Development and Validation of an AI Foundation Model for Frozen-Section Pathology

NCT07708207 | Active Not Recruiting

• 1 other identifier: SYSKY-2026-505-01
• Study Type: observational
• Target: 33,000
• Locations: 1 country
• Sites: 1

Brief Summary

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

Cancer; Intraoperative Pathology; Artificial Intelligence (AI)

Outcome Measures

Primary Outcomes (1)

• Area Under the Receiver Operating Characteristic Curve

  The area under the receiver operating characteristic curve (AUROC) will be calculated to evaluate the discriminative performance of the artificial intelligence foundation model for each prespecified frozen-section pathology diagnostic or prediction task. The reference standard will be the corresponding pathological diagnosis specified in the study protocol and statistical analysis plan. Higher AUROC values indicate better discriminative performance.

  For each enrolled patient, the diagnosis results of AI model will be obtained in several days after intraoperative pathology completion, and the AUROC of the AI model will be evaluated through study completion, an average of 3 year.

Secondary Outcomes (1)

• Diagnostic Accuracy

  For each enrolled patient, the diagnosis results of AI model will be obtained in several days after intraoperative pathology completion, and the accuracy of the AI model will be evaluated through study completion, an average of 3 years.

Study Arms (3)

Retrospective Model Development Cohort

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.

Retrospective External Validation Cohort

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.

Prospective Multicenter Validation Cohort

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.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population includes patients undergoing intraoperative frozen-section pathological examination at participating tertiary hospitals in China. The study will include patients with benign or malignant diseases involving various organs. Both retrospective and prospective cohorts will be included.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead

Study Sites (1)

Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, Guangdong

Guangzhou, China

Location

MeSH Terms

Conditions

Neoplasms

Study Design

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

Study Record Dates

• First Submitted: July 12, 2026
• First Posted: July 16, 2026
• Study Start: April 27, 2026
• Primary Completion (Estimated): October 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: July 16, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)