Neuroblastic TumorsNCT07549425
Construction and Validation of an Intelligent Ultrasound Diagnostic System for the Spectrum of Neuroblastoma in Children
An observational study of Neuroblastic Tumors, run by The Children's Hospital of Zhejiang University School of Medicine at 6 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Ages Up to 18 years
- Conditions the study is for
- Neuroblastic Tumors
- How many people
- 300 participants
- Where
- 6 sitesChina
- Study dates
- 1 Jan 2026to 30 Sep 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Up to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
6 sites in 1 country.
6 sites in 1 country
China6 sites
The Children's Hospital of Zhejiang University School of Medicine
Hangzhou, Zhejiang, 310000
Zhejiang Cancer Hospital
Hangzhou, Zhejiang, 310000
Anhui Provincial Children's Hospital
Hefei, Anhui, 230041
Kunming Children's Hospital
Kunming, Yunnan, 650100
The Children's Hospital Affiliated to Soochow University
Suzhou, Jiangsu, 215008
Wenling Institute of Medical Big Data and Artificial Intelligence
Wenling, Zhejiang, 317500
About this study
Official title: 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:
- Can the prediction model of neuroblastoma tumors (NTs) in children based on ultrasound images distinguish each pathological subtype?
- 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?
- Can this ultrasound diagnostic system achieve a systematic and intelligent assessment of NTs patients to assist in clinical risk stratification and individualized treatment decisions?
Read the 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:
- 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).
- 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.
- 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.
- Conditions
- Neuroblastic Tumors
- Keywords
- neuroblastic tumors, radiomics, ultrasound, children
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
F1 score
F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
Measured: 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.
Measured: 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%
Measured: 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%
Measured: Within one week after the model training is completed, calculations are conducted respectively on the internal validation set and the independent external validation set.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Apr 2026
- Study start1 Jan 2026
- Primary completion30 Apr 2026 (estimated)
- Study completion30 Sep 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Neuroblastic TumorsStudies of the same condition, open and under way.
- More trials run by The Children's Hospital of Zhejiang University School of MedicineThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, XuBin, MM, The Children's Hospital of Zhejiang University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07549425
- Sponsor's protocol ID
- 2026-IRB-0083-P-01
- Status last verified
- Jan 2026
- First submitted
- 1 Apr 2026
- Last updated
- 24 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Construction and Validation of an Intelligent Ultrasound Diagnostic System for the Spectrum of Neuroblastoma in Children: A Multicenter Study
- ClinicalTrials.gov ID
- NCT07549425
- Acronym / sponsor ID
- 2026-IRB-0083-P-01
- Conditions
- Neuroblastic Tumors
- Keywords
- neuroblastic tumors, radiomics, ultrasound, children
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- The Children's Hospital of Zhejiang University School of MedicineUniversity, hospital or other
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- 30 Apr 2026 (estimated)
- Study completion
- 30 Sep 2026 (estimated)
- First posted
- 24 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- Can the prediction model of neuroblastoma tumors (NTs) in children based on ultrasound images distinguish each pathological subtype?
- 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?
- Can this ultrasound diagnostic system achieve a systematic and intelligent assessment of NTs patients to assist in clinical risk stratification and individualized treatment decisions?
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:
- 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).
- 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.
- 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 design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- Up to 18 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
F1 score
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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%
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Apr 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
6 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Neuroblastic Tumors, run by The Children's Hospital of Zhejiang University School of Medicine at 6 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 24 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 6 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
F1 score (time frame: Within one week after the model training is completed, calculations are conducted respectively on the internal validation set and the independent external validation set.). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Apr 2026.