PancancerNCT07157618
Prospective Pathology Foundation Models
An observational study of Pancancer, run by Nanfang Hospital, Southern Medical University at 2 sites in China.
Trial summary
Histopathology remains the gold standard for disease diagnosis, yet faces challenges including pathologist shortages and diagnostic model limitations. This underscores the critical need to develop deep learning-based pathology foundation models integrating prospective imaging and clinical data. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Pancancer
- How many people
- 2,000 participants
- Where
- 2 sitesChina
- Study dates
- 28 Aug 2025to Aug 2028
- 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 18 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Inclusion criteria comprised patients aged 18-75 years with definitive pathological diagnoses. All cases were prospectively collected from Nanfang Hospital, Southern Medical University (NFHSMU) .
Who may be able to join
- Aged 18-75 years old.
- Patients with complete pathological slides and clinical information.
Who cannot join
- 1.Patients with missing data or specimens not meeting quality control requirements for analysis.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites, 2 recruiting
- Recruiting
Nanfang Hospital, Southern Medical University
Guangzhou, Guangdong, 510515
Li Liang, +8615989146266, lli@smu.edu.cn
Zhengyu Zhang, +8613837365993, zzyusmu@163.com
- Recruiting
Qianfoshan Hospital
Jinan, Shandong, 250014
Chenglong Zhao, +8615269183992
About this study
Official title: Development and Clinical Application of Deep Learning-Based Prospective Pathology Foundation Models
Histopathology remains the gold standard for disease diagnosis, yet faces challenges including pathologist shortages and diagnostic model limitations. This underscores the critical need to develop deep learning-based pathology foundation models integrating prospective imaging and clinical data. Such models would enhance diagnostic accuracy and efficiency, enabling tumor grading, histo-molecular classification, and intelligent chemotherapy guidance - ultimately optimizing clinical workflows. However, a critical gap remains: the absence of prospectively validated, pan-disease pathology foundation models. Developing clinically validated models is therefore imperative.
- Conditions
- Pancancer
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 |
|---|---|---|
| QFSH external validation dataset | 1000 slides from 1000 eligible individuals were obtained in the Qianfoshan Hospital (QFSH, Jinan, China) between January 2020 and July 2025, which was used to validate the pathology foundation models. | |
| Prospective validation datasets | We conducted a prospective validation study to compare the diagnostic performance among pathologists, our pathology foundation model, and pathologist-with-AI-assisted diagnosis. This study was initiated on August 28, 2025 at Nanfang Hospital, Southern Medical University, The First Affiliated Hospital, Zhejiang University School of Medicine, The First Affiliated Hospital of Zhengzhou University, Zhujiang Hospital, Southern Medical University, Zhongshan People's Hospital, The First Affiliated Hospital of Shantou University Medical College, and Xijing Hospital, Fourth Military Medical University. | |
| Randomized controlled trial | We conducted a prospective randomized controlled trial to compare the diagnostic performance among pathologists, the pathology foundation model, and pathologist-with-model-assisted diagnosis at Nanfang Hospital of Southern Medical University (NFHSMU).The trial commenced data collection on August 28, 2025 to establish the NFHSMU randomized controlled trial dataset. Following quality control, the slides were randomly allocated (1:1:1 ratio) into three groups:Model-only group/Pathologist-only group/Model-assisted pathologist group. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under ROC curve (AUC)
Area under the curve
Measured: Diagnostic evaluation will be performed within 1 week when the WSIs are obtained
Secondary outcomes (2)
Specificity
The true negative rate (TNR) of the diagnostic platform, which is the ratio between the number of negative individuals correctly categorized by platform and the total number of actual negative individuals (%).
Measured: Diagnostic evaluation will be performed within 1 week when the WSIs are obtained
Sensitivity
The true positive rate (TPR) of the diagnostic platform, which is the ratio between the number of positive individuals correctly categorized by platform and the total number of actual positive individuals (%).
Measured: Diagnostic evaluation will be performed within 1 week when the WSIs are obtained
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Sep 2025
- Study start28 Aug 2025
- Primary completionJul 2028 (estimated)
- Study completionAug 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PancancerStudies of the same condition, open and under way.
