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

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.

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.