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

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.

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Key information

Conditions

Neuroblastoma Congenital, Hereditary, and Neonatal Diseases and Abnormalities Disease Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Genetic Diseases, Inborn Glioma Hepatoblastoma Kidney Diseases Kidney Neoplasms Male Urogenital Diseases Medulloblastoma Myosarcoma Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Complex and Mixed Neoplasms, Connective and Soft Tissue Neoplasms, Germ Cell and Embryonal Neoplasms, Glandular and Epithelial Neoplasms, Muscle Tissue Neoplasms, Nerve Tissue Neoplasms, Neuroepithelial Neoplastic Syndromes, Hereditary Neuroectodermal Tumors Neuroectodermal Tumors, Primitive Neuroectodermal Tumors, Primitive, Peripheral Pathologic Processes Pathological Conditions, Signs and Symptoms Rhabdomyosarcoma Sarcoma Urogenital Diseases Urogenital Neoplasms Urologic Diseases Urologic Neoplasms Wilms Tumor

Age range

0 year–18 year

Sex eligibility

All sexes

Study type

Observational

About this study

Diagnosis test

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Neuroblastoma (NB): For newly diagnosed patients with NB aged 0-18 years, the diagnosis criteria are one of the following two items: (1) the patient's tumor tissue has obtained a positive pathological diagnosis under the light microscope; (2) Bone marrow biopsy or aspiration revealed characteristic neuroblastoma cells, which were small round cells, arranged in a nested or chrysanthemum clump or positive staining for anti-GD2 antibodies, and accompanied by an increase in urinary vanillylmandelic acid (VMA) and an increase in blood neuron-specific enolase (NSE).
Wilms tumor (nephroblastoma): patients aged 0-18 years old who have been diagnosed with Wilms tumor by histopathology.
Hepatoblastoma (HB): Patients aged 0-18 years who have been diagnosed with hepatoblastoma by histopathology.
Medulloblastoma (MB): Patients aged 0-18 years with a confirmed histopathological diagnosis of medulloblastoma.
rhabdomyosarcoma (RMS): patients aged 0-18 years old who have been diagnosed with medulloblastoma by histopathology.

Exclusion criteria

The patient's medical record and treatment follow-up information are incomplete; HE is not stained or faded
Those who have 2 or more types of tumors at the same time;
Those who do not meet the enrollment criteria.
Tumor subtype with less than 3 WSI images

Treatment and study plan

Primary outcomes

Diagnostic accuracy of patients

Time frame: immediately after surgery

For the diagnostic model, we use both micro and macro area under the curve (AUC) metrics to evaluate the model in terms of sensitivity, specificity, accuracy, positive predictive value and negative predictive value at different classification thresholds

Study contacts

Contact information is provided by the study sponsor or research team.

Kun Phd Sun

CONTACT

[email protected]

021-13601846338

Sponsors and collaborators

Lead sponsor

Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

Other

Registry information

Important dates

Study start: 2025
Primary completion: 2025
Study completion: 2025
First posted: Feb 12, 2025
Registry last updated: Feb 12, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed