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
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.
- 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