Development and Validation of an AI Foundation Model for CNS Tumor Classification (CNS-AIClass)
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.
Aperçu de l'étude
Statut
Les conditions
Description détaillée
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.
Type d'étude
Inscription (Estimé)
Contacts et emplacements
Coordonnées de l'étude
- Nom: Jinsong Wu, MD, PhD
- Numéro de téléphone: 86-21-52887200
- E-mail: wjsongc@126.com
Critères de participation
Critère d'éligibilité
Âges éligibles pour étudier
- Enfant
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
Méthode d'échantillonnage
Population étudiée
La description
Inclusion Criteria:
- 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.
Exclusion Criteria:
- 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.
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
Cohortes et interventions
Groupe / Cohorte |
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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.
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Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
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Hierarchical CNS tumor classification performance on the internal hold-out test set
Délai: Assessed at model evaluation after completion of training, up to Jul 2029
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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).
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Assessed at model evaluation after completion of training, up to Jul 2029
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Mesures de résultats secondaires
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
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Comparative performance of alternative feature extractors and MIL aggregation methods
Délai: Up to Jul 2029
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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.
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Up to Jul 2029
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Prediction performance for selected molecular biomarkers
Délai: Up to Jul 2029
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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.
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Up to Jul 2029
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Agreement between AI attention maps and neuropathologist-identified diagnostic regions
Délai: Up to Jul 2029
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Qualitative and semi-quantitative interpretability assessment of overlap between model attention heatmaps and diagnostically relevant regions identified independently by expert neuropathologists.
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Up to Jul 2029
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Human versus AI versus AI-assisted diagnostic performance
Délai: Up to Jul 2029
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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.
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Up to Jul 2029
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Collaborateurs et enquêteurs
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Dates d'enregistrement des études
Dates principales de l'étude
Début de l'étude (Estimé)
Achèvement primaire (Estimé)
Achèvement de l'étude (Estimé)
Dates d'inscription aux études
Première soumission
Première soumission répondant aux critères de contrôle qualité
Première publication (Réel)
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière vérification
Plus d'information
Termes liés à cette étude
Mots clés
Termes MeSH pertinents supplémentaires
Autres numéros d'identification d'étude
- KY2023-977
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