The investigators plan to develop a deep learning-based automatic interpretation model for Claudin18.2(CLDN18.2) using the institution's and multiple other centers' extensive pathological resources of digestive system adenocarcinomas. This study will not only strictly follow the latest domestic expert consensus and standards, but also aims to address current pain points in manual interpretation. It seeks to provide technical support for standardizing, objectifying, and streamlining CLDN18.2 testing, thereby advancing the application of precision medicine in the diagnosis and treatment of digestive system diseases. The project has clear clinical necessity and broad application prospects.

Inclusion Criteria:

1. Age over 18 years.
2. Patients who underwent CLDN18.2 immunohistochemistry and H&E staining.
3. Availability of complete pathology reports and clinical information.

Exclusion Criteria:

1.Patients with missing data or specimens not meeting quality control requirements for analysis.

Inclusion criteria comprised patients aged over 18 years with definitive pathological diagnoses.

Requests for the data collected and analyzed in this study will be considered if the application is in line with public benefits and the applicant is willing to sign a data access agreement. Contact can be through the corresponding author.