GliomaNCT06199388
Development and Validation of a Deep Learning-Based Survival Prediction Model for Pediatric Glioma Patients: A Retrospective Study Using the SEER Database and Chinese Data
An observational study of Glioma, run by Tang-Du Hospital at 1 site in China.
Trial summary
Accurately predicting the survival of pediatric glioma patients is crucial for informed clinical decision-making and selecting appropriate treatment strategies. However, there is a lack of prognostic models specifically tailored for pediatric glioma patients. Read more about this study
- Who is it for?
- Ages Up to 21 years
- Conditions the study is for
- Glioma
- What is being tested?
- Survival state
- How many people
- 9,532 participants
- Where
- 1 siteChina
- Study dates
- 20 Sep 2022to 20 Dec 2023
- 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 Up to 21 years
- All sexes
- Child, Adult
Study population: the US Surveillance, Epidemiology, and End Results (SEER) between January 2000 and December 2018 and a Chinese registry (The Tangdu Hospital of the Fourth Military Medical Universitye) between January 2010 and December 2018
Who may be able to join
- To identify specific tumor types, the International Classification of Diseases for Oncology, 3rd Edition codes (ICD-O-3) were used, including codes 9450, 9394, 9421, 9384, 9383, 9424, 9400, 9420, 9410, 9411, 9380, 9382, 9391, 9393, 9390, 9401, 9381, 9451, 9440, 9441, 9442, 9430, and 9380, covering astrocytic tumors, oligodendroglia tumors, oligoastrocytic tumors, ependymal tumors, and other gliomas. Inclusion criteria comprised all primary brain tumors (C71.0-C71.9, C72.3, C72.8, C75.3) diagnosed, among patients under 21 years old, and meeting the third edition of the ICD-O-3 classification.
Who cannot join
- Only patients with available survival time were included, and those with unknown or missing clinical features were excluded.
Trial locations and contacts
1 site in 1 country.
About this study
Accurately predicting the survival of pediatric glioma patients is crucial for informed clinical decision-making and selecting appropriate treatment strategies. However, there is a lack of prognostic models specifically tailored for pediatric glioma patients. This study aimed to address this gap by developing a time-dependent deep learning model to aid physicians in making more accurate prognostic assessments and treatment decisions.
Read the detailed description
This retrospective study focuses on survival prediction in pediatric glioma patients using a population-based approach. The model was trained using the Surveillance, Epidemiology, and End Results (SEER) Registry database. To identify specific tumor types, the International Classification of Diseases for Oncology, 3rd Edition codes (ICD-O-3) were used, including codes 9450, 9394, 9421, 9384, 9383, 9424, 9400, 9420, 9410, 9411, 9380, 9382, 9391, 9393, 9390, 9401, 9381, 9451, 9440, 9441, 9442, 9430, and 9380, covering astrocytic tumors, oligodendroglia tumors, oligoastrocytic tumors, ependymal tumors, and other gliomas. Inclusion criteria comprised all primary brain tumors (C71.0-C71.9, C72.3, C72.8, C75.3) diagnosed between 2000 and 2018, among patients under 21 years old, and meeting the third edition of the ICD-O-3 classification. Only patients with available survival time were included, and those with unknown or missing clinical features were excluded. This cohort consisted of 258 pediatric glioma patients diagnosed at Tangdu Hospital in Xi\'an, China, between January 2010 and December 2018. These patients had complete clinical data and comprehensive follow-up records.
