Large-scale Models of Esophageal Cancer and Related Research (DeepDT)

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

Study Overview

• Status: Enrolling by invitation
• Conditions: Esophageal Cancer
• Intervention / Treatment: Other: Observational study; no assigned intervention. Participants receive routine esophageal cancer management (endoscopy, imaging, pathology, clinical follow-up) as standard care.

• Study Type: Observational
• Enrollment (Estimated): 12000

Contacts and Locations

Study Locations

• China
  • Henan
    • Anyang, Henan, China, 455000
      • Anyang Tumor Hospital
    • Luoyang, Henan, China, 471000
      • The First Affiliated Hospital of Henan University of Science & Technology
    • Nanyang, Henan, China, 473000
      • Nanyang Central Hospital Medical Ethics Committee

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

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.

Description

Inclusion Criteria:

• 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.

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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.

Other: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
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.	Up to 3 years
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.	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.	Up to 3 years

Collaborators and Investigators

• Sponsor: The First Affiliated Hospital of Henan University of Science and Technology

Study record dates

Study Major Dates

• Study Start (Actual): May 15, 2026
• Primary Completion (Estimated): October 31, 2027
• Study Completion (Estimated): October 31, 2027

Study Registration Dates

• First Submitted: June 1, 2026
• First Submitted That Met QC Criteria: June 7, 2026
• First Posted (Actual): June 11, 2026

Study Record Updates

• Last Update Posted (Actual): June 11, 2026
• Last Update Submitted That Met QC Criteria: June 7, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Head and Neck Neoplasms; Esophageal Diseases; Esophageal Neoplasms; Investigative Techniques; Methods; Diagnostic Techniques and Procedures; Diagnosis; Surgical Procedures, Operative; Minimally Invasive Surgical Procedures; Physical Phenomena; Diagnostic Techniques, Surgical; Electromagnetic Phenomena; Magnetic Phenomena; Electromagnetic Radiation; Radiation; Radiation, Ionizing; Observation; X-Rays; Endoscopy
• Other Study ID Numbers: 2025-0697

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No