- 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
- Prospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Nanfang Hospital, Southern Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- Qianfoshan Hospital, Zhejiang University, The First Affiliated Hospital of Zhengzhou University, First Affiliated Hospital of Shantou University Medical College, Zhujiang Hospital, Air Force Military Medical University, China
- Responsible party
- Sponsor
- Investigators
- Li Liang, Study director, Nanfang Hospital, Southern Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07157618
- Sponsor's protocol ID
- NFEC-2025-403
- Status last verified
- Apr 2026
- First submitted
- 28 Aug 2025
- Last updated
- 23 Apr 2026
- Results posted
- No
- Individual data sharing
- No: Requests for the data collected and analyzed in this study will be considered if the application is in line with public benefits and the applicant is willing to sign a data access agreement. Contact can be through the corresponding author.
Study at a glance
From the registry record, last updated by the sponsor on 23 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Clinical Application of Deep Learning-Based Prospective Pathology Foundation Models
- ClinicalTrials.gov ID
- NCT07157618
- Acronym / sponsor ID
- NFEC-2025-403
- Conditions
- Pancancer
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Nanfang Hospital, Southern Medical UniversityUniversity, hospital or other
- Collaborators
- Qianfoshan Hospital, Zhejiang University, The First Affiliated Hospital of Zhengzhou University, First Affiliated Hospital of Shantou University Medical College, Zhujiang Hospital, Air Force Military Medical University, China
- Study start
- 28 Aug 2025 (actual)
- Primary completion
- Jul 2028 (estimated)
- Study completion
- Aug 2028 (estimated)
- First posted
- 5 Sep 2025 (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.
Histopathology remains the gold standard for disease diagnosis, yet faces challenges including pathologist shortages and diagnostic model limitations. This underscores the critical need to develop deep learning-based pathology foundation models integrating prospective imaging and clinical data. Such models would enhance diagnostic accuracy and efficiency, enabling tumor grading, histo-molecular classification, and intelligent chemotherapy guidance - ultimately optimizing clinical workflows. However, a critical gap remains: the absence of prospectively validated, pan-disease pathology foundation models. Developing clinically validated models is therefore imperative.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| QFSH external validation dataset | – | –1000 slides from 1000 eligible individuals were obtained in the Qianfoshan Hospital (QFSH, Jinan, China) between January 2020 and July 2025, which was used to validate the pathology foundation models. |
| Prospective validation datasets | – | –We conducted a prospective validation study to compare the diagnostic performance among pathologists, our pathology foundation model, and pathologist-with-AI-assisted diagnosis. This study was initiated on August 28, 2025 at Nanfang Hospital, Southern Medical University, The First Affiliated Hospital, Zhejiang University School of Medicine, The First Affiliated Hospital of Zhengzhou University, Zhujiang Hospital, Southern Medical University, Zhongshan People's Hospital, The First Affiliated Hospital of Shantou University Medical College, and Xijing Hospital, Fourth Military Medical University. |
| Randomized controlled trial | – | –We conducted a prospective randomized controlled trial to compare the diagnostic performance among pathologists, the pathology foundation model, and pathologist-with-model-assisted diagnosis at Nanfang Hospital of Southern Medical University (NFHSMU).The trial commenced data collection on August 28, 2025 to establish the NFHSMU randomized controlled trial dataset. Following quality control, the slides were randomly allocated (1:1:1 ratio) into three groups:Model-only group/Pathologist-only group/Model-assisted pathologist group. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Inclusion criteria comprised patients aged 18-75 years with definitive pathological diagnoses. All cases were prospectively collected from Nanfang Hospital, Southern Medical University (NFHSMU) .
Key inclusion criteria (2 listed)
- Aged 18-75 years old.
- Patients with complete pathological slides and clinical information.
Key exclusion criteria (1 listed)
- 1.Patients with missing data or specimens not meeting quality control requirements for analysis.
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 ROC curve (AUC)
Time frame: Diagnostic evaluation will be performed within 1 week when the WSIs are obtained
Area under the curve
2 secondary outcomes
Specificity
Time frame: Diagnostic evaluation will be performed within 1 week when the WSIs are obtained
Sensitivity
Time frame: Diagnostic evaluation will be performed within 1 week when the WSIs are obtained
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 23 Apr 2026.
- Individual participant dataSharing planned: No. Requests for the data collected and analyzed in this study will be considered if the application is in line with public benefits and the applicant is willing to sign a data access agreement. Contact can be through the corresponding author.
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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.
Li Liang study director
Nanfang Hospital, Southern Medical University
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pancancer, run by Nanfang Hospital, Southern Medical University at 2 sites 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 2 inclusion and 1 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: looking for participants now, as of the sponsor's last update on 23 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites 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?
Contact Zhengyu Zhang (+8613837365993, zzyusmu@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under ROC curve (AUC) (time frame: Diagnostic evaluation will be performed within 1 week when the WSIs are obtained). 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 23 Apr 2026.