Development and Validation of an AI Foundation Model for CNS Tumor Classification

NCT07685301 | Not Yet Recruiting

• 1 other identifier: KY2023-977
• Study Type: observational
• Target: 20,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

This is a multi-center, retrospective, observational study to develop and internally validate an artificial intelligence (AI) foundation model for hierarchical classification of central nervous system (CNS) tumors using approximately 20,000 hematoxylin and eosin (H\&E) whole-slide images (WSIs) collected at Huashan Hospital Fudan University and Shandong Provincial Hospital. Archived pathology slides and linked de-identified clinical, histopathological, and molecular diagnostic data from patients who underwent neurosurgical tumor resection or biopsy between January 1, 2010 and December 31, 2025 will be retrospectively analyzed. The study aims to train and evaluate weakly supervised multiple-instance learning models using pathology foundation models and conventional convolutional neural network feature extractors to predict tumor category, tumor family, terminal WHO 2021 CNS tumor diagnosis, and selected molecular alterations directly from routine H\&E slides. Internal model validation will be performed using patient-level training, validation, and hold-out test datasets. Secondary analyses include comparison of model architectures, virtual molecular profiling, interpretability analyses using attention heatmaps, and comparison of AI-assisted versus pathologist-only diagnostic performance on selected internal test cases.

Conditions

Brain Tumors; Central Nervous System Neoplasms

Keywords

Artificial Intelligence; Brain Tumor; CNS Tumor; Whole-Slide Imaging; Computational Pathology; Digital Pathology

Outcome Measures

Primary Outcomes (1)

• Hierarchical CNS tumor classification performance on the internal hold-out test set

  Diagnostic performance of the final AI model for hierarchical classification of CNS tumors at the tumor category, tumor family, and terminal WHO 2021 diagnosis levels using de-identified H\&E whole-slide images. Performance metrics will include macro- and/or micro-area under the receiver operating characteristic curve (AUC), balanced accuracy, weighted F1 score, and Matthews correlation coefficient (MCC).

  Assessed at model evaluation after completion of training, up to Jul 2029

Secondary Outcomes (4)

• Comparative performance of alternative feature extractors and MIL aggregation methods

  Up to Jul 2029

• Prediction performance for selected molecular biomarkers

  Up to Jul 2029

• Agreement between AI attention maps and neuropathologist-identified diagnostic regions

  Up to Jul 2029

• Human versus AI versus AI-assisted diagnostic performance

  Up to Jul 2029

Study Arms (1)

CNS Tumor Retrospective Cohort

Retrospective cohort of approximately 20,000 patients with primary or secondary CNS tumors treated surgically at Huashan Hospital, Fudan University, with archived H\&E slides and linked de-identified clinical, pathological, and molecular diagnostic data used for AI model development and internal validation.

Eligibility Criteria

• Age: 9 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of pediatric (≥9) and adult patients of any sex who underwent neurosurgical resection or biopsy for a suspected central nervous system (CNS) tumor at Huashan Hospital, Fudan University, between January 1, 2010 and December 31, 2025, and who have an available postoperative pathological diagnosis, archived hematoxylin and eosin (H\&E) stained slides and/or digital whole-slide images, and sufficient linked de-identified clinical, pathological, and molecular data for retrospective analysis. The cohort includes patients with primary or secondary CNS tumors for whom routine clinical care generated pathology materials suitable for computational pathology analysis.

You may qualify if:

• Patients who underwent brain or spinal tumor resection or biopsy at Huashan Hospital Fudan University and Shandong Provincial Hospital.
• Postoperative pathology diagnosis consistent with a primary or secondary central nervous system tumor.
• Availability of archived routine H\&E-stained glass slides or existing digital whole-slide image files of adequate quality for analysis.
• Availability of essential de-identified clinical and pathological information, including age, sex, tumor location, and key surgical/pathology records.
• Use of archived data and samples permitted under institutional ethics approval, including waiver of informed consent where applicable.

You may not qualify if:

• Severe slide preparation or scanning artifacts that preclude meaningful computational analysis, including extensive tissue folding, severe bubbles, severe detachment, markedly uneven staining/fading, or severe out-of-focus scanning.
• Insufficient viable tumor tissue or insufficient analyzable tumor area for patch extraction.
• Missing or uncertain pathological diagnosis that cannot be reliably reassigned according to the WHO 2021 CNS tumor classification using available records.
• Cases lacking sufficient clinical, pathological, or molecular information required for core study analyses.
• Other cases determined by the investigators to be unsuitable for algorithm training or evaluation after quality control review.

Sponsors & Collaborators

• Huashan Hospital: lead
• Shandong Provincial Hospital: collaborator

MeSH Terms

Conditions

Brain Neoplasms; Central Nervous System Neoplasms

Condition Hierarchy (Ancestors)

Nervous System Neoplasms; Neoplasms by Site; Neoplasms; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases

Central Study Contacts

Jinsong Wu, MD, PhD

CONTACT

86-21-52887200; wjsongc@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: June 29, 2026
• First Posted: July 6, 2026
• Study Start: August 1, 2026
• Primary Completion (Estimated): July 30, 2027
• Study Completion (Estimated): July 30, 2029
• Last Updated: July 6, 2026

Record last verified: 2026-06