Large-scale Models of Esophageal Cancer and Related Research

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

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Key information

Conditions

Esophageal Cancer Digestive System Diseases Digestive System Neoplasms Esophageal Diseases Esophageal Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Head and Neck Neoplasms Neoplasms Neoplasms by Site

Age range

18 year–90 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Anyang Tumor Hospital, Anyang, Henan, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Individuals with benign lesions: pathological reports specifying "absence of tumor cells" or a diagnosis consistent with benign lesions.

Individuals with precancerous lesions: pathological reports with a definitive diagnosis of Low-grade Intraepithelial Neoplasia (LGIN) or High-grade Intraepithelial Neoplasia (HGIN).

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.

Poor data quality: Low-quality key imaging data (endoscopy, CT) that is unsuitable for analysis (e.g., severe artifacts, missing images).

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.

Loss to follow-up: Failure to obtain key survival or recurrence follow-up information in the retrospective cohort.

Treatment and study plan

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.

Primary outcomes

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: 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.

Time frame: 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.

Time frame: Up to 3 years

Sponsors and collaborators

Lead sponsor

The First Affiliated Hospital of Henan University of Science and Technology

Other

Registry information

Official study title

Clinical Application Research of AI-Based Large Models for Early Screening, Diagnosis, Treatment, and Prognosis Assessment of Esophageal Cancer

Acronym: DeepDT

Important dates

Study start: 2026
Primary completion: 2027
Study completion: 2027
First posted: Jun 11, 2026
Registry last updated: Jun 11, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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