Development and Validation of an AI Foundation Model for Frozen-Section Pathology
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 33,000
- 试验地点
- 1
- 主要终点
- Area Under the Receiver Operating Characteristic Curve
研究概览
简要总结
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.
详细描述
Frozen-section pathology is an essential component of intraoperative consultation and provides rapid diagnostic information that may support surgical decision-making. However, frozen-section images differ substantially from formalin-fixed, paraffin-embedded tissue sections because of freezing artifacts, variable section quality, staining variation, and differences in tissue morphology. Most currently available pathology foundation models have been developed primarily using formalin-fixed, paraffin-embedded pathology images and may have limited generalizability to frozen-section pathology.
This multicenter, observational study is designed to develop and validate an artificial intelligence foundation model for frozen-section pathology. The study consists of a retrospective phase and a prospective validation phase.
In the retrospective phase, patients with frozen-section pathology data collected from January 2019 to April 2026 will be included for model development, internal validation, and external validation. The retrospective development and internal validation datasets will include patients from Sun Yat-sen Memorial Hospital, Sun Yat-sen University Cancer Center, and the Third Affiliated Hospital of Sun Yat-sen University. Retrospective external validation will include patients from collaborating hospitals, including Shantou Central Hospital, the First Affiliated Hospital of Shantou University Medical College, and the Fifth Affiliated Hospital of Sun Yat-sen University.
In the prospective phase, patients undergoing intraoperative frozen-section examination will be enrolled from June 2026 to October 2026, including patients from Sun Yat-sen Memorial Hospital and patients from the Fifth Affiliated Hospital of Sun Yat-sen University. The study population will include patients with benign or malignant diseases involving the lung, thyroid, stomach, colorectum, liver, bladder, prostate, kidney, uterus, and ovary.
For each eligible patient, clinical information, frozen-section whole-slide images, intraoperative pathology reports, and corresponding pathological data will be collected. Whole-slide images will undergo tissue detection, region-of-interest segmentation, and patch-level image processing. The model will be pretrained using self-supervised learning and subsequently fine-tuned for prespecified downstream frozen-section pathology tasks using multiple-instance learning or Transformer-based aggregation methods. Model interpretability will be explored using gradient-weighted class activation mapping.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
结局指标
主要结局
Area Under the Receiver Operating Characteristic Curve
时间窗: 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.
次要结局
- 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.)