An Artificial Intelligence Model for Aiding Claudin18.2 Expression Diagnosis in Gastric Adenocarcinoma

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ENROLLING_BY_INVITATION

Total Enrollment

2000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-10-01

Study Completion Date

2028-08-31

Brief Summary

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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.

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Detailed Description

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Conditions

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Digestive Oncology

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Prospective validation dataset

We conducted a prospective validation study to compare the diagnostic performance among pathologists, our pathology foundation model, and pathologist-with-AI-assisted diagnosis. This study was initiated on October 1, 2025 at Nanfang Hospital, Southern Medical University (NFHSMU)

No interventions assigned to this group

QFSH external validation dataset

2000 slides from 1000 eligible individuals were obtained in the Qianfoshan Hospital (QFSH, Jinan, China) between January 2020 and February 2026, which was used to validate the model.

No interventions assigned to this group

Randomized controlled trial

We conducted a randomized controlled trial（RCT）to compare the diagnostic performance among pathologists, the pathology foundation model, and pathologist-with-model-assisted diagnosis at Nanfang Hospital of Southern Medical University (NFHSMU).The trial commenced data collection on February 1, 2023 to establish the NFHSMU randomized controlled trial dataset. Following quality control, the slides were randomly allocated (1:1 ratio) into three groups:Pathologist-only group/Model-assisted pathologist group.

No interventions assigned to this group

Eligibility Criteria

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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.