Retrospective Pathology Foundation Models

Nanfang Hospital, Southern Medical University

Status: Invitation-only

Conditions: Pancancer

Study type: Observational

Funder types: Other

Identifiers

NCT07239297

NFEC-2025-419

Details and patient eligibility

About

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.

Enrollment: 2,000 estimated patients

Sex: All

Ages: 18 to 75 years old

Volunteers: No 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.

Trial design

2,000 participants in 2 patient groups

QFSH external validation dataset

Description:

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.

NFH dataset

Description:

We conducted a validation study to compare the diagnostic performance among pathologists, our pathology foundation model, and pathologist-with-AI-assisted diagnosis. This study was initiated at Nanfang Hospital, Southern Medical University (NFHSMU), with patient enrollment from January 1, 2011 to July 31, 2024.