- Conditions
- Glioma
- Being tested
- Survival state
- Medical subject headings
- Glioma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Survival stateOther
We recorded clinically relevant information and survival status of pediatric glioma patients
| Study group | Role | What they receive |
|---|---|---|
| SEER database | The model was trained using the Surveillance, Epidemiology, and End Results (SEER) Registry database. To identify specific tumor types, the International Classification of Diseases for Oncology, 3rd Edition codes (ICD-O-3) were used, including codes 9450, 9394, 9421, 9384, 9383, 9424, 9400, 9420, 9410, 9411, 9380, 9382, 9391, 9393, 9390, 9401, 9381, 9451, 9440, 9441, 9442, 9430, and 9380, covering astrocytic tumors, oligodendroglia tumors, oligoastrocytic tumors, ependymal tumors, and other gliomas. Inclusion criteria comprised all primary brain tumors (C71.0-C71.9, C72.3, C72.8, C75.3) diagnosed between 2000 and 2018, among patients under 21 years old, and meeting the third edition of the ICD-O-3 classification. Only patients with available survival time were included, and those with unknown or missing clinical features were excluded. Receives: Other: Survival state | |
| Chinese cohort | To assess the generalizability of the final model, an external validation cohort from China was used. This cohort consisted of 258 pediatric glioma patients diagnosed at Tangdu Hospital in Xi\'an, China, between January 2010 and December 2018. These patients had complete clinical data and comprehensive follow-up records. Receives: Other: Survival state |
What is being measured
The primary outcome is the main question the study is designed to answer.
overall survival
The primary outcome was overall survival (OS), which was defined as the time interval from the pediatric glioma diagnosis until death or the end of follow-up in SEER registry
Measured: 2000.01-2018.12
overall survival
The primary outcome was overall survival (OS), which was defined as the time interval from the pediatric glioma diagnosis until death or the end of follow-up in Chinese registry
Measured: 2010.01-2018.12
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jan 2024
- Study start20 Sep 2022
- Primary completion16 Aug 2023
- Study completion20 Dec 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for GliomaStudies of the same condition, open and under way.
- 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
- 9,532 participants (actual)
Sponsor and investigators
- Lead sponsor
- Tang-Du Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06199388
- Sponsor's protocol ID
- TDLL-202312-05
- Status last verified
- Dec 2023
- First submitted
- 27 Dec 2023
- Last updated
- 10 Jan 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The data involves the relevant personal privacy information of the patient
Publications
Thomas L, Li F, Pencina M. Using Propensity Score Methods to Create Target Populations in Observational Clinical Research. JAMA. 2020 Feb 4;323(5):466-467. doi: 10.1001/jama.2019.21558. No abstract available.
PubMed 31922529Doll KM, Rademaker A, Sosa JA. Practical Guide to Surgical Data Sets: Surveillance, Epidemiology, and End Results (SEER) Database. JAMA Surg. 2018 Jun 1;153(6):588-589. doi: 10.1001/jamasurg.2018.0501. No abstract available.
PubMed 29617544
Study at a glance
From the registry record, last updated by the sponsor on 10 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Deep Learning-Based Survival Prediction Model for Pediatric Glioma Patients: A Retrospective Study Using the SEER Database and Chinese Data
- ClinicalTrials.gov ID
- NCT06199388
- Acronym / sponsor ID
- TDLL-202312-05
- Conditions
- Glioma
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 9,532 participants (actual)
- Lead sponsor
- Tang-Du HospitalUniversity, hospital or other
- Study start
- 20 Sep 2022 (actual)
- Primary completion
- 16 Aug 2023 (actual)
- Study completion
- 20 Dec 2023 (actual)
- First posted
- 10 Jan 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.
Accurately predicting the survival of pediatric glioma patients is crucial for informed clinical decision-making and selecting appropriate treatment strategies. However, there is a lack of prognostic models specifically tailored for pediatric glioma patients. This study aimed to address this gap by developing a time-dependent deep learning model to aid physicians in making more accurate prognostic assessments and treatment decisions.
