Retrospective Pathology Foundation Models

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

Status

Enrolling by invitation

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07239297

Enrollment

2000

Registered

2025-11-20

Start date

2025-08-01

Completion date

2028-07-31

Last updated

2025-11-20

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

Conditions

Pancancer

Brief summary

By integrating retrospective multimodal data such as pathology and imaging, AI technologies offer novel solutions for disease classification, tumor grading, histological and molecular subtyping, selection of chemotherapy regimens, risk stratification, and treatment-response prediction. This research direction not only deepens our understanding of tumor biological characteristics but also provides essential support for precision medicine and individualized therapy. It holds significant theoretical and practical value and has important implications for mitigating strained medical resources and improving the accuracy of therapeutic decision-making, representing a cutting-edge application with substantial translational potential.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

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

Outcome results