Clinical trial · Observational
An Artificial Intelligence Model for Aiding Claudin18.2 Expression Diagnosis in Gastric Adenocarcinoma
Development and Real-World Validation of an Intelligent Interpretation Model for Claudin18.2 Protein Expression in Digestive System Tumors
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Digestive Oncology | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (3)
- label
- Prospective validation dataset
- description
- 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)
- label
- QFSH external validation dataset
- description
- 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.
- label
- Randomized controlled trial
- description
- 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.
Primary outcomes (1)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available