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NCT06822842

Accurate Diagnosis and Grading of Pediatric Solid Tumors Based on Pathological Large Models

Not yet recruiting Last updated 12 February 2025
What this trial tests

trial in Neuroblastoma in 2,000 participants. Not yet recruiting.

Timeline
1 February 2025
Primary endpoint
30 April 2025
30 April 2025

Quick facts

Lead sponsorXinhua Hospital, Shanghai Jiao Tong University School of Medicine
StatusNot yet recruiting
Study typeOBSERVATIONAL
Enrollment2,000
Start date1 February 2025
Primary completion30 April 2025
Estimated completion30 April 2025

Conditions studied

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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Other recruiting trials for Neuroblastoma

Currently open trials in the same condition.

Other Xinhua Hospital, Shanghai Jiao Tong University School of Medicine trials

Trials by the same sponsor.

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