Detailed description
This retrospective study focuses on survival prediction in pediatric glioma patients using a population-based approach. The model was trained using the Surveillance, Epidemiology, and End Results (SEER) Registry database. To identify specific tumor types, the International Classification of Diseases for Oncology, 3rd Edition codes (ICD-O-3) were used, including codes 9450, 9394, 9421, 9384, 9383, 9424, 9400, 9420, 9410, 9411, 9380, 9382, 9391, 9393, 9390, 9401, 9381, 9451, 9440, 9441, 9442, 9430, and 9380, covering astrocytic tumors, oligodendroglia tumors, oligoastrocytic tumors, ependymal tumors, and other gliomas. Inclusion criteria comprised all primary brain tumors (C71.0-C71.9, C72.3, C72.8, C75.3) diagnosed between 2000 and 2018, among patients under 21 years old, and meeting the third edition of the ICD-O-3 classification. Only patients with available survival time were included, and those with unknown or missing clinical features were excluded. This cohort consisted of 258 pediatric glioma patients diagnosed at Tangdu Hospital in Xi\'an, China, between January 2010 and December 2018. These patients had complete clinical data and comprehensive follow-up records.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| SEER database | – | Other: Survival stateThe model was trained using the Surveillance, Epidemiology, and End Results (SEER) Registry database. To identify specific tumor types, the International Classification of Diseases for Oncology, 3rd Edition codes (ICD-O-3) were used, including codes 9450, 9394, 9421, 9384, 9383, 9424, 9400, 9420, 9410, 9411, 9380, 9382, 9391, 9393, 9390, 9401, 9381, 9451, 9440, 9441, 9442, 9430, and 9380, covering astrocytic tumors, oligodendroglia tumors, oligoastrocytic tumors, ependymal tumors, and other gliomas. Inclusion criteria comprised all primary brain tumors (C71.0-C71.9, C72.3, C72.8, C75.3) diagnosed between 2000 and 2018, among patients under 21 years old, and meeting the third edition of the ICD-O-3 classification. Only patients with available survival time were included, and those with unknown or missing clinical features were excluded. |
| Chinese cohort | – | Other: Survival stateTo assess the generalizability of the final model, an external validation cohort from China was used. This cohort consisted of 258 pediatric glioma patients diagnosed at Tangdu Hospital in Xi\'an, China, between January 2010 and December 2018. These patients had complete clinical data and comprehensive follow-up records. |
Who may qualify
- Ages
- Up to 21 years
- Sex
- All
- Age groups
- Child, Adult
- Study population
- the US Surveillance, Epidemiology, and End Results (SEER) between January 2000 and December 2018 and a Chinese registry (The Tangdu Hospital of the Fourth Military Medical Universitye) between January 2010 and December 2018
Key inclusion criteria (1 listed)
- To identify specific tumor types, the International Classification of Diseases for Oncology, 3rd Edition codes (ICD-O-3) were used, including codes 9450, 9394, 9421, 9384, 9383, 9424, 9400, 9420, 9410, 9411, 9380, 9382, 9391, 9393, 9390, 9401, 9381, 9451, 9440, 9441, 9442, 9430, and 9380, covering astrocytic tumors, oligodendroglia tumors, oligoastrocytic tumors, ependymal tumors, and other gliomas. Inclusion criteria comprised all primary brain tumors (C71.0-C71.9, C72.3, C72.8, C75.3) diagnosed, among patients under 21 years old, and meeting the third edition of the ICD-O-3 classification.
Key exclusion criteria (1 listed)
- Only patients with available survival time were included, and those with unknown or missing clinical features were excluded.
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 |
|---|---|---|
| Survival stateWe recorded clinically relevant information and survival status of pediatric glioma patients | Other | Chinese cohort, SEER database |
Primary outcome
overall survival
Time frame: 2000.01-2018.12
The primary outcome was overall survival (OS), which was defined as the time interval from the pediatric glioma diagnosis until death or the end of follow-up in SEER registry
overall survival
Time frame: 2010.01-2018.12
The primary outcome was overall survival (OS), which was defined as the time interval from the pediatric glioma diagnosis until death or the end of follow-up in Chinese registry
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 10 Jan 2024.
- Results publication: PubMed 31922529Thomas L, Li F, Pencina M. Using Propensity Score Methods to Create Target Populations in Observational Clinical Research. JAMA. 2020 Feb 4;323(5):466-467. doi: 10.1001/jama.2019.21558. No abstract available.
- Results publication: PubMed 29617544Doll KM, Rademaker A, Sosa JA. Practical Guide to Surgical Data Sets: Surveillance, Epidemiology, and End Results (SEER) Database. JAMA Surg. 2018 Jun 1;153(6):588-589. doi: 10.1001/jamasurg.2018.0501. No abstract available.
- Individual participant dataSharing planned: No. The data involves the relevant personal privacy information of the patient
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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 Glioma, run by Tang-Du Hospital 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 1 inclusion and 1 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 10 Jan 2024. 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?
overall survival (time frame: 2000.01-2018.12). 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 NCT06199388, which also lists 2 linked publications.
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 10 Jan 2024.