Development and Validation of an AI Foundation Model for CNS Tumor Classification (CNS-AIClass)

June 29, 2026 updated by: Jinsong Wu, Huashan Hospital

Development and Validation of an Artificial Intelligence Foundation Model for Hierarchical Classification of Central Nervous System Tumors Using Hematoxylin and Eosin Whole-Slide Images

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.

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Not yet recruiting

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Detailed Description

Central nervous system tumors comprise a highly heterogeneous group of neoplasms with substantial diagnostic complexity. The WHO 2021 Classification of Tumors of the Central Nervous System integrates histology with molecular biomarkers, making accurate diagnosis increasingly dependent on molecular features such as IDH mutation, 1p/19q codeletion, H3 alterations, TERT promoter mutation, and other genomic or epigenomic markers. However, broad implementation of comprehensive molecular testing remains limited in many settings because of cost, turnaround time, technical complexity, and tissue constraints.

This retrospective study will use archived formalin-fixed paraffin-embedded H&E glass slides or existing digital WSIs from approximately 20,000 patients with primary or secondary CNS tumors treated at Huashan Hospital, Fudan University and Shandong Provincial Hospital. Slides will be digitized when necessary, de-identified, quality controlled, segmented for tissue regions, and divided into image patches. Patch-level features will be extracted using pretrained image encoders, including ResNet50, UNI, and CONCH, followed by weakly supervised multiple-instance learning aggregation methods such as attention-based MIL and CLAM.

The primary objective is to develop and internally validate an AI model capable of hierarchical CNS tumor classification, including tumor category, tumor family, and terminal WHO 2021 diagnosis. Secondary objectives are to compare alternative model architectures, evaluate prediction performance for key molecular markers, assess model interpretability with attention mapping, and compare AI-only, pathologist-only, and AI-assisted diagnosis on an internal test subset.

No intervention will be delivered to participants, and no clinical treatment decisions will be based on model outputs during this research stage. All data processing and model development will be conducted on secure in-hospital servers using de-identified data in accordance with institutional ethics approval and data protection procedures. ClinicalTrials.gov defines observational studies as studies in which investigators assess outcomes without assigning interventions, which matches this study design.

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Estimated)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
20000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact

The name and contact information for the person who can answer enrollment questions for a clinical study. Each location where the study is being conducted may also have a specific contact, who may be better able to answer those questions.
  • Name: Jinsong Wu, MD, PhD
  • Phone Number: 86-21-52887200
  • Email: wjsongc@126.com

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

  1. 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.
  2. Insufficient viable tumor tissue or insufficient analyzable tumor area for patch extraction.
  3. Missing or uncertain pathological diagnosis that cannot be reliably reassigned according to the WHO 2021 CNS tumor classification using available records.
  4. Cases lacking sufficient clinical, pathological, or molecular information required for core study analyses.
  5. Other cases determined by the investigators to be unsuitable for algorithm training or evaluation after quality control review.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort

Group / Cohort

A group or subgroup of participants in an observational study that is assessed for biomedical or health outcomes.
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.

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
Hierarchical CNS tumor classification performance on the internal hold-out test set
Time Frame: Assessed at model evaluation after completion of training, up to Jul 2029
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 Outcome Measures

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
Comparative performance of alternative feature extractors and MIL aggregation methods
Time Frame: Up to Jul 2029
Comparison of model performance across feature extractors (ResNet50, UNI, CONCH) and aggregation methods (ABMIL, CLAM) on the internal validation and hold-out test datasets using AUC, balanced accuracy, sensitivity, specificity, weighted F1 score, and MCC.
Up to Jul 2029
Prediction performance for selected molecular biomarkers
Time Frame: Up to Jul 2029
Performance of the AI model in predicting selected molecular alterations from H&E whole-slide images, including but not limited to IDH1/2 mutation, 1p/19q codeletion, H3 K27M/G34 alteration, TERT promoter mutation, and BRAF V600E, measured by AUC, sensitivity, specificity, and MCC.
Up to Jul 2029
Agreement between AI attention maps and neuropathologist-identified diagnostic regions
Time Frame: Up to Jul 2029
Qualitative and semi-quantitative interpretability assessment of overlap between model attention heatmaps and diagnostically relevant regions identified independently by expert neuropathologists.
Up to Jul 2029
Human versus AI versus AI-assisted diagnostic performance
Time Frame: Up to Jul 2029
Comparison of diagnostic accuracy, inter-rater agreement, and slide review time among AI-only diagnosis, pathologist-only diagnosis, and AI-assisted pathologist diagnosis on a selected internal test subset. Inter-rater agreement will be evaluated using Cohen's kappa where appropriate.
Up to Jul 2029

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Huashan Hospital

Collaborators

Collaborators

An organization other than the sponsor that provides support for a clinical study. This support may include activities related to funding, design, implementation, data analysis, or reporting.
Shandong Provincial Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
August 1, 2026

Primary Completion (Estimated)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
July 30, 2027

Study Completion (Estimated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
July 30, 2029

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
June 29, 2026

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
June 29, 2026

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
July 6, 2026

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
July 6, 2026

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
June 29, 2026

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
June 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • KY2023-977

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Cookie Settings

We use cookies to ensure that we give you the best experience on our website. For more details carefully read our Privacy and Cookies Policy. ...>>>You can change your preferences or withdraw your consent at any time by deleting the cookies from your website or computer as described in the policy. Please accept it and choose your preferences by ticking the corresponding boxes in the "Manage Settings" section below. Please carefully read our Terms of Service before you proceed with using any part of this website.
«Manage Settings