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

NCT07274579 | Enrolling By Invitation

• 1 other identifier: NFEC-2025-619
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 2

Brief Summary

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.

Conditions

Digestive Oncology

Outcome Measures

Primary Outcomes (1)

• Area under ROC curve (AUC)

  Area under the curve

  Diagnostic evaluation will be performed within 1 week when the whole slide images(WSIs) are obtained.

Secondary Outcomes (2)

• Specificity

  Diagnostic evaluation will be performed within 1 week when the WSIs are obtained

• Sensitivity

  Diagnostic evaluation will be performed within 1 week when the WSIs are obtained

Study Arms (3)

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)

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.

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Nanfang Hospital, Southern Medical University: lead
• Qianfoshan Hospital: collaborator

Study Sites (2)

Nanfang Hospital, Southern Medical University

Guangzhou, Guangdong, 510515, China

Location

Qianfoshan Hospital

Jinan, Shandong, 250014, China

Location

Biospecimen

Retention: SAMPLES WITHOUT DNA

Samples Without DNA: Samples retained, with no potential for DNA extraction from any retained samples (e.g., fixed tissue, plasma)

Study Officials

• Li Liang

  Nanfang Hospital, Southern Medical University

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 27, 2025
• First Posted: December 10, 2025
• Study Start: October 1, 2025
• Primary Completion (Estimated): July 1, 2028
• Study Completion (Estimated): August 1, 2028
• Last Updated: February 10, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (2)