Neuroblastoma (NB)NBNCT06703944
Risk Model for Metastasis Detection of Neuroblastoma
An observational study of Neuroblastoma (NB) and Prognosis, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 3 sites in China.
Trial summary
Neuroblastoma (NB) is the most common extracranial solid tumor in children, accounting for about 15% of tumor-related mortality. NB patients in high-risk group are prone to bone marrow and/or bone metastases with low five-year overall survival rate. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Neuroblastoma (NB), Prognosis, Bone Marrow Metastasis
- What is being tested?
- risk model in diagnosis and prognosis
- How many people
- 500 participants
- Where
- 3 sitesChina
- Study dates
- 1 Dec 2024to 1 Dec 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: This study included participants who were newly diagnosed with NB between January 2019 and June 2024. The follow-up date ended on June 30, 2024. The internal cohort including participants from Xinhua Hospital, Shanghai Jiao Tong University School of Medicine. The independent external cohorts including participants form Children's Hospital, Zhejiang University School of Medicine and Shenzhen Children's Hospital. All the participants have performed bone marrow smear analysis as routine examination. The bone marrow smear stained with Wright-Giemsa was made according to standard protocols.The diagnosis completed by experienced pathologist and correlated with clinical and/or radiological findings.
Who may be able to join
- The participant newly diagnosed with NB according to the International Neuroblastoma Risk Group Staging System (INRGSS). The diagnosis completed by experienced pathologist and correlated with clinical and/or radiological findings.
- The participant diagnosed with NB at other hospitals who have not received chemotherapy or radiotherapy.
- The participant with NB has performed bone marrow smear analysis as routine examination. The bone marrow smear stained with Wright-Giemsa was made according to standard protocols.
Who cannot join
- The participant with concurrent diagnosis of other malignancies.
- The participant with NB who has previously received chemotherapy and/or radiotherapy.
- The participant with incomplete clinical data, the metastasis state of bone marrow and/or bone is unclear.
- The participant was excluded due to non-representative specimens, such as unclear or faded Wright-Giemsa staining of bone marrow smear.
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China3 sites
The Children's Hospital, Zhejiang University School of Medicine
Hangzhou, Zhejiang
Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine
Shanghai, Shanghai Municipality, 200092
Shenzhen Children's Hospital
Shenzhen, 518038
About this study
Official title: Bone Marrow Cytology-based Artificial Intelligence Model for Detection and Prognosis of Neuroblastoma
Neuroblastoma (NB) is the most common extracranial solid tumor in children, accounting for about 15% of tumor-related mortality. NB patients in high-risk group are prone to bone marrow and/or bone metastases with low five-year overall survival rate. The artificial intelligence (AI) and deep learning technologies have potential to identifying morphological characteristics of bone marrow cytology in clinical practice. In this study, the investigators construct and evaluate the bone marrow cytology-based AI model for detection and prognosis of NB. The main questions of the study as follows:
The question 1: Dose bone marrow cytology-based AI model work for prediction of bone marrow metastasis in NB? The question 2: Dose bone marrow cytology-based AI model work for prediction of bone metastasis in NB? The question 3: Dose bone marrow cytology-based AI model have potential to assist doctors in making individualized predictions of survival outcome? The investigators will retrospectively obtain the participants with NB between January 2019 and June 2024. The follow-up date ended on June 30, 2024.
Read more
The internal cohort including participants from Xinhua Hospital, Shanghai Jiao Tong University School of Medicine. The independent external cohorts including participants form Children's Hospital, Zhejiang University School of Medicine and Shenzhen Children's Hospital.
The investigators collect the clinical data of enrolled participants at the time of the patients' initial admission to the hospital, prior to receiving treatment. The clinical information including age, gender, primary tumor location, tumor grade, bone marrow metastasis state, bone metastasis state, genetic aberrations (MYCN amplification, Chromosome 1p deletion, Chromosome 11q deletion) and lab variables (peripheral blood cell count, bone marrow cytology indicators, the serum concentration of lactate dehydrogenase, neuron specific enolase).
This study is a non-interventional observational study, there is no risk to the participants and investigators. Participants get these benefits:
- Early Detection: The model helps in early risk identification and personalize treatment.
