Active — Not RecruitingNot Applicable

Construction and Validation of an Intelligent Ultrasound Diagnostic System for the Spectrum of Neuroblastoma in Children

China300 participantsStarted 2026-01-01

Plain-language 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?

Who can participate

Age range18 Years

SexALL

See this in plain English?

AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

Inclusion criteria

✓. 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.
✓. Age ≤ 18 years old, with no gender restrictions.
✓. There are complete abdominal (or primary site) ultrasound images archived, in original DICOM or JPG format, with image quality meeting the analysis requirements.
✓. Complete clinical and pathological data relevant to the research purpose are available.

Exclusion criteria

✕. The patient has previously undergone surgical resection treatment in another hospital, but the tumor recurred or remained after the operation.
✕. 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.
✕. Severe data deficiency: Key clinical pathological data or imaging data are missing, making it impossible to extract and analyze the required information.

What they're measuring

F1 score

Timeframe: Within one week after the model training is completed, calculations are conducted respectively on the internal validation set and the independent external validation set.

accuracy rate

Timeframe: 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.

specificity

Timeframe: 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

Timeframe: Within one week after the model training is completed, calculations are conducted respectively on the internal validation set and the independent external validation set.

Trial details

NCT IDNCT07549425

SponsorThe Children's Hospital of Zhejiang University School of Medicine

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2026-04-30

View on ClinicalTrials.gov More Neuroblastic Tumors trials

Plain-language summary

Who can participate

Age range18 Years

SexALL

See this in plain English?

AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

Inclusion criteria

✓. 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.
✓. Age ≤ 18 years old, with no gender restrictions.
✓. There are complete abdominal (or primary site) ultrasound images archived, in original DICOM or JPG format, with image quality meeting the analysis requirements.
✓. Complete clinical and pathological data relevant to the research purpose are available.

Exclusion criteria

✕. The patient has previously undergone surgical resection treatment in another hospital, but the tumor recurred or remained after the operation.
✕. 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.
✕. Severe data deficiency: Key clinical pathological data or imaging data are missing, making it impossible to extract and analyze the required information.

What they're measuring

F1 score

Timeframe: Within one week after the model training is completed, calculations are conducted respectively on the internal validation set and the independent external validation set.

accuracy rate

Timeframe: 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.

specificity

Timeframe: 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

Timeframe: Within one week after the model training is completed, calculations are conducted respectively on the internal validation set and the independent external validation set.