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NCT06822842
Accurate Diagnosis and Grading of Pediatric Solid Tumors Based on Pathological Large Models
trial in Neuroblastoma in 2,000 participants. Not yet recruiting.
30 April 2025
Quick facts
| Lead sponsor | Xinhua Hospital, Shanghai Jiao Tong University School of Medicine |
|---|---|
| Status | Not yet recruiting |
| Study type | OBSERVATIONAL |
| Enrollment | 2,000 |
| Start date | 1 February 2025 |
| Primary completion | 30 April 2025 |
| Estimated completion | 30 April 2025 |
Conditions studied
- Neuroblastoma — all drugs for Neuroblastoma →
- Medulloblastoma — all drugs for Medulloblastoma →
- Wilms Tumor — all drugs for Wilms Tumor →
- Hepatoblastoma — all drugs for Hepatoblastoma →
Sponsor
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Who can join
Adults 0 to 18, any sex, with Neuroblastoma or Medulloblastoma. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
Pediatric malignancies are the second leading cause of death in the pediatric population, with solid tumors accounting for approximately 60% of all pediatric malignancies. The pathological diagnosis of pediatric solid tumors is highly complex and specialized, because of its diverse tissue morphology, rare tumor subtypes and lack of labeling data, the traditional pathological diagnosis relies on the experience of senior pathologists, but in actual clinical practice, due to the lack of expert resources and inconsistent diagnostic standards, more efficient and accurate auxiliary diagnostic tools are urgently needed. In this study, we aim to construct a multimodal dataset by collecting high-quality pathological images and pathological diagnosis results of pediatric solid tumors (neuroblastoma, medulloblastoma, Wilms tumor, hepatoblastoma, rhabdomyosarcoma, etc.), and introduce medical knowledge enhancement strategies on this basis, and improve the medical reasoning ability and adaptability to fine-grained pathological tasks by injecting domain knowledge (such as molecular characteristics of tumors, pathological grading standards, diagnostic rules, etc.) into the model. Through the model, the representation space of images and texts is unified, and diversified diagnostic tasks of pediatric solid tumors such as tumor region segmentation, cancer detection, and tumor subtype identification are realized, providing intelligent support for the accurate diagnosis and personalized treatment of pediatric solid tumors.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06822842 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
- Last refreshed: 12 February 2025
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