- Convenience: Because the model relies on general lab tests, it is easy to carry out can reduce invasive diagnostic procedures.
- Cost-Effective: Using existing clinical data from routine tests can make the prediction process more cost-effective.
- Data-Driven Decisions: The AI model improve diagnostic efficiency and support the medical decision.
Read the detailed description
- Dataset processing. In this study, the model including training set, validation set and test sets. During the model construction, the investigators process the training in internal cohort by randomly assign the enrolled participants to the training set and validation set in a 8:2 ratio. The investigators use the enrolled participants of external cohorts as test sets to evaluate the model performance independently, and then select the best model for future use. The Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine apaproved this study (XHEC-C-2024-023-2).
- image acquisition. In the AI model, the investigators use bone marrow smears of enrolled participants for cytological evaluation and image collection. During cell classification and metastasis detection, the experienced pathologists complete the bone marrow smear analysis, metastatic NB clusters in bone marrow usually exhibited aggregated round atypical cells with high nucleus/cytoplasmic ratio. The investigators scan stained bone marrow smear at 40 × magnification for digital whole slide imaging (WSI). The investigators segment WSIs into smaller patches as 512 × 512 pixels tiles and apply the Vahadane method to normalize the color of small tiles.
- Deep learning training. In the feature extraction of bone marrow cytological image, the process including two tiers of predictions: patch-level and WSI-level predictions. For patch-level predictions, the investigators carry out label predictions and their respective probabilities for all patches. The investigators apply model in the deep learning process as follows: recognized neural network (CNN)-resnet50 and Vision Transformer (ViT). The parameter configurations in the model as follows: optimizer-SGD, loss function-softmax cross-entropy, with a batch size of 64. For WSI-level predictions, the investigators use a multi-instance learning (MIL) algorithm to aggregate dispersed patch-level features to WSI-level features. During MIL for WSI fusion, the investigators perform WSI-level predictions with Patch Likelihood Histogram (PLH) pipeline and Bag of Words (BoW) pipeline in combination. Subsequently, the investigators get the WSI-level prediction as final representations of participant for subsequent analytical operations.
- Signature building. In the procession of feature selection, the investigators use LASSO (Least Absolute Shrinkage and Selection Operator) feature screening to determine the final WSI-level features of the bone marrow cytology. These selected features were then subjected to machine learning methods to develop AI model. the investigators apply several machine learning algorithms to predict metastasis state of bone marrow and/or bone in participants, such as support vector machines (SVM), Logistic regression (LR), tree-based models, such as random forests and extremely randomized trees (ExtraTrees), extreme Gradient Boosting (XGBoost), and light gradient boosting machine (LightGBM), as well as multilayer perceptron (MLP) to develop our models. In the prognosis model, the investigators e use Cox models to construct the survival model with bone marrow cytological signature and clinical characters.
- Model evaluation and statistical analysis. The investigators compare the clinical characteristics of participant with independent sample t-test for continuous variables and the χ² test for discrete variables in SPSS version 22.0 (SPSS, Inc., Chicago, IL, USA). P ≤ 0.05 was considered statistically significant.
For the diagnostic model, the investigators use both micro and macro area under the curve (AUC) metrics to evaluate the model in terms of sensitivity, specificity, accuracy, positive predictive value and negative predictive value at different classification thresholds. The model's performance and effectiveness were evaluated on separate test cohort. The study employs custom Python code written in Python v.3.7.12 to evaluate the model performance.
For the prognostic model, the investigators use AUC as the performance metric and calculating sensitivity and specificity. The model with the best performance on the test set was selected as the optimal model. Survival curves were constructed according to the Kaplan-Meier method.
- Conditions
- Neuroblastoma (NB), Prognosis, Bone Marrow Metastasis, Bone Metastases
- Being tested
- risk model in diagnosis and prognosis
- Keywords
- Neuroblastoma, Bone Marrow Metastasis, Bone Metastasis, Machine Learning Algorithms, Risk Prediction Models, artificial intelligence mode, Prognosis
- Medical subject headings
- Neuroblastoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
risk model in diagnosis and prognosisOther
In this study, we construct and evaluate the bone marrow cytology-based AI model for detection and prognosis of NB.
