The Children's Hospital of Zhejiang University School of Medicine
Phase
Observational
Started
2026-01-01
Last updated
2026-04-24
Condition(s) studied
Neuroblastic Tumors
Study summary
The goal of this observational study is to build an intelligent ultrasound diagnostic system that integrates pathological typing, risk stratification and prognosis assessment. The main question it aims to answer is:
1. Can the prediction model of neuroblastoma tumors (NTs) in children based on ultrasound images distinguish each pathological subtype?
2. Can the multimodal fusion model established based on clinical and pathological features identify high-risk patients, predict bone marrow metastasis, and estimate the therapeutic effect?
3. Can this ultrasound diagnostic system achieve a systematic and intelligent assessment of NTs patients to assist in clinical risk stratification and individualized treatment decisions?
Eligibility
Sex
ALL
Min age
—
Max age
18 Years
Healthy volunteers
No
Inclusion Criteria:
1. The diagnosis of NTs was confirmed by surgical resection or biopsy with histopathological examination, and the type was classified as NB, GNB or GN according to the INPC standard.
2. Age ≤ 18 years old, with no gender restrictions.
3. There are complete abdominal (or primary site) ultrasound images archived, in original DICOM or JPG format, with image quality meeting the analysis requirements.
4. Complete clinical and pathological data relevant to the research purpose are available.
Exclusion Criteria:
1. The patient has previously undergone surgical resection treatment in another hospital, but the tumor recurred or remained after the operation.
2. Poor quality of ultrasound images: There are artifacts that seriously affect the identification of tumor contours or feature extraction, image blurring, or incomplete display of the lesion.
3. Severe data deficiency: Key clinical pathological data or imaging data are missing, making it impossible to extract and analyze the required information.
Primary outcome measure(s)
F1 score — Within one week after the model training is completed, calculations are conducted respectively on the internal validation set and the independent external validation set. F1 Score = 2 \* (Precision \* Recall) / (Precision + Recall)
accuracy rate — Within one week after the model training is completed, performance tests are conducted respectively on the internal validation set and the independent external validation set. Draw multi-class ROC curves and calculate based on the ROC curves.
specificity — Within one week after the model training is completed, calculations are conducted respectively on the internal validation set and the independent external validation set. specificity = (True negative cases / (True negative cases + False positive cases)) \* 100%
sensitivity — Within one week after the model training is completed, calculations are conducted respectively on the internal validation set and the independent external validation set. sensitivity= （True Positive / (True Positive + False Negative)）\*100%
Trial sites (6)
Facility
City
Region
Status
Anhui Provincial Children's Hospital
Hefei
Anhui
The Children's Hospital Affiliated to Soochow University
Suzhou
Jiangsu
Kunming Children's Hospital
Kunming
Yunnan
The Children's Hospital of Zhejiang University School of Medicine
Hangzhou
Zhejiang
Zhejiang Cancer Hospital
Hangzhou
Zhejiang
Wenling Institute of Medical Big Data and Artificial Intelligence
Wenling
Zhejiang
More The Children's Hospital of Zhejiang University School of Medicine trials in China
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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