- Acronym / sponsor ID
- DeepDT / 2025-0697
- Conditions
- Esophageal Cancer
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 12,000 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Henan University of Science and TechnologyUniversity, hospital or other
- Study start
- 15 May 2026 (actual)
- Primary completion
- 31 Oct 2027 (estimated)
- Study completion
- 31 Oct 2027 (estimated)
- First posted
- 11 Jun 2026 (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 goal of this observational study is to learn about the clinical utility of an artificial intelligence (AI) large language model in patients undergoing screening, diagnosis, treatment, and prognosis assessment for esophageal cancer. The main question it aims to answer is:
Does the AI model improve early detection rate, diagnostic accuracy, treatment personalization, and prognostic prediction for esophageal cancer compared to standard care? Participants already receiving routine esophageal cancer management (including endoscopy, imaging, pathology, and clinical follow-up) as part of their regular medical care will have their de-identified data processed by the AI model; researchers will compare model-based recommendations and outcomes with standard care benchmarks over 3 years.
Last updated on Oct 31, 2027
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 3 Years
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Single cohort | – | Other: Observational study; no assigned intervention. Participants receive routine esophageal cancer management (endoscopy, imaging, pathology, clinical follow-up) as standard care.Patients receiving routine esophageal cancer management (including endoscopy, imaging, pathology, and clinical follow-up) as part of their regular medical care. De-identified data from these participants will be processed by an AI large language model, and model-based recommendations will be compared with standard care benchmarks over 3 years. |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population comprises patients receiving routine esophageal cancer management at participating healthcare institutions, including those undergoing screening (e.g., endoscopy), diagnosis (imaging, pathology), treatment (endoscopic resection, surgery, chemotherapy, radiotherapy), and prognostic follow-up. Inclusion criteria: age ≥18 years, suspected or confirmed esophageal cancer, and available complete clinical data (endoscopy, imaging, pathology, and follow-up records). Exclusion criteria: incomplete data or refusal to use medical records. The population spans early to advanced stages to evaluate the AI model across the full disease spectrum.
Key inclusion criteria (4 listed)
- 1. Aged 18 years or older. 2. Individuals with normal findings or inflammatory changes: endoscopic or pathological reports indicating "no significant abnormalities detected" or changes consistent with inflammation.
- 3. Individuals with benign lesions: pathological reports specifying "absence of tumor cells" or a diagnosis consistent with benign lesions.
- 4. Individuals with precancerous lesions: pathological reports with a definitive diagnosis of Low-grade Intraepithelial Neoplasia (LGIN) or High-grade Intraepithelial Neoplasia (HGIN).
- 5. Individuals with malignant tumors: pathological reports confirming a diagnosis of esophageal squamous cell carcinoma or esophageal adenocarcinoma.
Key exclusion criteria (4 listed)
- 1. Diagnostically uncertain: Lack of definitive pathological evidence, or with doubtful clinical diagnosis.
- 2. Poor data quality: Low-quality key imaging data (endoscopy, CT) that is unsuitable for analysis (e.g., severe artifacts, missing images).
- 3. Severe missingness of key clinical or follow-up data (missing rate > 20%). 4. Confounding by other malignancies: Presence of other active malignant tumors other than esophageal cancer within 5 years prior to enrollment.
- 5. Loss to follow-up: Failure to obtain key survival or recurrence follow-up information in the retrospective cohort.
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 |
|---|---|---|
| Observational study; no assigned intervention. Participants receive routine esophageal cancer management (endoscopy, imaging, pathology, clinical follow-up) as standard care.Routine esophageal cancer management including endoscopy, imaging, pathology, and clinical follow-up as per standard clinical practice. No additional, experimental, or assigned intervention is administered. The AI large language model processes de-identified data from routine care for comparative analysis against standard care benchmarks over 3 years. | Other | Single cohort |
Primary outcome
Area under the ROC curve (AUC) of the multimodal model for diagnosing esophageal cancer, calculated by ROC analysis using pathological biopsy as the gold standard, based on 5-fold cross-validation on the internal validation set.
Time frame: 1 year and 5 months
The AUC ranges from 0.5 to 1.0, with higher values indicating better diagnostic performance.
Overall accuracy (proportion of correct classifications) of the multimodal model for diagnosing esophageal cancer, derived from the confusion matrix of the model's predictions on the internal validation set, with pathological biopsy as the gold standard.
Time frame: Up to 3 years
The accuracy ranges from 0% to 100%, with higher percentages indicating better classification performance.
Concordance index (C-index) of the multimodal model for predicting overall survival and progression-free survival, derived from Cox proportional hazards model on time-to-event data.
Time frame: Up to 3 years
The C-index ranges from 0.5 to 1.0, where 0.5 indicates random chance and 1.0 indicates perfect prediction; higher values indicate better predictive discrimination.
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 27 Aug 2026.
- 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.
The record lists no contact. Use the official record to reach the sponsor.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Esophageal Cancer, run by The First Affiliated Hospital of Henan University of Science and Technology at 3 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 4 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: enrolling by invitation, as of the sponsor's last update on 27 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the ROC curve (AUC) of the multimodal model for diagnosing esophageal cancer, calculated by ROC analysis using pathological biopsy as the gold standard, based on 5-fold cross-validation on the internal validation set. (time frame: 1 year and 5 months). The study lists 3 primary outcomes in all. 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 Aug 2026.