Brain TumorsCNS-AIClassNCT07685301
Development and Validation of an AI Foundation Model for CNS Tumor Classification
An observational study of Brain Tumors and Central Nervous System Neoplasms, run by Huashan Hospital.
Trial 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… Read more about this study
- Who is it for?
- Ages 9 years and older
- Conditions the study is for
- Brain Tumors, Central Nervous System Neoplasms
- How many people
- 20,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Aug 2026to 30 Jul 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 9 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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.
Read the 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.
- Conditions
- Brain Tumors, Central Nervous System Neoplasms
- Keywords
- Artificial Intelligence, Brain Tumor, CNS Tumor, Whole-Slide Imaging, Computational Pathology, Digital Pathology
- Medical subject headings
- Brain Neoplasms, Central Nervous System Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 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
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.
Measured: Up to Jul 2029
Prediction performance for selected molecular biomarkers
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.
Measured: Up to Jul 2029
Agreement between AI attention maps and neuropathologist-identified diagnostic regions
Qualitative and semi-quantitative interpretability assessment of overlap between model attention heatmaps and diagnostically relevant regions identified independently by expert neuropathologists.
Measured: Up to Jul 2029
Human versus AI versus AI-assisted diagnostic performance
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.
Measured: Up to Jul 2029
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jul 2026
- Study start1 Aug 2026 (estimated)
- Primary completion30 Jul 2027 (estimated)
- Study completion30 Jul 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Brain TumorsStudies of the same condition, open and under way.
- More trials run by Huashan HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 20,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Huashan Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Shandong Provincial Hospital
- Responsible party
- Principal investigator, Jinsong Wu, Huashan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07685301
- Sponsor's protocol ID
- KY2023-977
- Acronym
- CNS-AIClass
- Status last verified
- Jun 2026
- First submitted
- 29 Jun 2026
- Last updated
- 6 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 6 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of an Artificial Intelligence Foundation Model for Hierarchical Classification of Central Nervous System Tumors Using Hematoxylin and Eosin Whole-Slide Images
- ClinicalTrials.gov ID
- NCT07685301
- Acronym / sponsor ID
- CNS-AIClass / KY2023-977
- Conditions
- Brain Tumors, Central Nervous System Neoplasms
- Keywords
- Artificial Intelligence, Brain Tumor, CNS Tumor, Whole-Slide Imaging, Computational Pathology, Digital Pathology
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 20,000 participants (estimated)
- Lead sponsor
- Huashan HospitalUniversity, hospital or other
- Collaborators
- Shandong Provincial Hospital
- Study start
- 1 Aug 2026 (estimated)
- Primary completion
- 30 Jul 2027 (estimated)
- Study completion
- 30 Jul 2029 (estimated)
- First posted
- 6 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 9 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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).
4 secondary outcomes
Comparative performance of alternative feature extractors and MIL aggregation methods
Time frame: Up to Jul 2029
Prediction performance for selected molecular biomarkers
Time frame: Up to Jul 2029
Agreement between AI attention maps and neuropathologist-identified diagnostic regions
Time frame: Up to Jul 2029
Human versus AI versus AI-assisted diagnostic performance
Time frame: Up to Jul 2029
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jul 2026.
- Individual participant dataSharing planned: Undecided
Related trials for Brain Tumors
RecruitingPhase 3
Beginning Radiation Immediately With GammaTile at GBM Excision Versus Standard of Care
Glioblastoma
RecruitingPhase 2
BrAin Metastasis in tripLe Negateive Breast Cancer: IvoneScimab and Trop2 ADC
Triple-Negative Breast Cancer (TNBC), Brain Metastases
RecruitingPhase 1/2
Testing the Addition of an Anti-cancer Drug, Selinexor, to the Usual Chemotherapy Treatment (Temozolomide) for Brain Tumors That Have Returned After Previous Treatment
Recurrent Glioblastoma, IDH-Wildtype, Recurrent MGMT-Methylated Glioblastoma
Active, not recruiting
Effects of Whole-body Electrical Muscle Stimulation Exercise on Adults With Neuromuscular Disease
Neuromuscular Diseases (NMD), Amyotrophic Lateral Sclerosis
Active, not recruitingPhase 1
Prospective Exploratory Study of FAPi PET/CT With Histopathology Validation in Patients With Various Cancers
Bladder Carcinoma, Cervical Carcinoma
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Brain Tumors and Central Nervous System Neoplasms, run by Huashan Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 6 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jinsong Wu, MD, PhD (86-21-52887200, wjsongc@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Jul 2026.