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

Construction and Validation of an Intelligent Ultrasound Diagnostic System for the Spectrum of Neuroblastoma in Children: A Multicenter Study

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?

Study Overview

• Status: Active, not recruiting
• Conditions: Neuroblastic Tumors

Detailed Description

Neuroblastic tumors (NTs) represent the most common extracranial solid tumors in childhood, with the vast majority of patients diagnosed with neuroblastoma (NB)-the subtype associated with the highest malignancy and poorest prognosis. These cases present significant challenges in clinical diagnosis and management, often leading to unfavorable overall outcomes. Histopathological examination remains the gold standard for definitive diagnosis and classification. However, this method is invasive, carries a risk of complications, and its diagnostic accuracy is subject to operator experience and biopsy sampling location. Although medical imaging allows for noninvasive tumor assessment, it primarily relies on subjective visual interpretation by physicians, resulting in limited accuracy and reproducibility in distinguishing between different NT subtypes.

Radiomics, an emerging artificial intelligence-based imaging analysis approach, enables high-throughput extraction, analysis, and quantification of imaging features through automated algorithms, uncovering vast amounts of subvisual information. It has demonstrated considerable promise in the differential diagnosis, treatment evaluation, and outcome prediction of tumors. Current radiomics research on neuroblastoma is still in its early stages, with most studies focusing on modalities such as computerized tomography(CT), magnetic resonance imaging(MRI), and Positron Emission Tomography-Computed Tomography(PET-CT), while ultrasound-based radiomics investigations remain unexplored.

Ultrasonography, owing to its unique advantages-including absence of ionizing radiation, real-time dynamic imaging, operational convenience, and low cost-has become the preferred imaging modality for pediatric tumor screening and follow-up. Consequently, integrating radiomics with ultrasonography to develop an intelligent diagnostic system capable of noninvasively and accurately assessing NTs holds significant clinical value and translational potential. Such a system would facilitate precise preoperative classification, patient risk stratification, and support for clinical decision-making.

This study aims to construct and validate an intelligent ultrasound diagnostic system for pediatric neuroblastic tumors based on ultrasound radiomics features, as follows:

1. To build a prediction model for pediatric neuroblastic tumors (NTs) based on ultrasound images, achieving automated differential diagnosis of neuroblastoma (NB), ganglioneuroblastoma (GNB), and ganglioneuroma (GN).
2. On the basis of pathological classification, integrate clinical pathological features to establish a multimodal fusion model. The focus is on identifying high-risk patients, predicting bone marrow metastasis, and estimating treatment outcomes, providing a reference basis for clinical decision-making.
3. Integrate previous research results to construct a comprehensive intelligent ultrasound diagnostic system that integrates pathological classification, risk stratification, and prognosis assessment, achieving systematic and intelligent evaluation of NTs patients to assist in clinical risk stratification and individualized treatment decisions.

• Study Type: Observational
• Enrollment (Estimated): 300

Contacts and Locations

Study Locations

• China
  • Anhui
    • Hefei, Anhui, China, 230041
      • Anhui Provincial Children's Hospital
  • Jiangsu
    • Suzhou, Jiangsu, China, 215008
      • The Children's Hospital Affiliated to Soochow University
  • Yunnan
    • Kunming, Yunnan, China, 650100
      • Kunming Children's Hospital
  • Zhejiang
    • Hangzhou, Zhejiang, China, 310000
      • Zhejiang Cancer Hospital
    • Hangzhou, Zhejiang, China, 310000
      • The Children's Hospital of Zhejiang University School of Medicine
    • Wenling, Zhejiang, China, 317500
      • Wenling Institute of Medical Big Data and Artificial Intelligence

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Patients diagnosed with NTs at the Children's Hospital of Zhejiang University School of Medicine, the Children's Hospital Affiliated to Soochow University, the Children's Hospital of Kunming City, and Anhui Provincial Children's Hospital from January 2015 to February 2025.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort
training set; The dataset from the Children's Hospital of Zhejiang University School of Medicine is planned to be randomly divided into a training set and an internal validation set in a ratio of 7:3.
internal validation set; The dataset from the Children's Hospital of Zhejiang University School of Medicine is planned to be randomly divided into a training set and an internal validation set in a ratio of 7:3.
independent external validation set; Data from the Children's Hospital Affiliated to Soochow University, Kunming Children's Hospital, and Anhui Provincial Children's Hospital were combined as an independent external validation set.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
F1 score	F1 Score = 2 * (Precision * Recall) / (Precision + Recall)	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	Draw multi-class ROC curves and calculate based on the ROC curves.	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	specificity = (True negative cases / (True negative cases + False positive cases)) * 100%	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	sensitivity= （True Positive / (True Positive + False Negative)）*100%	Within one week after the model training is completed, calculations are conducted respectively on the internal validation set and the independent external validation set.

Collaborators and Investigators

• Sponsor: The Children's Hospital of Zhejiang University School of Medicine

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2026
• Primary Completion (Estimated): April 30, 2026
• Study Completion (Estimated): September 30, 2026

Study Registration Dates

• First Submitted: April 1, 2026
• First Submitted That Met QC Criteria: April 21, 2026
• First Posted (Actual): April 24, 2026

Study Record Updates

• Last Update Posted (Actual): April 24, 2026
• Last Update Submitted That Met QC Criteria: April 21, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Keywords: ultrasound; children; radiomics; neuroblastic tumors
• Other Study ID Numbers: 2026-IRB-0083-P-01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No