- For the diagnostic model, we use AUC metrics to evaluate the model in terms of sensitivity, specificity, accuracy, positive predictive value and negative predictive value at different classification thresholds.
- For the prognostic model, we use AUC as the performance metric and calculating sensitivity and specificity. Survival curves were constructed according to the Kaplan-Meier method.
| Study group | Role | What they receive |
|---|---|---|
| Neuroblastoma With Bone Marrow Metastasis Group | For the diagnosis of neuroblastoma with bone marrow metastasis, the medical practices including as follows: bone marrow biopsy, bone marrow cytology of aspiration smear, flow cytometry and positron emission tomography-computed tomography(PET-CT). Bone marrow biopsy or smear analysis may reveal characteristic NB cells. Flow cytometry may detect NB cells with phenotype of cluster of differentiation antigen 45(CD45)-/cluster of differentiation antigen 56(CD56)+/cluster of differentiation antigen 81(CD81)+/GD2 ganglioside (GD2)+. PET/CT imaging reveal the metastatic NB cells in term of metabolic activity and spatial distribution of metastatic involvement. A positive result from any of these methods is sufficient for diagnosed as NB with bone marrow metastasis. Receives: Other: risk model in diagnosis and prognosis | |
| Neuroblastoma Without Bone Marrow Metastasis Group | For the diagnosis of bone marrow metastasis in the enrolled participants, if there is no positive result from any of these tests as follows: bone marrow biopsy, bone marrow cytology of smear, flow cytometry or PET/CT, the participant is classified into the Neuroblastoma Without Bone Marrow Metastasis Group. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Neuroblastoma with bone marrow metastasis
the medical practices in diagnosis of bone marrow metastasis including as follows: bone marrow biopsy, bone marrow cytology of aspiration smear, flow cytometry and PET/CT. Bone marrow biopsy or smear analysis may reveal characteristic NB cells. Flow cytometry may detect NB cells with phenotype of CD45-/CD56+/CD81+/GD2+. PET/CT imaging reveal the metastatic NB cells in term of metabolic activity and spatial distribution of metastatic involvement. A positive result from any of these methods is sufficient for diagnosed as NB with bone marrow metastasis.
Measured: The period from the initial diagnosis of neuroblastoma to the initiation of chemotherapy or radiotherapy, up to 3 months.
Neuroblastoma with bone metastasis
We diagnosed NB with bone metastasis if bone destruction or discontinuity of the bone cortex in radiology test including CT, PET/CT, MRI.
Measured: The period from the initial diagnosis of neuroblastoma to the initiation of chemotherapy or radiotherapy, up to 3 months.
Secondary outcomes (1)
Overall survival time
Overall survival (OS) was defined as time from diagnosis of neuroblastoma to death from any cause.
Measured: through study completion, up to 60 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Nov 2024
- Study start1 Dec 2024
- Primary completion1 Jul 2025 (estimated)
- Study completion1 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Neuroblastoma (NB)Studies of the same condition, open and under way.
- More trials run by Xinhua Hospital, Shanghai Jiao Tong 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Shenzhen Children's Hospital, The Children's Hospital of Zhejiang University School of Medicine
- Responsible party
- Principal investigator, Juan Ma, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
- Investigators
- juan ma, Doctor, Principal investigator, Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06703944
- Sponsor's protocol ID
- XH-24-008
- Acronym
- NB
- Status last verified
- Dec 2024
- First submitted
- 19 Nov 2024
- Last updated
- 15 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The investigators plan to share individual participant data (IPD) with other researchers to promote transparency and facilitate further research in the field. The shared data will include clinical outcomes, laboratory results, and demographic information.
Publications
Ma J, Yao Q, Fu X, Xia Z, Yue Y, Xu D, Yuan X, Zhao L, Wang J, Dong A, Gao L, Yang J, Shen L, Zheng Y, Ni S. Interpretative diagnostic model for neuroblastoma metastases using bone marrow cytology. J Transl Med. 2026 Apr 7;24(1):691. doi: 10.1186/s12967-026-08069-2.
