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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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| 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Survival state | Other | We recorded clinically relevant information and survival status of pediatric glioma patients |
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| Measure | Description | Time Frame |
|---|---|---|
| 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 | 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 | 2010.01-2018.12 |
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Inclusion Criteria:
Exclusion Criteria:
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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
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Tangdu Hospital | Xi'an | Shannxi | 710000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 31922529 | Result | 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. | |
| 29617544 | Result | Doll 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. |
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The data involves the relevant personal privacy information of the patient
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| ID | Term |
|---|---|
| D005910 | Glioma |
| ID | Term |
|---|---|
| D018302 | Neoplasms, Neuroepithelial |
| D017599 | Neuroectodermal Tumors |
| D009373 | Neoplasms, Germ Cell and Embryonal |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009380 | Neoplasms, Nerve Tissue |
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