Building prospective, pan‑cancer pathology AI models
Official title: Development and Clinical Application of Deep Learning-Based Prospective Pathology Foundation Models
Run by Nanfang Hospital, Southern Medical University · NCT07157618 · Data updated 2026-11-12
This project will test whether AI models trained on new pathology images and clinical data can help improve diagnosis and treatment guidance for adults with cancer.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 75 Years |
| Sex | All |
| Enrollment | 2000 (estimated) |
| Sponsor | Nanfang Hospital, Southern Medical University Academic / other |
| Drugs / interventions | chemotherapy |
| Locations | 2 sites (Guangzhou, Guangdong and 1 other locations) |
| Status | Recruiting |
| Started | August 28, 2025 |
| Main results expected | July 2028 |
| Study ends | August 2028 |
| Registry record updated | April 23, 2026 |
| Trial ID | NCT07157618 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults aged 18–75 with complete pathological slides and linked clinical information for their cancer are ideal candidates.
✕Probably not a fit
Patients outside the 18–75 age range or those with missing data or specimens that fail quality control are unlikely to benefit from participation.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
The project prospectively collects histopathology slides and linked clinical data across multiple cancer types to train large deep‑learning 'foundation' pathology models. These models are intended to support tasks such as tumor grading, histo‑molecular classification, and intelligent chemotherapy guidance by learning from real-world, forward‑looking clinical samples. The approach uses standardized quality control of specimens and centralized model development using data from participating hospitals. The goal is to produce models that are validated on prospectively acquired data rather than only on retrospective archives.
Why it matters
Potential benefit: If successful, these models could speed up and improve diagnostic accuracy, provide additional molecular and treatment guidance from routine slides, and reduce workload on pathologists.
How similar studies have gone: Retrospective deep‑learning studies on histopathology have shown promising results, but prospectively validated, pan‑disease foundation pathology models remain largely novel and unproven.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Aged 18-75 years old. 2. Patients with complete pathological slides and clinical information. Exclusion Criteria: 1.Patients with missing data or specimens not meeting quality control requirements for analysis.
Where it's running
-
Nanfang Hospital, Southern Medical University
Guangzhou, Guangdong, China Recruiting -
Qianfoshan Hospital
Jinan, Shandong, China Recruiting
Study contacts
- Study coordinator: Zhengyu Zhang
- Email: zzyusmu@163.com
- Phone: +8613837365993
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.