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NCT07685301Not yet recruiting

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

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

Source: ClinicalTrials.gov · Last updated 6 Jul 2026

Brief summary (registry, in English)

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.

Status (registry)
Not yet recruiting
Study type
Observational
Enrollment
20000 (estimated)
First submitted
29 Jun 2026
Start date
Aug 2026
Primary completion date
Jul 2027
Lead sponsor
Huashan Hospital
NCT number
NCT07685301

What you commit to

  • Study type: Observational
  • Planned participants: 20000
  • From Aug 2026 to the primary completion date, Jul 2027
  • No sites listed in no country
  • Visits and tests: ask the trial team, the registry does not list them

Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.

Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Other

  • 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.

Who the study is looking for

  • 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.

Age in the registry: 9 years to - · Sex: All · Does not accept healthy volunteers

Full criteria on ClinicalTrials.gov

This page is information, not medical advice. Discuss any trial with your doctor.