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.
Eligibility
Sex
ALL
Min age
—
Max age
—
Healthy volunteers
No
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.
Primary outcome measure(s)
Area Under the Receiver Operating Characteristic Curve — 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. 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.
Trial sites (1)
Facility
City
Region
Status
Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, Guangdong
Guangzhou
China
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This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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