PubMed 41947227
Study at a glance
From the registry record, last updated by the sponsor on 15 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Bone Marrow Cytology-based Artificial Intelligence Model for Detection and Prognosis of Neuroblastoma
- ClinicalTrials.gov ID
- NCT06703944
- Acronym / sponsor ID
- NB / XH-24-008
- Conditions
- Neuroblastoma (NB), Prognosis, Bone Marrow Metastasis, Bone Metastases
- Keywords
- Neuroblastoma, Bone Marrow Metastasis, Bone Metastasis, Machine Learning Algorithms, Risk Prediction Models, artificial intelligence mode, Prognosis
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of MedicineUniversity, hospital or other
- Collaborators
- Shenzhen Children's Hospital, The Children's Hospital of Zhejiang University School of Medicine
- Study start
- 1 Dec 2024 (actual)
- Primary completion
- 1 Jul 2025 (estimated)
- Study completion
- 1 Dec 2026 (estimated)
- First posted
- 25 Nov 2024 (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.
Neuroblastoma (NB) is the most common extracranial solid tumor in children, accounting for about 15% of tumor-related mortality. NB patients in high-risk group are prone to bone marrow and/or bone metastases with low five-year overall survival rate. The artificial intelligence (AI) and deep learning technologies have potential to identifying morphological characteristics of bone marrow cytology in clinical practice. In this study, the investigators construct and evaluate the bone marrow cytology-based AI model for detection and prognosis of NB. The main questions of the study as follows:
The question 1: Dose bone marrow cytology-based AI model work for prediction of bone marrow metastasis in NB? The question 2: Dose bone marrow cytology-based AI model work for prediction of bone metastasis in NB? The question 3: Dose bone marrow cytology-based AI model have potential to assist doctors in making individualized predictions of survival outcome? The investigators will retrospectively obtain the participants with NB between January 2019 and June 2024. The follow-up date ended on June 30, 2024.
The internal cohort including participants from Xinhua Hospital, Shanghai Jiao Tong University School of Medicine. The independent external cohorts including participants form Children's Hospital, Zhejiang University School of Medicine and Shenzhen Children's Hospital.
The investigators collect the clinical data of enrolled participants at the time of the patients' initial admission to the hospital, prior to receiving treatment. The clinical information including age, gender, primary tumor location, tumor grade, bone marrow metastasis state, bone metastasis state, genetic aberrations (MYCN amplification, Chromosome 1p deletion, Chromosome 11q deletion) and lab variables (peripheral blood cell count, bone marrow cytology indicators, the serum concentration of lactate dehydrogenase, neuron specific enolase).
This study is a non-interventional observational study, there is no risk to the participants and investigators. Participants get these benefits:
- Early Detection: The model helps in early risk identification and personalize treatment.
- Convenience: Because the model relies on general lab tests, it is easy to carry out can reduce invasive diagnostic procedures.
- Cost-Effective: Using existing clinical data from routine tests can make the prediction process more cost-effective.
- Data-Driven Decisions: The AI model improve diagnostic efficiency and support the medical decision.
Detailed description
- Dataset processing. In this study, the model including training set, validation set and test sets. During the model construction, the investigators process the training in internal cohort by randomly assign the enrolled participants to the training set and validation set in a 8:2 ratio. The investigators use the enrolled participants of external cohorts as test sets to evaluate the model performance independently, and then select the best model for future use. The Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine apaproved this study (XHEC-C-2024-023-2).
- image acquisition. In the AI model, the investigators use bone marrow smears of enrolled participants for cytological evaluation and image collection. During cell classification and metastasis detection, the experienced pathologists complete the bone marrow smear analysis, metastatic NB clusters in bone marrow usually exhibited aggregated round atypical cells with high nucleus/cytoplasmic ratio. The investigators scan stained bone marrow smear at 40 × magnification for digital whole slide imaging (WSI). The investigators segment WSIs into smaller patches as 512 × 512 pixels tiles and apply the Vahadane method to normalize the color of small tiles.
