Prospective Pathology Foundation Models

Development and Clinical Application of Deep Learning-Based Prospective Pathology Foundation Models

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07157618

Enrollment

2000

Registered

2025-09-05

Start date

2025-08-28

Completion date

2028-08-01

Last updated

2026-04-23

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Pancancer

Brief 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. 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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 75 Years

Healthy volunteers

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.

Design outcomes

Primary

Measure	Time frame	Description
Area under ROC curve (AUC)	Diagnostic evaluation will be performed within 1 week when the WSIs are obtained	Area under the curve

Secondary

Measure	Time frame	Description
Specificity	Diagnostic evaluation will be performed within 1 week when the WSIs are obtained	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 (%).
Sensitivity	Diagnostic evaluation will be performed within 1 week when the WSIs are obtained	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 (%).

Countries

China

Contacts

CONTACTZhengyu Zhang

zzyusmu@163.com+8613837365993

STUDY_DIRECTORLi Liang

Nanfang Hospital, Southern Medical University

Outcome results