CancerNCT07708207
Development and Validation of an AI Foundation Model for Frozen-Section Pathology
An observational study of Cancer and Intraoperative Pathology, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Cancer, Intraoperative Pathology, Artificial Intelligence (AI)
- How many people
- 33,000 participants
- Where
- 1 siteChina
- Study dates
- 27 Apr 2026to 1 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, Guangdong
Guangzhou
About this study
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.
Read more
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.
Read the detailed description
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.
The primary outcome will be the discriminative performance of the artificial intelligence foundation model, assessed using the area under the receiver operating characteristic curve. Secondary outcomes will include accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and robustness across participating centers, organ systems, and image-quality conditions.
- Medical subject headings
- Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Diagnostic accuracy will be calculated as the proportion of correctly classified cases among all evaluable cases for each prespecified frozen-section pathology task at the prespecified operating threshold.
Measured: 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.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jul 2026
- Study start27 Apr 2026
- Primary completion1 Oct 2026 (estimated)
- Study completion1 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CancerStudies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 33,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07708207
- Sponsor's protocol ID
- SYSKY-2026-505-01
- Status last verified
- Jul 2026
- First submitted
- 12 Jul 2026
- Last updated
- 16 Jul 2026
- Results posted
- No
- Individual data sharing
- No: To protect patient privacy, pathological slide images and other patient-related data are not publicly accessible.
Study at a glance
From the registry record, last updated by the sponsor on 16 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of an AI Foundation Model for Frozen-Section Pathology
- ClinicalTrials.gov ID
- NCT07708207
- Acronym / sponsor ID
- SYSKY-2026-505-01
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 33,000 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 27 Apr 2026 (actual)
- Primary completion
- 1 Oct 2026 (estimated)
- Study completion
- 1 Dec 2026 (estimated)
- First posted
- 16 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
The primary outcome will be the discriminative performance of the artificial intelligence foundation model, assessed using the area under the receiver operating characteristic curve. Secondary outcomes will include accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and robustness across participating centers, organ systems, and image-quality conditions.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Area Under the Receiver Operating Characteristic Curve
Time frame: 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.
1 secondary outcome
Diagnostic Accuracy
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Jul 2026.
- Individual participant dataSharing planned: No. To protect patient privacy, pathological slide images and other patient-related data are not publicly accessible.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cancer and Intraoperative Pathology, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 16 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Receiver Operating Characteristic Curve (time frame: 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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Jul 2026.