NeuroblastomaNCT06822842
Accurate Diagnosis and Grading of Pediatric Solid Tumors Based on Pathological Large Models
An observational study of Neuroblastoma and Medulloblastoma, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 1 site in China.
Trial summary
Pediatric malignancies are the second leading cause of death in the pediatric population, with solid tumors accounting for approximately 60% of all pediatric malignancies. The pathological diagnosis of pediatric solid tumors is highly complex and specialized, because of its diverse tissue morphology, rare tumor… Read more about this study
- Who is it for?
- Ages 0 to 18 years
- Conditions the study is for
- Neuroblastoma, Medulloblastoma, Wilms Tumor
- How many people
- 1,229 participants
- Where
- 1 siteChina
- Study dates
- 22 Jan 2025to 26 Mar 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
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 0 to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
Study population: The clinical data and HE-stained sections of pathological tissues of children with solid tumors (including neuroblastoma, Wilms tumor, hepatoblastoma and medulloblastoma, etc.) diagnosed and treated by histopathology in Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine from January 2012 to January 2022 were retrospectively analyzed.
Who may be able to join
- Neuroblastoma (NB): For newly diagnosed patients with NB aged 0-18 years, the diagnosis criteria are one of the following two items: (1) the patient's tumor tissue has obtained a positive pathological diagnosis under the light microscope; (2) Bone marrow biopsy or aspiration revealed characteristic neuroblastoma cells, which were small round cells, arranged in a nested or chrysanthemum clump or positive staining for anti-GD2 antibodies, and accompanied by an increase in urinary vanillylmandelic acid (VMA) and an increase in blood neuron-specific enolase (NSE).
- Wilms tumor (nephroblastoma): patients aged 0-18 years old who have been diagnosed with Wilms tumor by histopathology.
- Hepatoblastoma (HB): Patients aged 0-18 years who have been diagnosed with hepatoblastoma by histopathology.
- Medulloblastoma (MB): Patients aged 0-18 years with a confirmed histopathological diagnosis of medulloblastoma.
- rhabdomyosarcoma (RMS): patients aged 0-18 years old who have been diagnosed with medulloblastoma by histopathology.
Who cannot join
- The patient's medical record and treatment follow-up information are incomplete; HE is not stained or faded
- Those who have 2 or more types of tumors at the same time;
- Those who do not meet the enrollment criteria.
- Tumor subtype with less than 3 WSI images
Trial locations and contacts
1 site in 1 country.
China1 site
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality, 200092
About this study
Pediatric malignancies are the second leading cause of death in the pediatric population, with solid tumors accounting for approximately 60% of all pediatric malignancies. The pathological diagnosis of pediatric solid tumors is highly complex and specialized, because of its diverse tissue morphology, rare tumor subtypes and lack of labeling data, the traditional pathological diagnosis relies on the experience of senior pathologists, but in actual clinical practice, due to the lack of expert resources and inconsistent diagnostic standards, more efficient and accurate auxiliary diagnostic tools are urgently needed. In this study, we aim to construct a multimodal dataset by collecting high-quality pathological images and pathological diagnosis results of pediatric solid tumors (neuroblastoma, medulloblastoma, Wilms tumor, hepatoblastoma, rhabdomyosarcoma, etc.), and introduce medical knowledge enhancement strategies on this basis, and improve the medical reasoning ability and adaptability to fine-grained pathological tasks by injecting domain knowledge (such as molecular characteristics of tumors, pathological grading standards, diagnostic rules, etc.) into the model. Through the model, the representation space of images and texts is unified, and diversified diagnostic tasks of pediatric solid tumors such as tumor region segmentation, cancer detection, and tumor subtype identification are realized, providing intelligent support for the accurate diagnosis and personalized treatment of pediatric solid tumors.
Read the detailed description
Diagnosis test
- Conditions
- Neuroblastoma, Medulloblastoma, Wilms Tumor, Hepatoblastoma, Rhabdomyosarcoma
- Keywords
- Machine learning, Diagnosis, Pathological classification
- Medical subject headings
- Neuroblastoma, Medulloblastoma, Wilms Tumor, Hepatoblastoma, Rhabdomyosarcoma, Disease
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of patients
For the diagnostic model, we 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
Measured: immediately after surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Feb 2025
- Study start22 Jan 2025
- Primary completion31 Dec 2025
- Study completion26 Mar 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for NeuroblastomaStudies 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
- Other
- Time perspective
- Retrospective
- Enrolment
- 1,229 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kun Sun, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06822842
- Sponsor's protocol ID
- XHEC-C-2025-015-1
- Status last verified
- Aug 2026
- First submitted
- 24 Jan 2025
- Last updated
- 4 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: IPD may not be shared or may be unavailable for sharing due to several reasons. Firstly, confidentiality is of utmost importance. IPD contains sensitive personal information, and sharing such data could potentially compromise the privacy of participants, thereby restricting the sharing of such detailed information. Secondly, the data is protected by intellectual property rights. This may limit its sharing without proper agreements or permissions. Thirdly, IPD is large in volume and complex in nature, requiring substantial resources for storage, transmission, and analysis. Detailed discussions and plans need to be formulated to ensure the integrity and security of the data during the sharing process.