- Deep learning training. In the feature extraction of bone marrow cytological image, the process including two tiers of predictions: patch-level and WSI-level predictions. For patch-level predictions, the investigators carry out label predictions and their respective probabilities for all patches. The investigators apply model in the deep learning process as follows: recognized neural network (CNN)-resnet50 and Vision Transformer (ViT). The parameter configurations in the model as follows: optimizer-SGD, loss function-softmax cross-entropy, with a batch size of 64. For WSI-level predictions, the investigators use a multi-instance learning (MIL) algorithm to aggregate dispersed patch-level features to WSI-level features. During MIL for WSI fusion, the investigators perform WSI-level predictions with Patch Likelihood Histogram (PLH) pipeline and Bag of Words (BoW) pipeline in combination. Subsequently, the investigators get the WSI-level prediction as final representations of participant for subsequent analytical operations.
- Signature building. In the procession of feature selection, the investigators use LASSO (Least Absolute Shrinkage and Selection Operator) feature screening to determine the final WSI-level features of the bone marrow cytology. These selected features were then subjected to machine learning methods to develop AI model. the investigators apply several machine learning algorithms to predict metastasis state of bone marrow and/or bone in participants, such as support vector machines (SVM), Logistic regression (LR), tree-based models, such as random forests and extremely randomized trees (ExtraTrees), extreme Gradient Boosting (XGBoost), and light gradient boosting machine (LightGBM), as well as multilayer perceptron (MLP) to develop our models. In the prognosis model, the investigators e use Cox models to construct the survival model with bone marrow cytological signature and clinical characters.
- Model evaluation and statistical analysis. The investigators compare the clinical characteristics of participant with independent sample t-test for continuous variables and the χ² test for discrete variables in SPSS version 22.0 (SPSS, Inc., Chicago, IL, USA). P ≤ 0.05 was considered statistically significant.
For the diagnostic model, the investigators use both micro and macro area under the curve (AUC) metrics to evaluate the model in terms of sensitivity, specificity, accuracy, positive predictive value and negative predictive value at different classification thresholds. The model's performance and effectiveness were evaluated on separate test cohort. The study employs custom Python code written in Python v.3.7.12 to evaluate the model performance.
For the prognostic model, the investigators use AUC as the performance metric and calculating sensitivity and specificity. The model with the best performance on the test set was selected as the optimal model. Survival curves were constructed according to the Kaplan-Meier method.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Neuroblastoma With Bone Marrow Metastasis Group | – | Other: risk model in diagnosis and prognosisFor the diagnosis of neuroblastoma with bone marrow metastasis, the medical practices including as follows: bone marrow biopsy, bone marrow cytology of aspiration smear, flow cytometry and positron emission tomography-computed tomography(PET-CT). Bone marrow biopsy or smear analysis may reveal characteristic NB cells. Flow cytometry may detect NB cells with phenotype of cluster of differentiation antigen 45(CD45)-/cluster of differentiation antigen 56(CD56)+/cluster of differentiation antigen 81(CD81)+/GD2 ganglioside (GD2)+. PET/CT imaging reveal the metastatic NB cells in term of metabolic activity and spatial distribution of metastatic involvement. A positive result from any of these methods is sufficient for diagnosed as NB with bone marrow metastasis. |
| Neuroblastoma Without Bone Marrow Metastasis Group | – | –For the diagnosis of bone marrow metastasis in the enrolled participants, if there is no positive result from any of these tests as follows: bone marrow biopsy, bone marrow cytology of smear, flow cytometry or PET/CT, the participant is classified into the Neuroblastoma Without Bone Marrow Metastasis Group. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- This study included participants who were newly diagnosed with NB between January 2019 and June 2024. The follow-up date ended on June 30, 2024. The internal cohort including participants from Xinhua Hospital, Shanghai Jiao Tong University School of Medicine. The independent external cohorts including participants form Children's Hospital, Zhejiang University School of Medicine and Shenzhen Children's Hospital. All the participants have performed bone marrow smear analysis as routine examination. The bone marrow smear stained with Wright-Giemsa was made according to standard protocols.The diagnosis completed by experienced pathologist and correlated with clinical and/or radiological findings.
Key inclusion criteria (3 listed)
- The participant newly diagnosed with NB according to the International Neuroblastoma Risk Group Staging System (INRGSS). The diagnosis completed by experienced pathologist and correlated with clinical and/or radiological findings.
- The participant diagnosed with NB at other hospitals who have not received chemotherapy or radiotherapy.
- The participant with NB has performed bone marrow smear analysis as routine examination. The bone marrow smear stained with Wright-Giemsa was made according to standard protocols.
