Clinical trial · Observational
Development and Validation of an AI Foundation Model for Frozen-Section Pathology
- 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)
This multicenter observational study aims to develop and validate an artificial intelligence foundation model for frozen-section pathology. The study includes a retrospective phase and a prospective validation phase. Retrospective frozen-section pathology data will be used for model development, internal validation, and external validation. A prospective multicenter cohort of patients undergoing intraoperative frozen-section examination will then be enrolled to evaluate the model in a real-world clinical setting. The model will analyze digitized frozen-section whole-slide images and will be evaluated for prespecified frozen-section pathology diagnostic tasks across multiple organ systems. Its performance will be assessed using pathological reference standards. The primary outcome is the area under the receiver operating characteristic curve. Secondary outcomes include accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. This study is observational and will not require research-mandated changes to routine clinical care.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence (AI) | — | UNRESOLVED | — |
| Cancer | Malignant Neoplasm | ALIAS | 0.90 |
| Intraoperative Pathology | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (3)
- label
- Retrospective Model Development Cohort
- description
- Approximately 27,000 patients with frozen-section pathology data collected retrospectively at Sun Yat-sen Memorial Hospital, Sun Yat-sen University Cancer Center, and the Third Affiliated Hospital of Sun Yat-sen University. This cohort will be used for model development.
- label
- Retrospective External Validation Cohort
- description
- Approximately 3,000 patients with frozen-section pathology data collected retrospectively from external collaborating hospitals. This cohort will be used to evaluate the generalizability and robustness of the artificial intelligence foundation model.
- label
- Prospective Multicenter Validation Cohort
- description
- Approximately 3,000 consecutive patients undergoing intraoperative frozen-section examination at Sun Yat-sen Memorial Hospital and the Fifth Affiliated Hospital of Sun Yat-sen University from June 2026 to October 2026. This cohort will be used for prospective validation of the artificial intelligence foundation model.
Primary outcomes (1)
- measure
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * Patients undergoing surgery with intraoperative frozen-section pathological examination. * Availability of complete clinical information and intraoperative frozen-section pathology records. * Availability of digitized frozen-section whole-slide images suitable for artificial intelligence analysis. Exclusion Criteria: * Frozen-section whole-slide images with inadequate quality for evaluation, including substantial blur, ghosting, severe artifacts, or insufficient diagnostic tissue. * Missing or indeterminate key clinical, intraoperative pathology, or pathological reference data required for the prespecified study task. * Withdrawal of informed consent in the prospective validation cohort, where applicable.
References
Publications (0)
Data not yet available