Study at a glance
From the registry record, last updated by the sponsor on 4 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Accurate Diagnosis and Grading of Pediatric Solid Tumors Based on Pathological Large Models
- ClinicalTrials.gov ID
- NCT06822842
- Acronym / sponsor ID
- XHEC-C-2025-015-1
- Conditions
- Neuroblastoma, Medulloblastoma, Wilms Tumor, Hepatoblastoma, Rhabdomyosarcoma
- Keywords
- Machine learning, Diagnosis, Pathological classification
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,229 participants (actual)
- Lead sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of MedicineUniversity, hospital or other
- Study start
- 22 Jan 2025 (actual)
- Primary completion
- 31 Dec 2025 (actual)
- Study completion
- 26 Mar 2026 (actual)
- First posted
- 12 Feb 2025 (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.
Pediatric malignancies are the second leading cause of death in the pediatric population, with solid tumors accounting for approximately 60% of all pediatric malignancies. The pathological diagnosis of pediatric solid tumors is highly complex and specialized, because of its diverse tissue morphology, rare tumor subtypes and lack of labeling data, the traditional pathological diagnosis relies on the experience of senior pathologists, but in actual clinical practice, due to the lack of expert resources and inconsistent diagnostic standards, more efficient and accurate auxiliary diagnostic tools are urgently needed. In this study, we aim to construct a multimodal dataset by collecting high-quality pathological images and pathological diagnosis results of pediatric solid tumors (neuroblastoma, medulloblastoma, Wilms tumor, hepatoblastoma, rhabdomyosarcoma, etc.), and introduce medical knowledge enhancement strategies on this basis, and improve the medical reasoning ability and adaptability to fine-grained pathological tasks by injecting domain knowledge (such as molecular characteristics of tumors, pathological grading standards, diagnostic rules, etc.) into the model. Through the model, the representation space of images and texts is unified, and diversified diagnostic tasks of pediatric solid tumors such as tumor region segmentation, cancer detection, and tumor subtype identification are realized, providing intelligent support for the accurate diagnosis and personalized treatment of pediatric solid tumors.
Detailed description
Diagnosis test
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 0 to 18 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- Study population
- The clinical data and HE-stained sections of pathological tissues of children with solid tumors (including neuroblastoma, Wilms tumor, hepatoblastoma and medulloblastoma, etc.) diagnosed and treated by histopathology in Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine from January 2012 to January 2022 were retrospectively analyzed.
Key inclusion criteria (5 listed)
- Neuroblastoma (NB): For newly diagnosed patients with NB aged 0-18 years, the diagnosis criteria are one of the following two items: (1) the patient's tumor tissue has obtained a positive pathological diagnosis under the light microscope; (2) Bone marrow biopsy or aspiration revealed characteristic neuroblastoma cells, which were small round cells, arranged in a nested or chrysanthemum clump or positive staining for anti-GD2 antibodies, and accompanied by an increase in urinary vanillylmandelic acid (VMA) and an increase in blood neuron-specific enolase (NSE).
- Wilms tumor (nephroblastoma): patients aged 0-18 years old who have been diagnosed with Wilms tumor by histopathology.
- Hepatoblastoma (HB): Patients aged 0-18 years who have been diagnosed with hepatoblastoma by histopathology.
- Medulloblastoma (MB): Patients aged 0-18 years with a confirmed histopathological diagnosis of medulloblastoma.
- rhabdomyosarcoma (RMS): patients aged 0-18 years old who have been diagnosed with medulloblastoma by histopathology.
Key exclusion criteria (4 listed)
- The patient's medical record and treatment follow-up information are incomplete; HE is not stained or faded
- Those who have 2 or more types of tumors at the same time;
- Those who do not meet the enrollment criteria.
- Tumor subtype with less than 3 WSI images
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
Diagnostic accuracy of patients
Time frame: immediately after surgery
For the diagnostic model, we 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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Aug 2026.
- Individual participant dataSharing planned: Undecided. IPD may not be shared or may be unavailable for sharing due to several reasons. Firstly, confidentiality is of utmost importance. IPD contains sensitive personal information, and sharing such data could potentially compromise the privacy of participants, thereby restricting the sharing of such detailed information. Secondly, the data is protected by intellectual property rights. This may limit its sharing without proper agreements or permissions. Thirdly, IPD is large in volume and complex in nature, requiring substantial resources for storage, transmission, and analysis. Detailed discussions and plans need to be formulated to ensure the integrity and security of the data during the sharing process.
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Neuroblastoma and Medulloblastoma, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 1 site 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 5 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: finished, as of the sponsor's last update on 4 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Diagnostic accuracy of patients (time frame: immediately after surgery). 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 4 Aug 2026.