Key exclusion criteria (4 listed)
- The participant with concurrent diagnosis of other malignancies.
- The participant with NB who has previously received chemotherapy and/or radiotherapy.
- The participant with incomplete clinical data, the metastasis state of bone marrow and/or bone is unclear.
- The participant was excluded due to non-representative specimens, such as unclear or faded Wright-Giemsa staining of bone marrow smear.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| risk model in diagnosis and prognosisIn this study, we construct and evaluate the bone marrow cytology-based AI model for detection and prognosis of NB. 1. For the diagnostic model, we use AUC metrics to evaluate the model in terms of sensitivity, specificity, accuracy, positive predictive value and negative predictive value at different classification thresholds. 2. For the prognostic model, we use AUC as the performance metric and calculating sensitivity and specificity. Survival curves were constructed according to the Kaplan-Meier method. | Other | Neuroblastoma With Bone Marrow Metastasis Group |
Primary outcome
Neuroblastoma with bone marrow metastasis
Time frame: The period from the initial diagnosis of neuroblastoma to the initiation of chemotherapy or radiotherapy, up to 3 months.
the medical practices in diagnosis of bone marrow metastasis including as follows: bone marrow biopsy, bone marrow cytology of aspiration smear, flow cytometry and PET/CT. Bone marrow biopsy or smear analysis may reveal characteristic NB cells. Flow cytometry may detect NB cells with phenotype of CD45-/CD56+/CD81+/GD2+. PET/CT imaging reveal the metastatic NB cells in term of metabolic activity and spatial distribution of metastatic involvement. A positive result from any of these methods is sufficient for diagnosed as NB with bone marrow metastasis.
Neuroblastoma with bone metastasis
Time frame: The period from the initial diagnosis of neuroblastoma to the initiation of chemotherapy or radiotherapy, up to 3 months.
We diagnosed NB with bone metastasis if bone destruction or discontinuity of the bone cortex in radiology test including CT, PET/CT, MRI.
1 secondary outcome
Overall survival time
Time frame: through study completion, up to 60 months.
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 15 May 2025.
- Derived from this study: PubMed 41947227Ma J, Yao Q, Fu X, Xia Z, Yue Y, Xu D, Yuan X, Zhao L, Wang J, Dong A, Gao L, Yang J, Shen L, Zheng Y, Ni S. Interpretative diagnostic model for neuroblastoma metastases using bone marrow cytology. J Transl Med. 2026 Apr 7;24(1):691. doi: 10.1186/s12967-026-08069-2.
- Individual participant dataSharing planned: Yes. The investigators plan to share individual participant data (IPD) with other researchers to promote transparency and facilitate further research in the field. The shared data will include clinical outcomes, laboratory results, and demographic information.
Related trials for Neuroblastoma (NB)
RecruitingPhase 2
A Study of a Vaccine in Combination With Beta-glucan in People With Neuroblastoma
Neuroblastoma, High-risk Neuroblastoma
RecruitingPhase 2
Phase II Study of Chidamide-Dinutuximab Beta-Irinotecan-Temozolomide for Refractory/Relapsed Neuroblastoma in Children
Neuroblastoma (NB), Neuroblastoma in Children
RecruitingEarly Phase 1
Safety, Tolerability, and Preliminary Efficacy of [211At]MABG in Patients With Relapsed or Refractory Neuroblastoma
Neuroblastoma (NB)
Active, not recruitingEarly Phase 1
A Study of N9 Chemotherapy in Children With Neuroblastoma
Neuroblastoma, Pediatric Cancer
Active, not recruitingEarly Phase 1
Study of Chemoimmunotherapy for High-Risk Neuroblastoma
Neuroblastoma (NB)
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.
juan ma, Doctor principal investigator
Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Neuroblastoma (NB) and Prognosis, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 3 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 3 inclusion and 4 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: enrolling by invitation, as of the sponsor's last update on 15 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 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?
Neuroblastoma with bone marrow metastasis (time frame: The period from the initial diagnosis of neuroblastoma to the initiation of chemotherapy or radiotherapy, up to 3 months.). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06703944, which also lists 1 linked publication.
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 15 May 2025.