Clinical trial NCT06822842: Accurate Diagnosis and Grading of Pediatric Solid Tumors Based on Pathological Large Models
Official title: Accurate Diagnosis and Grading of Pediatric Solid Tumors Based on Pathological Large Models
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Neuroblastoma
Medulloblastoma
Wilms Tumor
Hepatoblastoma
Rhabdomyosarcoma
Keywords: Machine learning
Diagnosis
Pathological classification
Lead sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Sponsor class: OTHER
First posted: 2025-02-12
Enrollment: 2000
Sex: ALL
Minimum age (years): 0
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The clinical data and HE-stained sections of pathological tissues of children with solid tumors (including neuroblastoma, Wilms tumor, hepatoblastoma and medulloblastoma, etc.) diagnosed and treated by histopathology in Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine from January 2012 to January 2022 were retrospectively analyzed.

Brief summary: 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.

Detailed description: Diagnosis test

Eligibility criteria: Inclusion Criteria:

1. 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).
2. Wilms tumor (nephroblastoma): patients aged 0-18 years old who have been diagnosed with Wilms tumor by histopathology.
3. Hepatoblastoma (HB): Patients aged 0-18 years who have been diagnosed with hepatoblastoma by histopathology.
4. Medulloblastoma (MB): Patients aged 0-18 years with a confirmed histopathological diagnosis of medulloblastoma.
5. rhabdomyosarcoma (RMS): patients aged 0-18 years old who have been diagnosed with medulloblastoma by histopathology.

Exclusion Criteria:

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

Primary outcomes:
measure: Diagnostic accuracy of patients
timeFrame: immediately after surgery
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
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